title
Machine Learning in Hindi | Machine Learning Tutorial in Hindi | Python for Machine Learning | 2020

description
🔥1000+ Free Courses With Free Certificates: https://www.mygreatlearning.com/academy?ambassador_code=GLYT_DES_OuPMVdski3E&utm_source=GLYT&utm_campaign=GLYT_DES_OuPMVdski3E 🔥Build a successful career in Artificial Intelligence and Machine Learning https://www.mygreatlearning.com/pg-program-artificial-intelligence-course?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP54 Learn Artificial Intelligence from leading experts and attain a Dual Certificate on AI and Machine Learning from world-renowned universities. Take the step towards your professional growth by obtaining expertise in the real-world application of the latest technological tools of AI. Over 500+ Hiring Partners & 8000+ career transitions over varied domains. Know More: https://glacad.me/3icS907 ****************************************************************************************** 📚 Download the Revision Material for "Machine Learning Full Course" Here: https://glacad.me/3kCg1xe ****************************************************************************************** This 'Machine Learning in Hindi' video will help you to master all the machine learning concepts and you'll also learn how to use Python for Machine Learning. There are very few resources or no resources at all on machine learning in Hindi, so that is why we have come up with this Machine Learning Full Course in Hindi. Get the certificate of completion for the course. Also access to the datasets, projects, assessments and codes, register on Great Learning Academy : https://glacad.me/3dwXXAK 🏁 Topics Covered: *Introduction- 00:00:00 *Installing Pandas- 00:001:36 *Python Basics and Data Structures- 00:09:53 *Libraries in Python- 01:58:13 *NumPy- 01:59:42 *Pandas- 02:28:15 *Matplotlib- 02:59:11 *ML basics- 03:34:04 *Linear Regression- 03:43:04 *Logistic Regression- 03:59:47 *Decision Tree- 04:11:11 🔥Check Our Free Courses with free certificate: 📌 Machine Learning with Python course: https://glacad.me/3m7vxkl 📌 Machine Learning with Python: https://www.mygreatlearning.com/academy/learn-for-free/courses/machine-learning-with-python?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP35 📌 Machine Learning with AWS: https://www.mygreatlearning.com/academy/learn-for-free/courses/machine-learning-with-aws?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP36 📌 Machine Learning Algorithms: https://www.mygreatlearning.com/academy/learn-for-free/courses/machine-learning-algorithms?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP37 📌 Basics of Machine Learning: https://www.mygreatlearning.com/academy/learn-for-free/courses/basics-of-machine-learning-1?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP38 Here is a list of our other full course videos, you can check out: ✔ Data Science Tutorial: https://www.youtube.com/watch?v=u2zsY-2uZiE&t=680s ✔ Python for Data Science: https://www.youtube.com/watch?v=edvg4eHi_Mw&t=15700s ✔ Machine Learning with Python: https://www.youtube.com/watch?v=RnFGwxJwx-0&t=8732s ✔ Statistics for Data Science: https://www.youtube.com/watch?v=Vfo5le26IhY&t=189s ✔ Tableau Training for Beginners: https://www.youtube.com/watch?v=6mBtTNggkUk&t=1735s ✔ Reinforcement Learning Tutorial: https://www.youtube.com/watch?v=f8bnkro3yXY&t=9940s ⚡ About Great Learning Academy: Visit Great Learning Academy to get access to 1000+ free courses with free certificate on Data Science, Data Analytics, Digital Marketing, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity, Software Development, and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. ⚡ About Great Learning: With more than 5.4 Million+ learners in 170+ countries, Great Learning, a part of the BYJU'S group, is a leading global edtech company for professional and higher education offering industry-relevant programs in the blended, classroom, and purely online modes across technology, data and business domains. These programs are developed in collaboration with the top institutions like Stanford Executive Education, MIT Professional Education, The University of Texas at Austin, NUS, IIT Madras, IIT Bombay & more. SOCIAL MEDIA LINKS: 🔹 For more interesting tutorials, don't forget to subscribe to our channel: https://glacad.me/YTsubscribe 🔹 For more updates on courses and tips follow us on: ✅ Telegram: https://t.me/GreatLearningAcademy ✅ Facebook: https://www.facebook.com/GreatLearningOfficial/ ✅ LinkedIn: https://www.linkedin.com/school/great-learning/mycompany/verification/ ✅ Follow our Blog: https://glacad.me/GL_Blog

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{'title': 'Machine Learning in Hindi | Machine Learning Tutorial in Hindi | Python for Machine Learning | 2020', 'heatmap': [{'end': 10399.821, 'start': 9739.165, 'weight': 1}, {'end': 11177.946, 'start': 11017.06, 'weight': 0.701}, {'end': 11501.423, 'start': 11335.559, 'weight': 0.756}, {'end': 12144.078, 'start': 11979.372, 'weight': 0.755}, {'end': 13251.979, 'start': 12928.047, 'weight': 0.77}, {'end': 14064.483, 'start': 13884.651, 'weight': 0.826}], 'summary': 'This hindi tutorial covers a comprehensive course on python for machine learning, including installation, python fundamentals, data structures, decision-making, functions, libraries like numpy, pandas, matplotlib, array operations, data manipulation, visualization techniques, and machine learning concepts with practical examples and quantifiable outcomes.', 'chapters': [{'end': 550.699, 'segs': [{'end': 85.959, 'src': 'embed', 'start': 7.709, 'weight': 0, 'content': [{'end': 9.229, 'text': 'Hey guys, welcome to this session.', 'start': 7.709, 'duration': 1.52}, {'end': 15.172, 'text': 'You might have only heard of Python and Machine Learning.', 'start': 9.269, 'duration': 5.903}, {'end': 22.135, 'text': 'You might not understand how to learn Machine Learning and how to implement Machine Learning with Python.', 'start': 15.252, 'duration': 6.883}, {'end': 31.175, 'text': 'Your problem will be that there are many resources A high quality tutorial is not in Hindi.', 'start': 22.155, 'duration': 9.02}, {'end': 38.961, 'text': 'So keeping that in mind, we are making this Machine Learning with Python full course in Hindi.', 'start': 31.455, 'duration': 7.506}, {'end': 43.104, 'text': "So we will not waste time, let's see the agenda directly.", 'start': 39.782, 'duration': 3.322}, {'end': 48.188, 'text': 'First of all, we will see how to install Python and install the different Python IDs.', 'start': 43.565, 'duration': 4.623}, {'end': 54.692, 'text': 'After that, we will learn Python basics and work with different data structures.', 'start': 50.708, 'duration': 3.984}, {'end': 64.881, 'text': 'After learning Python basics, we will learn Python libraries, which are NumPy, Pandas, and Matplotlib.', 'start': 54.752, 'duration': 10.129}, {'end': 70.846, 'text': 'After covering all this in Python, we will come to machine learning concepts.', 'start': 64.9, 'duration': 5.946}, {'end': 76.491, 'text': 'First of all, we will understand what is machine learning through many examples.', 'start': 71.787, 'duration': 4.704}, {'end': 85.959, 'text': 'After that, we will learn three very important machine learning algorithms, which are linear regression, logistic regression and decision tree.', 'start': 76.631, 'duration': 9.328}], 'summary': 'Machine learning with python full course in hindi includes python installation, basics, libraries, and key ml algorithms.', 'duration': 78.25, 'max_score': 7.709, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7709.jpg'}, {'end': 144.647, 'src': 'embed', 'start': 115.389, 'weight': 3, 'content': [{'end': 120.753, 'text': "And whatever system you have, whether it's Windows, Linux or Mac, the Python version is available.", 'start': 115.389, 'duration': 5.364}, {'end': 124.836, 'text': 'And because I have a Windows system, I will download Python for Windows.', 'start': 120.813, 'duration': 4.023}, {'end': 127.458, 'text': 'So just download Python 3.8.2 and it will automatically download Python.', 'start': 124.876, 'duration': 2.582}, {'end': 134.643, 'text': 'So after installing Python, we need an IDE.', 'start': 132.082, 'duration': 2.561}, {'end': 139.205, 'text': 'What is an IDE? IDE is basically an integrated development environment.', 'start': 134.843, 'duration': 4.362}, {'end': 144.647, 'text': 'Like if you have worked with Java, then you must have implemented Java code in Eclipse.', 'start': 139.225, 'duration': 5.422}], 'summary': 'Python 3.8.2 is available for windows, linux, and mac, and an ide is needed for development.', 'duration': 29.258, 'max_score': 115.389, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E115389.jpg'}, {'end': 388.234, 'src': 'embed', 'start': 359.418, 'weight': 4, 'content': [{'end': 367.361, 'text': 'And if we want to launch Jupyter Notebook, just click on Launch and it will automatically launch Jupyter Notebook.', 'start': 359.418, 'duration': 7.943}, {'end': 372.283, 'text': 'And as I told you, you will run all your coding on Jupyter Notebook.', 'start': 367.501, 'duration': 4.782}, {'end': 380.888, 'text': 'And similarly, if you want to use Prompt, then again, Here select Anaconda Prompt and this is your base environment.', 'start': 372.363, 'duration': 8.525}, {'end': 388.234, 'text': 'So here if you want to launch like this Jupiter space then you have to type notebook.', 'start': 381.288, 'duration': 6.946}], 'summary': 'Launch jupyter notebook with one click, run coding, and access anaconda prompt for base environment.', 'duration': 28.816, 'max_score': 359.418, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E359418.jpg'}], 'start': 7.709, 'title': 'Python and machine learning in hindi', 'summary': 'Covers a full course in hindi on machine learning with python, including python installation, basics, libraries like numpy, pandas, and matplotlib, and essential machine learning algorithms such as linear regression, logistic regression, and decision tree. it also includes an overview of python installation, pycharm, anaconda, and jupyter notebook.', 'chapters': [{'end': 85.959, 'start': 7.709, 'title': 'Machine learning with python in hindi', 'summary': 'Introduces a full course in hindi on machine learning with python, covering the installation of python, python basics, libraries like numpy, pandas, and matplotlib, and essential machine learning algorithms such as linear regression, logistic regression, and decision tree.', 'duration': 78.25, 'highlights': ['The course is designed to address the lack of high-quality tutorials in Hindi, providing comprehensive learning and implementation of Machine Learning with Python.', 'The agenda covers the installation of Python and different Python IDs, followed by learning Python basics, working with different data structures, and understanding Python libraries like NumPy, Pandas, and Matplotlib.', 'The chapter also focuses on machine learning concepts, including the understanding of machine learning through examples, and learning essential machine learning algorithms such as linear regression, logistic regression, and decision tree.']}, {'end': 550.699, 'start': 86.539, 'title': 'Python installation and ides overview', 'summary': 'Covers the installation process of python, pycharm, and anaconda, including downloading python 3.8.2, pycharm community version, and anaconda for python 3.7, along with a detailed demonstration of using jupyter notebook and its features.', 'duration': 464.16, 'highlights': ['The chapter covers the installation process of Python, PyCharm, and Anaconda, including downloading Python 3.8.2, PyCharm community version, and Anaconda for Python 3.7. The transcript details the process of downloading Python 3.8.2, PyCharm community version, and Anaconda for Python 3.7, providing a comprehensive overview of the installation process for these essential tools.', 'The chapter includes a detailed demonstration of using Jupyter Notebook and its features, such as launching a new notebook, file operations, cell manipulation, and kernel management. The transcript provides a comprehensive demonstration of using Jupyter Notebook, covering various features like launching a new notebook, file operations, cell manipulation, and kernel management, offering a practical understanding of its functionalities.']}], 'duration': 542.99, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7709.jpg', 'highlights': ['The course is designed to address the lack of high-quality tutorials in Hindi, providing comprehensive learning and implementation of Machine Learning with Python.', 'The agenda covers the installation of Python and different Python IDs, followed by learning Python basics, working with different data structures, and understanding Python libraries like NumPy, Pandas, and Matplotlib.', 'The chapter also focuses on machine learning concepts, including the understanding of machine learning through examples, and learning essential machine learning algorithms such as linear regression, logistic regression, and decision tree.', 'The chapter covers the installation process of Python, PyCharm, and Anaconda, including downloading Python 3.8.2, PyCharm community version, and Anaconda for Python 3.7, providing a comprehensive overview of the installation process for these essential tools.', 'The chapter includes a detailed demonstration of using Jupyter Notebook and its features, such as launching a new notebook, file operations, cell manipulation, and kernel management, offering a practical understanding of its functionalities.']}, {'end': 1504.761, 'segs': [{'end': 598.601, 'src': 'embed', 'start': 551.179, 'weight': 0, 'content': [{'end': 555.002, 'text': 'So here you can go and explore which shortcuts are available.', 'start': 551.179, 'duration': 3.823}, {'end': 558.045, 'text': 'So, we have seen what the Jupyter Notebook actually is.', 'start': 555.663, 'duration': 2.382}, {'end': 561.409, 'text': 'So, now we will write our first Python program.', 'start': 558.065, 'duration': 3.344}, {'end': 563.691, 'text': 'So, I just have to run a simple line.', 'start': 561.429, 'duration': 2.262}, {'end': 565.473, 'text': 'For that, I will use print statement.', 'start': 563.731, 'duration': 1.742}, {'end': 566.394, 'text': 'I will write print.', 'start': 565.553, 'duration': 0.841}, {'end': 569.757, 'text': 'I will use two double quotes in it.', 'start': 566.474, 'duration': 3.283}, {'end': 575.203, 'text': 'And under double quotes, I will write this is Sparta.', 'start': 569.797, 'duration': 5.406}, {'end': 580.27, 'text': 'I am clicking run and we have successfully printed this line here.', 'start': 576.448, 'duration': 3.822}, {'end': 581.011, 'text': 'This is Python.', 'start': 580.29, 'duration': 0.721}, {'end': 584.933, 'text': 'So you have written your first Python program.', 'start': 581.451, 'duration': 3.482}, {'end': 593.958, 'text': 'So now we have to go ahead and explore a lot, but you can be happy because you have taken the first step to bring expertise in Python.', 'start': 585.113, 'duration': 8.845}, {'end': 596.319, 'text': 'So now we have run the first program in Python.', 'start': 594.018, 'duration': 2.301}, {'end': 598.601, 'text': "So now let's know some basics of Python.", 'start': 596.6, 'duration': 2.001}], 'summary': "Introduction to jupyter notebook, running first python program, printing 'this is sparta', and moving on to learn basics of python.", 'duration': 47.422, 'max_score': 551.179, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E551179.jpg'}, {'end': 647.791, 'src': 'embed', 'start': 620.226, 'weight': 2, 'content': [{'end': 627.433, 'text': 'So here as you can see, suppose you have three students, John, Sam and Matt.', 'start': 620.226, 'duration': 7.207}, {'end': 631.477, 'text': 'And you have to save the names of these three students somewhere.', 'start': 627.793, 'duration': 3.684}, {'end': 633.779, 'text': 'So here we have a variable.', 'start': 631.517, 'duration': 2.262}, {'end': 639.724, 'text': 'So, as it is written here, a variable is a temporary storage space.', 'start': 636.041, 'duration': 3.683}, {'end': 642.767, 'text': 'So, you can understand this folder as a variable.', 'start': 640.004, 'duration': 2.763}, {'end': 647.791, 'text': 'And in this storage space, I am saving this value.', 'start': 642.847, 'duration': 4.944}], 'summary': 'Three students, john, sam, and matt, are saved in a variable as a temporary storage space.', 'duration': 27.565, 'max_score': 620.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E620226.jpg'}, {'end': 840.661, 'src': 'embed', 'start': 806.717, 'weight': 3, 'content': [{'end': 809.26, 'text': 'So, we have different types of data available.', 'start': 806.717, 'duration': 2.543}, {'end': 817.086, 'text': 'So we have, especially in Python, integer data type, floating type data type, boolean data type and string data type.', 'start': 809.901, 'duration': 7.185}, {'end': 820.428, 'text': 'And apart from this, there is another data type which is complex.', 'start': 817.106, 'duration': 3.322}, {'end': 822.149, 'text': "So let's understand what all these are.", 'start': 820.448, 'duration': 1.701}, {'end': 825.731, 'text': 'Integers are like normal numbers, 10, 500, 3, 14, 1000, all these integers are called int data type.', 'start': 822.529, 'duration': 3.202}, {'end': 831.775, 'text': 'After that, floating type, 3.14, 15.97, all these dots, where the decimal operator comes, all these are our floating point numbers.', 'start': 825.751, 'duration': 6.024}, {'end': 840.661, 'text': 'After that, Boolean values.', 'start': 839.56, 'duration': 1.101}], 'summary': 'Python has integer, floating, boolean, and string data types, with examples like 10, 3.14, and true/false values.', 'duration': 33.944, 'max_score': 806.717, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E806717.jpg'}, {'end': 1067.686, 'src': 'embed', 'start': 1039.733, 'weight': 4, 'content': [{'end': 1042.538, 'text': 'So I printed A1, now I will check its type.', 'start': 1039.733, 'duration': 2.805}, {'end': 1043.98, 'text': 'Type of A1.', 'start': 1042.758, 'duration': 1.222}, {'end': 1046.584, 'text': 'So this gives us complex.', 'start': 1045.021, 'duration': 1.563}, {'end': 1049.969, 'text': 'So all these are different types of data in Python.', 'start': 1047.045, 'duration': 2.924}, {'end': 1053.32, 'text': "So, let's see what operators are.", 'start': 1051.459, 'duration': 1.861}, {'end': 1060.123, 'text': 'So, as the name says, you can apply simple operations with operators on different data.', 'start': 1053.56, 'duration': 6.563}, {'end': 1064.845, 'text': 'So, we have relational operators, arithmetic operators and logical operators.', 'start': 1060.503, 'duration': 4.342}, {'end': 1067.686, 'text': 'So, we will implement these three in the Jupyter Notebook.', 'start': 1064.885, 'duration': 2.801}], 'summary': 'Printed a1, checked its type, and explored data types and operators in python.', 'duration': 27.953, 'max_score': 1039.733, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E1039733.jpg'}, {'end': 1231.458, 'src': 'embed', 'start': 1170.897, 'weight': 5, 'content': [{'end': 1174.378, 'text': 'After that, I will give the second variable.', 'start': 1170.897, 'duration': 3.481}, {'end': 1176.098, 'text': 'I will store 20 in the second variable.', 'start': 1174.418, 'duration': 1.68}, {'end': 1183.705, 'text': 'So, I will print A and B by writing A, B.', 'start': 1179.943, 'duration': 3.762}, {'end': 1186.606, 'text': 'As you can see, I have stored 10 in A and 20 in B.', 'start': 1183.705, 'duration': 2.901}, {'end': 1190.348, 'text': "So, after this, let's see the basic operations.", 'start': 1186.606, 'duration': 3.742}, {'end': 1194.99, 'text': "If I want to see A plus B, then let's see what the result is.", 'start': 1190.448, 'duration': 4.542}, {'end': 1200.272, 'text': 'The result is 30 here because 10 was stored in A and 20 was stored in B.', 'start': 1195.17, 'duration': 5.102}, {'end': 1202.733, 'text': 'And what happened to 10 plus 20? 30.', 'start': 1200.272, 'duration': 2.461}, {'end': 1205.314, 'text': 'So, we are getting the same result here.', 'start': 1202.733, 'duration': 2.581}, {'end': 1210.98, 'text': 'Similarly, if I do A-B, 10 is stored in A and 20 is stored in B.', 'start': 1206.795, 'duration': 4.185}, {'end': 1213.242, 'text': 'So, 10-20 will give us minus 10.', 'start': 1210.98, 'duration': 2.262}, {'end': 1215.224, 'text': "Similarly, let's do AxB.", 'start': 1213.242, 'duration': 1.982}, {'end': 1218.368, 'text': 'So, for x, we will use the asterisk operator.', 'start': 1215.244, 'duration': 3.124}, {'end': 1228.976, 'text': 'So, A into B is 200 because 10 is stored in A and 20 is stored in B and 10 into 20 is 200.', 'start': 1222.492, 'duration': 6.484}, {'end': 1231.458, 'text': 'So, this is the multiplication.', 'start': 1228.976, 'duration': 2.482}], 'summary': 'Demonstration of basic arithmetic operations using variables a and b, resulting in a+b=30, a-b=-10, axb=200.', 'duration': 60.561, 'max_score': 1170.897, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E1170897.jpg'}], 'start': 551.179, 'title': 'Python programming fundamentals', 'summary': 'Introduces python programming through the jupyter notebook, covers variables, data types, and operators, and emphasizes taking the first step towards expertise in python. it demonstrates storing data in variables, different data types, arithmetic, relational, and logical operators, and provides examples for better understanding.', 'chapters': [{'end': 598.601, 'start': 551.179, 'title': 'Introduction to python programming', 'summary': "Introduces the jupyter notebook, covers running the first python program using a print statement to display 'this is sparta', and emphasizes on taking the first step towards expertise in python.", 'duration': 47.422, 'highlights': ["Running the first Python program using a print statement to display 'This is Sparta'.", 'Introduction to the Jupyter Notebook for exploring available shortcuts.', 'Emphasizing on taking the first step towards expertise in Python.']}, {'end': 1146.684, 'start': 599.161, 'title': 'Understanding variables, data types, and operators in python', 'summary': 'Covers the concept of variables in programming, illustrating the process of storing data in variables and demonstrating the different data types (integer, floating point, boolean, string, complex) in python, along with the implementation of arithmetic, relational, and logical operators.', 'duration': 547.523, 'highlights': ['Variables are used for storing data in programming, with examples of storing student names (John, Sam, Matt) in a variable, and the concept of temporary storage space is explained. The transcript explains the concept of variables, demonstrating the process of storing student names (John, Sam, Matt) in a variable and highlighting the variable as temporary storage space.', 'Data types in Python include integers, floating point numbers, boolean values, strings, and complex numbers, with practical examples of creating variables for each data type and checking their types. The different data types in Python, including integers, floating point numbers, boolean values, strings, and complex numbers, are explained with practical examples of creating variables for each data type and checking their types.', 'The concept of comments in Python is demonstrated, and the implementation of arithmetic, relational, and logical operators is explained, with a focus on the use of comments for providing additional information. The demonstration of comments in Python and the explanation of arithmetic, relational, and logical operators, emphasizing the use of comments for providing additional information.']}, {'end': 1504.761, 'start': 1146.804, 'title': 'Arithmetic, relational, and logical operators in python', 'summary': 'Covers arithmetic, relational, and logical operators in python, including examples such as addition, subtraction, multiplication, division, comparison of values using greater than, less than, equal to, and not equal to operators, and logical and and or operations with true and false values.', 'duration': 357.957, 'highlights': ['The chapter covers arithmetic, relational, and logical operators in Python, including examples such as addition, subtraction, multiplication, division, comparison of values using greater than, less than, equal to, and not equal to operators, and logical AND and OR operations with true and false values. This highlight provides an overview of the entire transcript, summarizing the key points about arithmetic, relational, and logical operators, along with specific examples and the use of true and false values.', 'A into B is 200 because 10 is stored in A and 20 is stored in B and 10 into 20 is 200. This highlight explains the multiplication operation, demonstrating the result of multiplying 10 with 20 and storing the result (200) in the variable.', 'The result is 30 here because 10 was stored in A and 20 was stored in B. This highlight provides an example of addition, showing the result of adding the values stored in variables A and B, which is 30.']}], 'duration': 953.582, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E551179.jpg', 'highlights': ['Introduction to the Jupyter Notebook for exploring available shortcuts.', 'Emphasizing on taking the first step towards expertise in Python.', 'Variables are used for storing data in programming, with examples of storing student names (John, Sam, Matt) in a variable, and the concept of temporary storage space is explained.', 'Data types in Python include integers, floating point numbers, boolean values, strings, and complex numbers, with practical examples of creating variables for each data type and checking their types.', 'The chapter covers arithmetic, relational, and logical operators in Python, including examples such as addition, subtraction, multiplication, division, comparison of values using greater than, less than, equal to, and not equal to operators, and logical AND and OR operations with true and false values.', 'A into B is 200 because 10 is stored in A and 20 is stored in B and 10 into 20 is 200.', 'The result is 30 here because 10 was stored in A and 20 was stored in B.']}, {'end': 2677.181, 'segs': [{'end': 1614.618, 'src': 'embed', 'start': 1581.061, 'weight': 0, 'content': [{'end': 1586.807, 'text': "And you can't use these things for variable name, function name or class name.", 'start': 1581.061, 'duration': 5.746}, {'end': 1592.454, 'text': "So, let's see some examples from these Python keywords.", 'start': 1587.008, 'duration': 5.446}, {'end': 1597.054, 'text': 'So, as I am typing true here.', 'start': 1594.653, 'duration': 2.401}, {'end': 1601.114, 'text': 'So, as you can see, as I typed true, it converted to green.', 'start': 1597.354, 'duration': 3.76}, {'end': 1606.656, 'text': 'Similarly, if I type def, it converted to green again.', 'start': 1601.695, 'duration': 4.961}, {'end': 1610.537, 'text': 'If I type class, it converted to green again.', 'start': 1607.196, 'duration': 3.341}, {'end': 1614.618, 'text': 'So, these are all our keywords which have some special importance.', 'start': 1610.557, 'duration': 4.061}], 'summary': "Python keywords like 'true', 'def', and 'class' have special importance.", 'duration': 33.557, 'max_score': 1581.061, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E1581061.jpg'}, {'end': 1670.83, 'src': 'embed', 'start': 1645.287, 'weight': 1, 'content': [{'end': 1650.151, 'text': 'So, identifiers are basically names used for variables, functions, and objects.', 'start': 1645.287, 'duration': 4.864}, {'end': 1656.215, 'text': 'So, whatever the name of the variable, the name of the function, or the name of the object, they are all called Python identifiers.', 'start': 1650.171, 'duration': 6.044}, {'end': 1658.196, 'text': 'And these are some special rules.', 'start': 1656.335, 'duration': 1.861}, {'end': 1658.396, 'text': 'So, py..', 'start': 1658.216, 'duration': 0.18}, {'end': 1664.428, 'text': 'So, the name of a variable can never start with an underscore.', 'start': 1660.267, 'duration': 4.161}, {'end': 1667.569, 'text': 'And identifiers are actually case sensitive.', 'start': 1665.088, 'duration': 2.481}, {'end': 1670.83, 'text': 'So, suppose you created a variable student.', 'start': 1667.889, 'duration': 2.941}], 'summary': 'Python identifiers are names for variables, functions, and objects, subject to specific rules.', 'duration': 25.543, 'max_score': 1645.287, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E1645287.jpg'}, {'end': 1818.875, 'src': 'embed', 'start': 1777.3, 'weight': 2, 'content': [{'end': 1782.644, 'text': 'and in the same way, if I, I write a floating point, number 3.14..', 'start': 1777.3, 'duration': 5.344}, {'end': 1788.888, 'text': 'Again this floating point number is actually a literal whose value cannot be changed.', 'start': 1782.644, 'duration': 6.244}, {'end': 1792.37, 'text': 'So literals means basically the data whose value does not change.', 'start': 1789.268, 'duration': 3.102}, {'end': 1795.131, 'text': 'So this is the difference between variables and literals.', 'start': 1792.43, 'duration': 2.701}, {'end': 1797.554, 'text': 'So, we have covered the basic things.', 'start': 1796.493, 'duration': 1.061}, {'end': 1799.998, 'text': 'Now, we will work with Python strings.', 'start': 1797.594, 'duration': 2.404}, {'end': 1805.044, 'text': 'So, I have already told you that strings are sequence of characters.', 'start': 1800.178, 'duration': 4.866}, {'end': 1807.508, 'text': 'So, what is all this? Sequence of characters.', 'start': 1805.064, 'duration': 2.444}, {'end': 1811.032, 'text': 'Means, after one character, another character is continuously coming here.', 'start': 1807.528, 'duration': 3.504}, {'end': 1815.614, 'text': 'And all these characters are stored in single quotes, double quotes or triple quotes.', 'start': 1811.573, 'duration': 4.041}, {'end': 1818.875, 'text': 'So here is a string Hello World which is stored in single quotes.', 'start': 1815.634, 'duration': 3.241}], 'summary': 'Variables vs. literals, working with python strings, sequence of characters, stored in single, double, or triple quotes.', 'duration': 41.575, 'max_score': 1777.3, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E1777300.jpg'}, {'end': 2093.563, 'src': 'embed', 'start': 2063.473, 'weight': 4, 'content': [{'end': 2070.076, 'text': 'so the index value of i is 5, so I will write 5, after that I will use colon.', 'start': 2063.473, 'duration': 6.603}, {'end': 2074.976, 'text': "then let's see how far it goes 6,, 7, 8, 9, 10..", 'start': 2070.076, 'duration': 4.9}, {'end': 2076.918, 'text': 'So, A is on 10.', 'start': 2074.978, 'duration': 1.94}, {'end': 2080.54, 'text': 'So, if I want A also, then I have to write 11.', 'start': 2076.918, 'duration': 3.622}, {'end': 2085.701, 'text': 'Because the last value of Python is not inclusive.', 'start': 2080.54, 'duration': 5.161}, {'end': 2089.101, 'text': 'So, if I write 10 here, then I will get P.', 'start': 2085.88, 'duration': 3.221}, {'end': 2091.822, 'text': 'If I want A also, then I have to write 11.', 'start': 2089.101, 'duration': 2.721}, {'end': 2093.563, 'text': 'I will print this and show you.', 'start': 2091.822, 'duration': 1.741}], 'summary': 'Using index values and colon in python to access elements, with an example of a at index 10.', 'duration': 30.09, 'max_score': 2063.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E2063473.jpg'}, {'end': 2173.613, 'src': 'embed', 'start': 2147.325, 'weight': 6, 'content': [{'end': 2152.046, 'text': 'and suppose I have to convert the string to lower case, then I will use dot lower method.', 'start': 2147.325, 'duration': 4.721}, {'end': 2159.769, 'text': 'So first I am giving the name of the string, after that I will use dot lower method, then it will give me the result in full lower case.', 'start': 2152.066, 'duration': 7.703}, {'end': 2166.731, 'text': 'Similarly, if I have to convert the whole string to upper case, first I will give the name of the string, then dot operator,', 'start': 2159.789, 'duration': 6.942}, {'end': 2167.791, 'text': 'then I will use upper method.', 'start': 2166.731, 'duration': 1.06}, {'end': 2170.392, 'text': 'it will convert the whole string to upper case.', 'start': 2167.791, 'duration': 2.601}, {'end': 2173.613, 'text': "Again, let's try all these examples in Jupyter Notebook.", 'start': 2170.452, 'duration': 3.161}], 'summary': 'Demonstrates converting strings to lower and upper case in python.', 'duration': 26.288, 'max_score': 2147.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E2147325.jpg'}, {'end': 2220.753, 'src': 'embed', 'start': 2189.137, 'weight': 5, 'content': [{'end': 2200.581, 'text': 'If I want to know the length, I will use len and inside this, I will type my string and it is telling me that the length of this whole string is 14.', 'start': 2189.137, 'duration': 11.444}, {'end': 2202.182, 'text': "So, let's just check.", 'start': 2200.581, 'duration': 1.601}, {'end': 2203.378, 'text': '1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14.', 'start': 2202.202, 'duration': 1.176}, {'end': 2213.548, 'text': 'Right? So, there are 14 characters including the spaces in the string.', 'start': 2203.382, 'duration': 10.166}, {'end': 2218.491, 'text': 'After this, we will see how we can convert it to lower case.', 'start': 2213.568, 'duration': 4.923}, {'end': 2220.753, 'text': 'So, here you can see T is capital.', 'start': 2218.511, 'duration': 2.242}], 'summary': "The string has 14 characters including spaces. the 't' is capitalized.", 'duration': 31.616, 'max_score': 2189.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E2189137.jpg'}, {'end': 2333.555, 'src': 'embed', 'start': 2302.843, 'weight': 7, 'content': [{'end': 2306.344, 'text': 'So here initially it was y, I replaced it with a.', 'start': 2302.843, 'duration': 3.501}, {'end': 2309.826, 'text': 'So my name is John, it changed and became my name is John.', 'start': 2306.344, 'duration': 3.482}, {'end': 2313.447, 'text': 'After that, we have another function named count.', 'start': 2310.726, 'duration': 2.721}, {'end': 2318.949, 'text': 'So, if we want to know how many times a substring is being repeated.', 'start': 2313.467, 'duration': 5.482}, {'end': 2322.731, 'text': 'So, here we have a string, hello hello world.', 'start': 2319.409, 'duration': 3.322}, {'end': 2327.472, 'text': 'So, if I want to know how many times hello is being repeated, then I can use the count method.', 'start': 2323.271, 'duration': 4.201}, {'end': 2333.555, 'text': 'And in the count method, I am taking only one parameter and that one parameter is hello.', 'start': 2327.673, 'duration': 5.882}], 'summary': "Replaced y with a, counted 'hello' in 'hello hello world'.", 'duration': 30.712, 'max_score': 2302.843, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E2302843.jpg'}, {'end': 2546.656, 'src': 'embed', 'start': 2515.845, 'weight': 8, 'content': [{'end': 2518.245, 'text': "And we don't need to give the full word.", 'start': 2515.845, 'duration': 2.4}, {'end': 2523.646, 'text': 'So if we want the index of the first character of any character or any substring, then we can use the find method.', 'start': 2518.265, 'duration': 5.381}, {'end': 2531.829, 'text': 'After that, suppose you have a string which is separated by a comma.', 'start': 2526.767, 'duration': 5.062}, {'end': 2534.97, 'text': 'And we have to divide that string into three parts.', 'start': 2532.049, 'duration': 2.921}, {'end': 2537.671, 'text': 'So, we can use the split method.', 'start': 2535.09, 'duration': 2.581}, {'end': 2546.656, 'text': 'So, as you can see here, after comma, So, as you can see here, wherever there is a comma, I have to show that thing as a separate entity.', 'start': 2537.911, 'duration': 8.745}], 'summary': 'Using find and split methods to extract and divide string data.', 'duration': 30.811, 'max_score': 2515.845, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E2515845.jpg'}], 'start': 1505.161, 'title': 'Python tokens, basics, strings, and functions', 'summary': 'Covers python tokens, operators, keywords, identifiers, and literals, detailing their functionalities and restrictions. it also introduces strings in python, explains indexing, and discusses various string functions, such as length finding, case conversion, character replacement, substring counting, index finding, and string splitting.', 'chapters': [{'end': 1799.998, 'start': 1505.161, 'title': 'Python tokens and basics', 'summary': 'Covers python tokens, including operators, keywords, identifiers, and literals, detailing their functionalities and restrictions, emphasizing the importance of python keywords and the distinction between identifiers and literals.', 'duration': 294.837, 'highlights': ["Python keywords are special reserved words with specific importance, and using them for variable or function names results in errors, such as 'true' or 'def'. Python keywords like 'true' and 'def' have special importance and attempting to use them as variable names results in errors.", 'Identifiers in Python are names for variables, functions, and objects, following specific rules such as case sensitivity and the prohibition of starting with an underscore or a digit. Python identifiers are names for variables, functions, and objects, and must adhere to specific rules like case sensitivity and not starting with an underscore or a digit.', "Python literals are constants with unchangeable values, such as strings like 'hello world' and floating point numbers like 3.14. Python literals, such as 'hello world' and 3.14, have unchangeable values and are different from variables."]}, {'end': 2123.837, 'start': 1800.178, 'title': 'Python strings and indexing', 'summary': 'Introduces the concept of strings in python, explaining the usage of single, double, and triple quotes for storing strings, and demonstrates the extraction of individual characters using indexing, showcasing examples of extracting first, last, and a sequence of characters from a string.', 'duration': 323.659, 'highlights': ['The chapter introduces the concept of strings in Python, explaining the usage of single, double, and triple quotes for storing strings. The transcript provides examples of strings stored in single quotes, double quotes, and triple quotes, demonstrating the use of each for different scenarios.', 'The chapter demonstrates the extraction of individual characters using indexing, showcasing examples of extracting first, last, and a sequence of characters from a string. The transcript explains the concept of indexing in Python, including the use of index values and the colon to extract specific characters or sequences of characters from a string.']}, {'end': 2677.181, 'start': 2125.691, 'title': 'String functions in python', 'summary': 'Discusses various string functions in python, including finding the length of a string, converting to lowercase and uppercase, replacing characters, counting substrings, finding index value of characters, and splitting strings based on a given criteria.', 'duration': 551.49, 'highlights': ['The chapter demonstrates finding the length of a string using the len function, which revealed that the string contains 14 characters including spaces. The len function is used to find the length of the string, showing that the string contains 14 characters including spaces.', 'It explains the process of converting a string to lowercase using the dot lower method and converting the string to uppercase using the upper method. The process of converting a string to lowercase using the dot lower method and to uppercase using the upper method is explained.', 'The transcript demonstrates the usage of the replace method to replace characters in a string, and the count method to determine the number of times a substring is repeated within a string. The usage of the replace method to replace characters and the count method to determine the number of times a substring is repeated within a string is demonstrated.', 'It illustrates the find method to determine the index value of a character within a string and the split method to divide a string into parts based on a specified criteria. The illustration of the find method to determine the index value of a character within a string and the split method to divide a string into parts based on a specified criteria is provided.']}], 'duration': 1172.02, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E1505161.jpg', 'highlights': ["Python keywords like 'true' and 'def' have special importance and attempting to use them as variable names results in errors.", 'Python identifiers are names for variables, functions, and objects, and must adhere to specific rules like case sensitivity and not starting with an underscore or a digit.', "Python literals, such as 'hello world' and 3.14, have unchangeable values and are different from variables.", 'The chapter introduces the concept of strings in Python, explaining the usage of single, double, and triple quotes for storing strings.', 'The transcript explains the concept of indexing in Python, including the use of index values and the colon to extract specific characters or sequences of characters from a string.', 'The len function is used to find the length of the string, showing that the string contains 14 characters including spaces.', 'The process of converting a string to lowercase using the dot lower method and to uppercase using the upper method is explained.', 'The usage of the replace method to replace characters and the count method to determine the number of times a substring is repeated within a string is demonstrated.', 'The illustration of the find method to determine the index value of a character within a string and the split method to divide a string into parts based on a specified criteria is provided.']}, {'end': 4713.105, 'segs': [{'end': 2726.907, 'src': 'embed', 'start': 2697.55, 'weight': 0, 'content': [{'end': 2700.552, 'text': 'So, in Python, there are four basic data structures.', 'start': 2697.55, 'duration': 3.002}, {'end': 2703.293, 'text': 'That is, tuple, list, dictionary and set.', 'start': 2700.572, 'duration': 2.721}, {'end': 2706.194, 'text': "So first of all, let's start with tuple.", 'start': 2703.993, 'duration': 2.201}, {'end': 2712.238, 'text': 'So as it is written here, tuple is an ordered collection of elements enclosed within round brackets.', 'start': 2706.715, 'duration': 5.523}, {'end': 2714.679, 'text': 'So this is an ordered collection of elements.', 'start': 2712.278, 'duration': 2.401}, {'end': 2719.162, 'text': 'So till now we were working with variables in which you could store only one value.', 'start': 2714.699, 'duration': 4.463}, {'end': 2726.907, 'text': 'So now as the data will increase, you will have to store a lot of elements together.', 'start': 2720.603, 'duration': 6.304}], 'summary': 'Python has four basic data structures: tuple, list, dictionary, and set.', 'duration': 29.357, 'max_score': 2697.55, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E2697550.jpg'}, {'end': 3510.468, 'src': 'embed', 'start': 3485.613, 'weight': 1, 'content': [{'end': 3491.496, 'text': 'So, using append method, I am adding a new item whose value is Sparta.', 'start': 3485.613, 'duration': 5.883}, {'end': 3498.06, 'text': 'Similarly, if I have to pop out an element from it, then L1.pop and I am popping out the last element.', 'start': 3492.117, 'duration': 5.943}, {'end': 3502.942, 'text': 'So, C is removed from it and as you can see in the final result, there is no C here.', 'start': 3498.1, 'duration': 4.842}, {'end': 3505.784, 'text': 'So, again we implement all these things in Python.', 'start': 3503.343, 'duration': 2.441}, {'end': 3510.468, 'text': 'So, this is our L1, I will print L1 again here.', 'start': 3507.785, 'duration': 2.683}], 'summary': 'Demonstrating list manipulation in python using append and pop methods.', 'duration': 24.855, 'max_score': 3485.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E3485613.jpg'}, {'end': 3912.803, 'src': 'embed', 'start': 3882.555, 'weight': 2, 'content': [{'end': 3885.758, 'text': 'And at the end of this, I have to concatenate L1.', 'start': 3882.555, 'duration': 3.203}, {'end': 3886.979, 'text': 'So just write plus L1.', 'start': 3885.798, 'duration': 1.181}, {'end': 3894.605, 'text': 'And you can see that I have repeated all this and then I have concatenated the values of L2 at the end of L1.', 'start': 3888.22, 'duration': 6.385}, {'end': 3895.786, 'text': 'So, we have finished the list.', 'start': 3894.625, 'duration': 1.161}, {'end': 3898.669, 'text': "So, let's head on to the next data structure which is dictionary.", 'start': 3895.967, 'duration': 2.702}, {'end': 3905.514, 'text': 'So, as written here, dictionary is an unordered collection of key value pairs enclosed within these curly braces.', 'start': 3899.369, 'duration': 6.145}, {'end': 3912.803, 'text': 'So, the difference between dictionary and other data structures is that in dictionary we have key value pair.', 'start': 3906.155, 'duration': 6.648}], 'summary': 'Concatenated l2 values to l1, moved to dictionary data structure.', 'duration': 30.248, 'max_score': 3882.555, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E3882555.jpg'}, {'end': 4398.503, 'src': 'embed', 'start': 4371.561, 'weight': 3, 'content': [{'end': 4378.107, 'text': 'So set is an unordered and unindexed collection of elements which you store in a curly braces.', 'start': 4371.561, 'duration': 6.546}, {'end': 4383.451, 'text': 'When I say unordered, it means that you have put all these elements in a sequence.', 'start': 4378.287, 'duration': 5.164}, {'end': 4386.794, 'text': 'But when you print it, it will not have any order.', 'start': 4384.312, 'duration': 2.482}, {'end': 4388.475, 'text': 'It will be stored in a random order.', 'start': 4386.814, 'duration': 1.661}, {'end': 4390.897, 'text': 'And it will not have any index.', 'start': 4388.535, 'duration': 2.362}, {'end': 4398.503, 'text': "So if there is no index, then you can't individually extract any element with the index.", 'start': 4390.917, 'duration': 7.586}], 'summary': 'Set is an unordered collection of elements stored in curly braces, without indexing or order.', 'duration': 26.942, 'max_score': 4371.561, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E4371561.jpg'}], 'start': 2677.201, 'title': 'Python data structures and operations', 'summary': 'Introduces python data structures like tuple and list, covering their properties, operations such as indexing, concatenation, repetition, and finding minimum and maximum values. it also details list operations, concatenation, repetition, dictionary manipulation, and sets in python.', 'chapters': [{'end': 3376.208, 'start': 2677.201, 'title': 'Python data structures basics', 'summary': 'Introduces python data structures including tuple and list, detailing their properties and operations, such as indexing, concatenation, repetition, and finding minimum and maximum values.', 'duration': 699.007, 'highlights': ['Python has four basic data structures: tuple, list, dictionary, and set. Python has four basic data structures: tuple, list, dictionary, and set.', 'Tuple is an ordered collection of elements enclosed within round brackets, allowing storage of heterogeneous elements and being immutable. Tuple is an ordered collection of elements enclosed within round brackets, allowing storage of heterogeneous elements and being immutable.', 'Indexing in tuple starts from zero, and individual elements can be extracted using indexing. Indexing in tuple starts from zero, and individual elements can be extracted using indexing.', 'Tuple concatenation and repetition can be performed using the plus and into operators respectively. Tuple concatenation and repetition can be performed using the plus and into operators respectively.', 'Functions like min and max can be used to find the minimum and maximum values in a tuple. Functions like min and max can be used to find the minimum and maximum values in a tuple.', 'List is mutable and defined with square braces, allowing storage of heterogeneous elements. List is mutable and defined with square braces, allowing storage of heterogeneous elements.']}, {'end': 3800.807, 'start': 3376.308, 'title': 'List operations in python', 'summary': 'Covers how to extract, modify, and manipulate elements in a list in python, including extracting individual elements, changing values, adding and removing elements, reversing, inserting, and sorting elements in a list.', 'duration': 424.499, 'highlights': ['The chapter covers how to extract, modify, and manipulate elements in a list in Python, including extracting individual elements, changing values, adding and removing elements, reversing, inserting, and sorting elements in a list. The chapter provides a comprehensive overview of list operations in Python, including extraction, modification, and manipulation of list elements through methods such as append, pop, reverse, insert, and sort.', 'The list is mutable, allowing for the change of its values. The list is mutable, enabling the alteration of its values, demonstrated by changing the value of the first element from 1 to 100 and adding a new element using the append method.', "Elements can be added to and removed from a list using methods such as append and pop. The methods append and pop are used to add a new element (e.g., 'Sparta') at the end of the list and remove the last element, respectively, demonstrating the dynamic nature of list operations.", 'The reverse method can be utilized to reverse the order of elements in a list. The reverse method is employed to reverse the order of elements in the list, showcasing the capability to manipulate the sequence of elements in a list.', 'The sort method can be used to arrange elements in a list in alphabetical order. The sort method is utilized to arrange the elements in the list in alphabetical order, exemplifying the ability to organize list elements based on specific criteria.']}, {'end': 4370.78, 'start': 3800.827, 'title': 'Concatenation, repetition in lists, dictionary operations', 'summary': 'Covers concatenation and repetition operations in lists, followed by creation and manipulation of dictionary data structure in python, emphasizing key-value pairs and mutability. the chapter concludes with an introduction to sets.', 'duration': 569.953, 'highlights': ['The chapter covers concatenation and repetition operations in lists, followed by creation and manipulation of dictionary data structure in Python. The transcript explains how to concatenate and repeat elements in lists, providing examples and step-by-step instructions.', 'Emphasizes key-value pairs and mutability in dictionaries. The transcript details the creation of a dictionary with key-value pairs and demonstrates how to modify, add, and remove elements within the dictionary.', 'Introduction to sets. The chapter introduces the final data structure, sets, without providing in-depth demonstration or examples.']}, {'end': 4713.105, 'start': 4371.561, 'title': 'Sets in python', 'summary': 'Explains the concept of sets in python, highlighting that sets are unordered and unindexed collections that do not allow duplicates. it also covers basic set operations such as adding, removing elements, finding the union and intersection of sets.', 'duration': 341.544, 'highlights': ['Sets are unordered and unindexed collections that do not allow duplicates.', 'Sets allow adding and removing elements using methods like add, remove, and update.', 'The chapter explains finding the union and intersection of sets using union and intersection methods.']}], 'duration': 2035.904, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E2677201.jpg', 'highlights': ['Python has four basic data structures: tuple, list, dictionary, and set.', 'The chapter provides a comprehensive overview of list operations in Python, including extraction, modification, and manipulation of list elements through methods such as append, pop, reverse, insert, and sort.', 'The chapter covers concatenation and repetition operations in lists, followed by creation and manipulation of dictionary data structure in Python.', 'Sets are unordered and unindexed collections that do not allow duplicates.']}, {'end': 6191.146, 'segs': [{'end': 4853.766, 'src': 'embed', 'start': 4824.727, 'weight': 0, 'content': [{'end': 4826.969, 'text': 'So, these are some basic examples of if statement.', 'start': 4824.727, 'duration': 2.242}, {'end': 4829.15, 'text': "So, let's see its pseudo code.", 'start': 4826.989, 'duration': 2.161}, {'end': 4834.273, 'text': 'Pseudo code means basically the blueprint of if statement is that.', 'start': 4829.55, 'duration': 4.723}, {'end': 4835.794, 'text': "So, let's understand this.", 'start': 4834.434, 'duration': 1.36}, {'end': 4839.437, 'text': 'First of all, you will write if and you will give condition in brackets.', 'start': 4835.814, 'duration': 3.623}, {'end': 4845.901, 'text': 'And if this condition is true, then you will execute the statements inside it.', 'start': 4839.897, 'duration': 6.004}, {'end': 4853.766, 'text': 'If this condition is false, then you will come to else and you will execute the statements in the block of else.', 'start': 4846.061, 'duration': 7.705}], 'summary': 'Basic examples and pseudo code explanation of if statement.', 'duration': 29.039, 'max_score': 4824.727, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E4824727.jpg'}, {'end': 5178.118, 'src': 'embed', 'start': 5149.264, 'weight': 1, 'content': [{'end': 5152.406, 'text': 'So after that we are checking is B bigger than C? This is false.', 'start': 5149.264, 'duration': 3.142}, {'end': 5155.208, 'text': 'So true and false will again give us false.', 'start': 5152.766, 'duration': 2.442}, {'end': 5157.51, 'text': 'So this is false, this is false.', 'start': 5155.248, 'duration': 2.262}, {'end': 5159.771, 'text': 'What else is left? Else.', 'start': 5157.53, 'duration': 2.241}, {'end': 5161.412, 'text': 'So we have printed whatever is in else.', 'start': 5159.791, 'duration': 1.621}, {'end': 5166.036, 'text': 'So now we will see how to use if statement with tuple, list and dictionary.', 'start': 5161.432, 'duration': 4.604}, {'end': 5167.517, 'text': 'I will again add a comment here.', 'start': 5166.056, 'duration': 1.461}, {'end': 5174.616, 'text': "If with tuple So I'll make a tuple here.", 'start': 5168.017, 'duration': 6.599}, {'end': 5178.118, 'text': "And I'll give the name of that tuple tup1.", 'start': 5174.636, 'duration': 3.482}], 'summary': 'Demonstration of using if statements with tuples, lists, and dictionaries. adding a comment.', 'duration': 28.854, 'max_score': 5149.264, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E5149264.jpg'}, {'end': 5639.998, 'src': 'embed', 'start': 5610.024, 'weight': 2, 'content': [{'end': 5614.947, 'text': 'And as soon as this condition is false, we will come out of the while loop.', 'start': 5610.024, 'duration': 4.923}, {'end': 5619.97, 'text': "So now let's go to the Jupyter Notebook and see some examples with the while loop.", 'start': 5616.408, 'duration': 3.562}, {'end': 5622.931, 'text': 'So suppose I have to print the first 10 numbers.', 'start': 5619.99, 'duration': 2.941}, {'end': 5625.772, 'text': "So I'll give you an example of that.", 'start': 5622.951, 'duration': 2.821}, {'end': 5631.174, 'text': 'First of all, I take a variable i and initialize it with 1.', 'start': 5625.812, 'duration': 5.362}, {'end': 5639.998, 'text': "And after that, I'll start the while loop and I'm checking the while loop while i is less than or equal to 10.", 'start': 5631.174, 'duration': 8.824}], 'summary': 'Demonstrating while loop with examples in jupyter notebook', 'duration': 29.974, 'max_score': 5610.024, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E5610024.jpg'}, {'end': 5885.212, 'src': 'embed', 'start': 5829.511, 'weight': 3, 'content': [{'end': 5830.672, 'text': 'After that, the value of i will be 3.', 'start': 5829.511, 'duration': 1.161}, {'end': 5831.413, 'text': '2x3 is equal to 6.', 'start': 5830.672, 'duration': 0.741}, {'end': 5833.354, 'text': 'The value of i will be 4.', 'start': 5831.413, 'duration': 1.941}, {'end': 5834.115, 'text': '2x4 is equal to 8.', 'start': 5833.354, 'duration': 0.761}, {'end': 5835.436, 'text': 'The value of i will be 5.', 'start': 5834.115, 'duration': 1.321}, {'end': 5836.277, 'text': '2x5 is equal to 10.', 'start': 5835.436, 'duration': 0.841}, {'end': 5838.579, 'text': 'So, we can print 2 tables like this.', 'start': 5836.277, 'duration': 2.302}, {'end': 5842.736, 'text': 'So these were some basic examples.', 'start': 5841.633, 'duration': 1.103}, {'end': 5845.825, 'text': "Now let's see how we can work with the while loop with the list.", 'start': 5842.756, 'duration': 3.069}, {'end': 5846.827, 'text': "I'll add a comment here.", 'start': 5845.865, 'duration': 0.962}, {'end': 5851.848, 'text': 'while with list.', 'start': 5850.487, 'duration': 1.361}, {'end': 5859.654, 'text': "So after this, I create a list, l1 equal to, and I'll add some values to it.", 'start': 5852.589, 'duration': 7.065}, {'end': 5861.755, 'text': '1, 2, 3, 4, and 5.', 'start': 5859.674, 'duration': 2.081}, {'end': 5863.497, 'text': 'My list is now ready.', 'start': 5861.755, 'duration': 1.742}, {'end': 5868.26, 'text': 'So now my aim is to add 100 values to each individual value.', 'start': 5863.957, 'duration': 4.303}, {'end': 5871.723, 'text': "So for this, I'll use while loop.", 'start': 5868.52, 'duration': 3.203}, {'end': 5877.95, 'text': "So first of all, I'll take a variable, i, and I'll initialize i from 0.", 'start': 5871.763, 'duration': 6.187}, {'end': 5885.212, 'text': 'So this is done and my while loop will be my condition while i is less than length of L1.', 'start': 5877.95, 'duration': 7.262}], 'summary': 'Demonstrated multiplication and while loop with list, aiming to add 100 values to each individual value.', 'duration': 55.701, 'max_score': 5829.511, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E5829511.jpg'}, {'end': 6084.119, 'src': 'embed', 'start': 6043.1, 'weight': 5, 'content': [{'end': 6044.561, 'text': 'After that, i became apple.', 'start': 6043.1, 'duration': 1.461}, {'end': 6045.602, 'text': 'After that, it became grapes.', 'start': 6044.581, 'duration': 1.021}, {'end': 6046.763, 'text': 'After that, it became orange.', 'start': 6045.622, 'duration': 1.141}, {'end': 6049.446, 'text': 'And we are printing all these values individually here.', 'start': 6046.863, 'duration': 2.583}, {'end': 6054.055, 'text': "And now let's look at multiple for loops.", 'start': 6051.793, 'duration': 2.262}, {'end': 6056.556, 'text': 'What are multiple for loops? Basically, nested for loops.', 'start': 6054.095, 'duration': 2.461}, {'end': 6059.478, 'text': 'Meaning, in one for loop, another for loop.', 'start': 6056.616, 'duration': 2.862}, {'end': 6064.201, 'text': "It's quite interesting, isn't it? So, let's look at an example of this.", 'start': 6059.498, 'duration': 4.703}, {'end': 6067.163, 'text': "So, first of all, I'll make an L1 again.", 'start': 6064.221, 'duration': 2.942}, {'end': 6069.805, 'text': "And in this, I'll add some colors here.", 'start': 6067.243, 'duration': 2.562}, {'end': 6071.366, 'text': "So, I've got orange.", 'start': 6069.825, 'duration': 1.541}, {'end': 6084.119, 'text': "After orange, I've got The first item is the chair, the second item is the book, and the third item is the laptop.", 'start': 6071.406, 'duration': 12.713}], 'summary': 'Demonstration of nested for loops iterating through colors and items.', 'duration': 41.019, 'max_score': 6043.1, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E6043100.jpg'}], 'start': 4728.578, 'title': 'Python decision making and loops', 'summary': 'Covers decision-making statements in python including if statements, pseudo code of if statement, and testing conditions. it also explains basic if-else statements, usage with tuple, list, and dictionary, while loops, and for loops with relevant examples and outcomes.', 'chapters': [{'end': 4954.149, 'start': 4728.578, 'title': 'Python decision making statements', 'summary': 'Covers common elements between s1 and s3, examples of decision-making statements in python including if statements, pseudo code of if statement, and testing conditions using if statement.', 'duration': 225.571, 'highlights': ['The chapter covers common elements between S1 and S3, where 5 and 6 are identified as common between S1 and S3.', 'Examples of if statements are provided including scenarios of playing football in the rain and getting ice cream based on exam marks.', 'The pseudo code of the if statement is explained, including the structure of the if and else blocks, and the importance of indentation.', 'Testing conditions using if statements are demonstrated using Python code, including checking if B is greater than A and the impact of indentation.']}, {'end': 5149.244, 'start': 4954.169, 'title': 'Basic if-else statements', 'summary': 'Explains basic if-else statements, demonstrating their usage and providing an example of finding the greatest value among three numbers using if-else conditions, with specific values and outcomes highlighted throughout the explanation.', 'duration': 195.075, 'highlights': ['The chapter covers the basic usage of if-else statements, emphasizing the need for an else statement in certain conditions and providing a clear example of using else to handle false evaluations, with a specific demonstration of how the else statement works. It also highlights the use of if, else if (elif), and else statements to determine the greatest value among three numbers, with specific values and outcomes showcased throughout the explanation.', 'The explanation includes a demonstration of using if, else if (elif), and else statements to determine the greatest value among three numbers, with specific values (A=10, B=20, C=30) and the corresponding outcomes (C is the greatest) clearly highlighted, illustrating the logical flow and outcome of the if-else conditions.', 'The transcript provides a detailed example of using if-else statements to compare the values of A, B, and C, showcasing the logical flow of the conditions and the resulting outcome (C is the greatest) with specific values (A=10, B=20, C=30) and clear explanations of the evaluation process.']}, {'end': 5441.736, 'start': 5149.264, 'title': 'Using if statements with tuple, list, and dictionary', 'summary': 'Explains how to use if statements with tuple, list, and dictionary, demonstrating their functionality with examples and outcomes, and later delves into the basics of looping statements.', 'duration': 292.472, 'highlights': ['Demonstrated using if statements with tuple, list, and dictionary The transcript explains the functionality of if statements with tuple, list, and dictionary, providing examples and outcomes for each, highlighting their usage.', 'Modified the value in the list based on a condition The speaker demonstrates modifying the value in a list based on a condition and then prints the modified list, highlighting the practical application of an if statement with a list.', 'Checked and modified the value in the dictionary based on a condition The speaker checks if a specific value in a dictionary meets a condition and then modifies the value based on the outcome, effectively showcasing the use of an if statement with a dictionary.', 'Explained the basics of FL statement and introduced looping statements The chapter concludes by explaining the basics of FL statement and introducing looping statements, emphasizing their role in repeating tasks.']}, {'end': 5567.402, 'start': 5441.916, 'title': 'Repetition in daily life', 'summary': 'Discusses the concept of repetition through examples like filling a bucket with water, listening to a song on loop, and receiving a salary every month.', 'duration': 125.486, 'highlights': ['Filling a bucket with water by repeatedly filling it with a mug until it is full. The process of filling a bucket with water through repetitive actions of filling it with a mug until it is fully filled.', 'Listening to a song repeatedly on a music app by putting it in a loop or on repeat. The concept of looping a song on a music app to listen to it repeatedly, exemplifying the idea of repetition.', 'Receiving a monthly salary as a repetitive task after working for 29 days. The repetitive nature of receiving a salary at the end of every month after completing a set number of working days.']}, {'end': 5709.203, 'start': 5567.422, 'title': 'Introduction to while loop', 'summary': 'Introduces the concept of while loop with examples, explaining the flow diagram and the process of condition testing, statement execution, and exiting the loop, illustrating the printing of the first 10 numbers using a while loop.', 'duration': 141.781, 'highlights': ['The chapter explains the process of condition testing, statement execution, and exiting the while loop, with an example of printing the first 10 numbers using a while loop.', 'The chapter introduces the concept of while loop with examples and explains the flow diagram.', 'The chapter provides an example of printing the first 10 numbers using a while loop, demonstrating the process of condition testing and statement execution.']}, {'end': 6191.146, 'start': 5709.203, 'title': 'Using while and for loops in python', 'summary': 'Explains the usage of while and for loops in python, demonstrating how to create multiplication tables using while loops and iterating through lists using for loops, along with nested for loops.', 'duration': 481.943, 'highlights': ['The chapter explains the usage of while loops to create multiplication tables up to 10, demonstrating the iteration process and the resulting values. Creation of multiplication tables using while loop, demonstration of iteration process, result values up to 10', 'It demonstrates the usage of a while loop to add 100 to each individual value in a given list, showcasing the iteration and resulting modified list values. Usage of while loop to modify individual list values, addition of 100 to each value, resulting modified list values', 'The chapter illustrates the usage of for loops to iterate through a list of fruits, showcasing the individual printing of each element. Iteration through a list of fruits using for loop, individual printing of each element', 'It provides an example of nested for loops to iterate through two lists, demonstrating the iteration process and resulting combinations of elements from both lists. Usage of nested for loops, iteration through two lists, resulting combinations of elements']}], 'duration': 1462.568, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E4728578.jpg', 'highlights': ['The chapter covers the basic usage of if-else statements, emphasizing the need for an else statement in certain conditions and providing a clear example of using else to handle false evaluations, with a specific demonstration of how the else statement works.', 'Demonstrated using if statements with tuple, list, and dictionary The transcript explains the functionality of if statements with tuple, list, and dictionary, providing examples and outcomes for each, highlighting their usage.', 'The chapter explains the process of condition testing, statement execution, and exiting the while loop, with an example of printing the first 10 numbers using a while loop.', 'The chapter explains the usage of while loops to create multiplication tables up to 10, demonstrating the iteration process and the resulting values.', 'It demonstrates the usage of a while loop to add 100 to each individual value in a given list, showcasing the iteration and resulting modified list values.', 'The chapter illustrates the usage of for loops to iterate through a list of fruits, showcasing the individual printing of each element.']}, {'end': 7074.671, 'segs': [{'end': 6366.554, 'src': 'embed', 'start': 6338.595, 'weight': 2, 'content': [{'end': 6341.237, 'text': "And let's say there are a lot of people in the line.", 'start': 6338.595, 'duration': 2.642}, {'end': 6343.619, 'text': 'This time, from 1000 to 10,000 people are standing.', 'start': 6341.257, 'duration': 2.362}, {'end': 6345.48, 'text': 'In a day, 10,000 transactions happen easily.', 'start': 6343.639, 'duration': 1.841}, {'end': 6350.203, 'text': 'So, for those 10,000 transactions, you have to write 1000 lines again.', 'start': 6346.3, 'duration': 3.903}, {'end': 6351.044, 'text': 'So, that 10,000 x 1000.', 'start': 6350.223, 'duration': 0.821}, {'end': 6357.168, 'text': 'Instead of doing such a repetitive task, you have to write the withdraw function of 1000 lines of code.', 'start': 6351.044, 'duration': 6.124}, {'end': 6359.589, 'text': 'And this withdraw function sees everything else.', 'start': 6357.188, 'duration': 2.401}, {'end': 6366.554, 'text': 'Similarly, if you want to check the balance suppose this is 500 lines of code then just write 500 lines of code.', 'start': 6362.051, 'duration': 4.503}], 'summary': '10,000 transactions/day, 1000 lines/transaction, automate with 1000-line functions', 'duration': 27.959, 'max_score': 6338.595, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E6338595.jpg'}, {'end': 6485.191, 'src': 'embed', 'start': 6455.95, 'weight': 1, 'content': [{'end': 6464.156, 'text': 'So now we actually have to create a function which takes a value and returns that value by adding plus 10.', 'start': 6455.95, 'duration': 8.206}, {'end': 6477.025, 'text': "So let's create that function def and I will write the name of the function as add10 and it will take a parameter and randomly I am writing x here.", 'start': 6464.156, 'duration': 12.869}, {'end': 6479.426, 'text': 'So whatever value we give inside this function will be stored in x.', 'start': 6477.065, 'duration': 2.361}, {'end': 6485.191, 'text': 'and it will return x plus 10.', 'start': 6482.208, 'duration': 2.983}], 'summary': 'Create function add10 to add 10 to a given value.', 'duration': 29.241, 'max_score': 6455.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E6455950.jpg'}, {'end': 6619.58, 'src': 'embed', 'start': 6549.928, 'weight': 0, 'content': [{'end': 6552.729, 'text': 'So I have to print.', 'start': 6549.928, 'duration': 2.801}, {'end': 6553.05, 'text': 'X is even.', 'start': 6552.749, 'duration': 0.301}, {'end': 6570.407, 'text': 'And if it is not zero after dividing by 2, then I have to print x is odd.', 'start': 6560.24, 'duration': 10.167}, {'end': 6576.35, 'text': 'So I have created a function here.', 'start': 6570.827, 'duration': 5.523}, {'end': 6579.552, 'text': 'After this, I will write odd even again.', 'start': 6576.39, 'duration': 3.162}, {'end': 6582.074, 'text': 'I am invoking the function here.', 'start': 6579.652, 'duration': 2.422}, {'end': 6586.078, 'text': 'I have to pass a value here, actually.', 'start': 6583.857, 'duration': 2.221}, {'end': 6587.219, 'text': "So, I'll pass 5.", 'start': 6586.298, 'duration': 0.921}, {'end': 6590.881, 'text': "As you can see, it's printing 5 as odd.", 'start': 6587.219, 'duration': 3.662}, {'end': 6592.762, 'text': "Similarly, I'll pass an even number.", 'start': 6591.421, 'duration': 1.341}, {'end': 6594.163, 'text': "Let's pass 10 and see.", 'start': 6592.802, 'duration': 1.361}, {'end': 6595.243, 'text': 'You can see 10 is even.', 'start': 6594.183, 'duration': 1.06}, {'end': 6598.285, 'text': 'So, these are the basic functions.', 'start': 6595.263, 'duration': 3.022}, {'end': 6603.668, 'text': "Now, there's a special type of function in Python which you call lambda function.", 'start': 6598.305, 'duration': 5.363}, {'end': 6606.129, 'text': 'Lambda function is also called anonymous function.', 'start': 6604.228, 'duration': 1.901}, {'end': 6611.473, 'text': "It's called anonymous function because it doesn't have a name.", 'start': 6607.33, 'duration': 4.143}, {'end': 6613.515, 'text': "So you don't define it.", 'start': 6611.493, 'duration': 2.022}, {'end': 6619.58, 'text': 'You just give the parameters of the function in one line and give the code of the function, the expression.', 'start': 6613.855, 'duration': 5.725}], 'summary': 'Demonstration of basic and lambda functions in python with examples of odd and even number identification.', 'duration': 69.652, 'max_score': 6549.928, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E6549928.jpg'}, {'end': 6920.195, 'src': 'embed', 'start': 6883.251, 'weight': 4, 'content': [{'end': 6884.692, 'text': 'Lambda with map.', 'start': 6883.251, 'duration': 1.441}, {'end': 6886.873, 'text': 'So again I make a list here.', 'start': 6885.492, 'duration': 1.381}, {'end': 6891.035, 'text': 'Here I come with simple values.', 'start': 6886.893, 'duration': 4.142}, {'end': 6898.619, 'text': 'So from 1 to here, I will store all the values up to 8 in L1.', 'start': 6891.055, 'duration': 7.564}, {'end': 6904.483, 'text': 'After this, my task is to multiply all the individual elements by 2.', 'start': 6898.639, 'duration': 5.844}, {'end': 6905.964, 'text': 'So 1 x 2 will be 2, 2 x 2 will be 4, 3 x 2 will be 6.', 'start': 6904.483, 'duration': 1.481}, {'end': 6906.965, 'text': 'For this, I will use the map function.', 'start': 6905.964, 'duration': 1.001}, {'end': 6920.195, 'text': 'So, map function helps me to map one function or task on all individual elements.', 'start': 6914.79, 'duration': 5.405}], 'summary': 'Using map function to multiply elements in a list by 2.', 'duration': 36.944, 'max_score': 6883.251, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E6883251.jpg'}, {'end': 7040.589, 'src': 'embed', 'start': 7012.985, 'weight': 3, 'content': [{'end': 7016.469, 'text': 'Or we just want a final result on top of a sequence.', 'start': 7012.985, 'duration': 3.484}, {'end': 7018.311, 'text': 'So I will give an example again here.', 'start': 7016.489, 'duration': 1.822}, {'end': 7021.734, 'text': 'I will write reduce here.', 'start': 7019.412, 'duration': 2.322}, {'end': 7023.295, 'text': 'It takes two parameters again.', 'start': 7021.754, 'duration': 1.541}, {'end': 7025.016, 'text': 'The first parameter will be lambda function.', 'start': 7023.375, 'duration': 1.641}, {'end': 7026.738, 'text': 'The second parameter will be list.', 'start': 7025.157, 'duration': 1.581}, {'end': 7028.139, 'text': 'So I have given this list here.', 'start': 7026.758, 'duration': 1.381}, {'end': 7030.861, 'text': 'Then I will create the lambda function here.', 'start': 7028.179, 'duration': 2.682}, {'end': 7035.505, 'text': 'And it is taking two parameters here.', 'start': 7030.941, 'duration': 4.564}, {'end': 7036.385, 'text': 'x, y.', 'start': 7035.725, 'duration': 0.66}, {'end': 7040.589, 'text': 'And what is it actually doing? x plus y.', 'start': 7036.385, 'duration': 4.204}], 'summary': 'Demonstrating the use of reduce function with lambda function to calculate the sum of a list.', 'duration': 27.604, 'max_score': 7012.985, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7012985.jpg'}], 'start': 6191.506, 'title': 'Functions and lambda functions in programming', 'summary': 'Covers the concept of functions in programming, providing examples and quantifiable data. it also explains the usage of lambda functions with filter, map, and reduce functions, achieving a final result of 36.', 'chapters': [{'end': 6684.551, 'start': 6191.506, 'title': 'Functions in programming', 'summary': 'Explains functions in programming as a block of code performing a specific task, giving examples with quantifiable data of lines of code and transactions, and demonstrates the creation of basic and lambda functions in python.', 'duration': 493.045, 'highlights': ['Functions are explained as a block of code which performs a specific task, with examples of eating, running, and cycling as real-life functions. Explanation of functions in real life tasks, illustrating the concept of multiple tasks involved, quantifiable data not provided.', 'An example of using functions in programming is demonstrated with an online banking scenario, where the deposit, withdraw, and balance functions are explained, emphasizing the reduction of repetitive tasks with quantifiable data of 1000 lines of code and 10,000 transactions. Illustration of using functions in programming with an online banking scenario, emphasizing the reduction of repetitive tasks with quantifiable data of 1000 lines of code and 10,000 transactions.', "The creation and usage of a basic function in Python, 'hello', is demonstrated with no parameters and a print statement, showcasing the function definition and invocation. Demonstration of creating and using a basic function in Python, 'hello', with no parameters and a print statement.", "A function named 'add10' is created to take a parameter and return the value by adding 10, with demonstrations of invoking the function with different values and quantifiable data of the returned values. Creation of the 'add10' function to add 10 to a parameter and return the value, with demonstrations of invoking the function with different values and quantifiable data of the returned values.", "The creation of a function to check if a number is even or odd ('odd even') is demonstrated, with examples of invoking the function with different values and quantifiable data of the printed results. Demonstration of creating a function to check if a number is even or odd ('odd even'), with examples of invoking the function with different values and quantifiable data of the printed results.", "The concept of lambda functions in Python is introduced, showcasing the creation of an anonymous function using the 'Lambda' keyword and demonstrating its usage with quantifiable data of the computed results. Introduction of lambda functions in Python, showcasing the creation of an anonymous function using the 'Lambda' keyword and demonstrating its usage with quantifiable data of the computed results."]}, {'end': 7074.671, 'start': 6684.551, 'title': 'Lambda functions: filter, map, reduce', 'summary': 'Explains the usage of lambda functions with filter, map, and reduce functions, demonstrating how to filter odd and even values, map elements to a new list, and reduce a sequence to a final result, achieving a final result of 36.', 'duration': 390.12, 'highlights': ['Demonstrating how to filter odd and even values The chapter provides an example of using lambda function with the filter function to filter odd and even values from a list, showcasing the practical application of lambda functions.', 'Mapping elements to a new list The chapter demonstrates the usage of lambda function with the map function to multiply individual elements of a list by 2, resulting in a new list with the mapped values.', 'Reducing a sequence to a final result of 36 The chapter explains the application of lambda function with the reduce function to obtain a final result of 36 by adding all the values in a sequence, showcasing the capability of lambda functions in reducing a sequence to a consolidated result.']}], 'duration': 883.165, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E6191506.jpg', 'highlights': ["Introduction of lambda functions in Python, showcasing the creation of an anonymous function using the 'Lambda' keyword and demonstrating its usage with quantifiable data of the computed results.", "Creation of the 'add10' function to add 10 to a parameter and return the value, with demonstrations of invoking the function with different values and quantifiable data of the returned values.", 'Illustration of using functions in programming with an online banking scenario, emphasizing the reduction of repetitive tasks with quantifiable data of 1000 lines of code and 10,000 transactions.', 'The chapter explains the application of lambda function with the reduce function to obtain a final result of 36 by adding all the values in a sequence, showcasing the capability of lambda functions in reducing a sequence to a consolidated result.', 'The chapter demonstrates the usage of lambda function with the map function to multiply individual elements of a list by 2, resulting in a new list with the mapped values.', "Demonstration of creating a function to check if a number is even or odd ('odd even'), with examples of invoking the function with different values and quantifiable data of the printed results."]}, {'end': 7769.784, 'segs': [{'end': 7179.553, 'src': 'embed', 'start': 7148.174, 'weight': 1, 'content': [{'end': 7151.977, 'text': 'There are many complex mathematical operations.', 'start': 7148.174, 'duration': 3.803}, {'end': 7159.783, 'text': 'If we want to implement them from scratch in Python code, it will take a lot of time.', 'start': 7152.037, 'duration': 7.746}, {'end': 7164.706, 'text': 'So, in return, Python gives us a NumPy library which can do numerical computing very easily.', 'start': 7160.023, 'duration': 4.683}, {'end': 7171.41, 'text': 'After that, we have another library named Matplotlib, through which we can do visualization.', 'start': 7167.188, 'duration': 4.222}, {'end': 7179.553, 'text': 'For example, barplot, graph chart, scatterplot, all these different types of visualizations can be done with Matplotlib library.', 'start': 7171.47, 'duration': 8.083}], 'summary': 'Numpy and matplotlib in python ease complex mathematical operations and enable diverse visualizations.', 'duration': 31.379, 'max_score': 7148.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7148174.jpg'}, {'end': 7245.651, 'src': 'embed', 'start': 7193.322, 'weight': 0, 'content': [{'end': 7198.166, 'text': 'And it is a core library for numeric and scientific computing, which I have already told you.', 'start': 7193.322, 'duration': 4.844}, {'end': 7203.411, 'text': 'And this Python NumPy has a lot of multi-dimensional array objects.', 'start': 7198.707, 'duration': 4.704}, {'end': 7210.536, 'text': 'So, if you have worked in C, C++ or Java, then you must have read some data type of array.', 'start': 7203.931, 'duration': 6.605}, {'end': 7218.263, 'text': 'So, assume this is the same type of data structure that NumPy provides you with which you can do multi-dimensional computing.', 'start': 7210.596, 'duration': 7.667}, {'end': 7225.188, 'text': "So, if you take Java's array or C's array, then it has only one dimension, it has multiple dimensions.", 'start': 7218.503, 'duration': 6.685}, {'end': 7229.451, 'text': 'In this, you can work with row, column, x-axis, y-axis and z-axis.', 'start': 7225.268, 'duration': 4.183}, {'end': 7230.172, 'text': 'So, this is very good.', 'start': 7229.471, 'duration': 0.701}, {'end': 7236.337, 'text': 'And with these multidimensional arrays, you have some methods.', 'start': 7232.874, 'duration': 3.463}, {'end': 7240.46, 'text': 'Using those methods, you can process these multidimensional arrays.', 'start': 7236.397, 'duration': 4.063}, {'end': 7245.651, 'text': 'So this is a brief introduction of NumPy array.', 'start': 7242.91, 'duration': 2.741}], 'summary': 'Python numpy provides multi-dimensional array objects for scientific computing, enabling processing of multidimensional arrays with methods.', 'duration': 52.329, 'max_score': 7193.322, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7193322.jpg'}, {'end': 7426.765, 'src': 'embed', 'start': 7398.316, 'weight': 3, 'content': [{'end': 7401.017, 'text': 'And now I will show its type.', 'start': 7398.316, 'duration': 2.701}, {'end': 7402.477, 'text': 'Type of n1.', 'start': 7401.177, 'duration': 1.3}, {'end': 7407.979, 'text': 'So as you can see, this is NumPy.ndArray which means we have created a NumPy n-dimensional array.', 'start': 7402.497, 'duration': 5.482}, {'end': 7413.841, 'text': "So this is our single dimensional array, so now let's create a multidimensional array.", 'start': 7410.02, 'duration': 3.821}, {'end': 7421.163, 'text': 'So again I will write np.array and I will pass list of lists.', 'start': 7414.101, 'duration': 7.062}, {'end': 7426.765, 'text': 'So first of all I will create a list, then another list, then I will add comma and create another list.', 'start': 7421.183, 'duration': 5.582}], 'summary': 'Demonstration of creating numpy n-dimensional array with list of lists.', 'duration': 28.449, 'max_score': 7398.316, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7398316.jpg'}, {'end': 7491.336, 'src': 'embed', 'start': 7458.22, 'weight': 4, 'content': [{'end': 7461.982, 'text': "So now let's see how to initialize a NumPy array.", 'start': 7458.22, 'duration': 3.762}, {'end': 7469.928, 'text': 'So sometimes we just need a NumPy array in which initially we put zeros and then if we want to manipulate something, we can manipulate it later.', 'start': 7462.283, 'duration': 7.645}, {'end': 7474.591, 'text': 'So here we have a method in NumPy which is called zeros method.', 'start': 7470.388, 'duration': 4.203}, {'end': 7479.932, 'text': 'So we have loaded NumPy and after that we are writing here np.zeros.', 'start': 7475.151, 'duration': 4.781}, {'end': 7483.433, 'text': 'So we are giving two parameters to np.zeros.', 'start': 7480.373, 'duration': 3.06}, {'end': 7491.336, 'text': 'So how many rows do we need and how many columns do we need? So we need a NumPy array whose dimensions are 1 x 2.', 'start': 7483.814, 'duration': 7.522}], 'summary': 'Initializing a numpy array with zeros method for a 1x2 array.', 'duration': 33.116, 'max_score': 7458.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7458220.jpg'}, {'end': 7733.458, 'src': 'embed', 'start': 7703.815, 'weight': 5, 'content': [{'end': 7706.037, 'text': 'Let me create a new NumPy array here.', 'start': 7703.815, 'duration': 2.222}, {'end': 7707.619, 'text': 'I will write n1 here and here will be np.array.', 'start': 7706.057, 'duration': 1.562}, {'end': 7709, 'text': 'Actually, it will be np.arange.', 'start': 7707.639, 'duration': 1.361}, {'end': 7712.343, 'text': "And what is the range? Let's say from 50 to..", 'start': 7709.16, 'duration': 3.183}, {'end': 7724.056, 'text': 'I need all the numbers till 100.', 'start': 7722.236, 'duration': 1.82}, {'end': 7726.097, 'text': 'So, I will give 50 to 101 and I will print N1 here.', 'start': 7724.056, 'duration': 2.041}, {'end': 7729.757, 'text': 'So, you can see that I have printed all the numbers till 50 to 100.', 'start': 7726.117, 'duration': 3.64}, {'end': 7731.638, 'text': 'Similarly, I need values from 50 to 500.', 'start': 7729.757, 'duration': 1.881}, {'end': 7733.458, 'text': 'So, I will give 500 here.', 'start': 7731.638, 'duration': 1.82}], 'summary': 'Creating numpy arrays with ranges from 50 to 100 and 50 to 500.', 'duration': 29.643, 'max_score': 7703.815, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7703815.jpg'}], 'start': 7074.992, 'title': 'Python libraries and numpy array creation', 'summary': 'Covers the introduction to python libraries such as pandas, numpy, and matplotlib, emphasizing their benefits and use cases. it also delves into numpy array creation, including single and multi-dimensional arrays, initialization methods like np.zeros, np.full, and np.arange, with practical examples.', 'chapters': [{'end': 7287.583, 'start': 7074.992, 'title': 'Introduction to python libraries', 'summary': 'Introduces the concept of libraries in python, specifically focusing on pandas for data manipulation, numpy for numerical computing, and matplotlib for visualization, highlighting their benefits and use cases. it also discusses the basic intro of numpy library and how to create numpy arrays in python.', 'duration': 212.591, 'highlights': ['Python provides libraries like Pandas, NumPy, and Matplotlib for data manipulation, numerical computing, and visualization, which help in simplifying tasks and reducing manual effort.', 'NumPy is a core library for numeric and scientific computing, offering multi-dimensional array objects and methods for processing them, making it efficient for complex mathematical operations.', 'The introduction includes the benefits of using NumPy, such as multi-dimensional computing capabilities, and the ability to work with arrays having multiple dimensions, including row, column, x-axis, y-axis, and z-axis.']}, {'end': 7769.784, 'start': 7288.323, 'title': 'Numpy array creation and initialization', 'summary': 'Discusses creating single-dimensional and multi-dimensional numpy arrays, initializing arrays with zeros, specific values, and within a range using methods such as np.zeros, np.full, and np.arange, demonstrating various examples and parameters.', 'duration': 481.461, 'highlights': ['Demonstrating creation of single-dimensional and multi-dimensional NumPy arrays The speaker demonstrates creating single-dimensional array by passing a list in np.array method and creating multi-dimensional array by passing a list of lists, showing examples of 1 row 2 columns and 2 rows 4 columns arrays.', 'Initializing arrays with zeros and specific values using np.zeros and np.full methods The speaker initializes a 2x2 NumPy array with zeros and a 3x3 NumPy array with specific value 55 using np.zeros and np.full methods respectively.', 'Illustrating array initialization within a range using np.arange method The speaker illustrates array initialization within a range using np.arange method, showcasing examples of creating arrays from 10 to 19 and from 50 to 100 with specified steps, and from 50 to 490 with a gap of 10.']}], 'duration': 694.792, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7074992.jpg', 'highlights': ['NumPy is a core library for numeric and scientific computing, offering multi-dimensional array objects and methods for processing them, making it efficient for complex mathematical operations.', 'Python provides libraries like Pandas, NumPy, and Matplotlib for data manipulation, numerical computing, and visualization, which help in simplifying tasks and reducing manual effort.', 'The introduction includes the benefits of using NumPy, such as multi-dimensional computing capabilities, and the ability to work with arrays having multiple dimensions, including row, column, x-axis, y-axis, and z-axis.', 'Demonstrating creation of single-dimensional and multi-dimensional NumPy arrays The speaker demonstrates creating single-dimensional array by passing a list in np.array method and creating multi-dimensional array by passing a list of lists, showing examples of 1 row 2 columns and 2 rows 4 columns arrays.', 'Initializing arrays with zeros and specific values using np.zeros and np.full methods The speaker initializes a 2x2 NumPy array with zeros and a 3x3 NumPy array with specific value 55 using np.zeros and np.full methods respectively.', 'Illustrating array initialization within a range using np.arange method The speaker illustrates array initialization within a range using np.arange method, showcasing examples of creating arrays from 10 to 19 and from 50 to 100 with specified steps, and from 50 to 490 with a gap of 10.']}, {'end': 8893.663, 'segs': [{'end': 7915.595, 'src': 'embed', 'start': 7890.565, 'weight': 4, 'content': [{'end': 7896.527, 'text': 'and it is telling me that there are 2 rows and 3 columns in this array.', 'start': 7890.565, 'duration': 5.962}, {'end': 7903.931, 'text': 'And if I want to change this shape then I will just have to invoke the shape operator again with this object.', 'start': 7896.607, 'duration': 7.324}, {'end': 7910.893, 'text': 'Since this array is stored in n1, I will write n1.shape and change the dimensions.', 'start': 7904.491, 'duration': 6.402}, {'end': 7912.614, 'text': 'Initially it was 2x3, I am changing it to 3x2.', 'start': 7910.953, 'duration': 1.661}, {'end': 7915.595, 'text': 'And now when I am looking at the shape, it has become 3x2.', 'start': 7912.634, 'duration': 2.961}], 'summary': 'Array has been modified from 2x3 to 3x2.', 'duration': 25.03, 'max_score': 7890.565, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7890565.jpg'}, {'end': 8048.587, 'src': 'embed', 'start': 8019.969, 'weight': 1, 'content': [{'end': 8024.712, 'text': 'First, we can stack vertically, then horizontally, and then we can stack columns.', 'start': 8019.969, 'duration': 4.743}, {'end': 8026.573, 'text': "So let's know these three well.", 'start': 8024.732, 'duration': 1.841}, {'end': 8028.735, 'text': "First of all, let's see the vertical stack.", 'start': 8026.593, 'duration': 2.142}, {'end': 8030.856, 'text': 'Here we already have two NumPy arrays.', 'start': 8028.775, 'duration': 2.081}, {'end': 8031.096, 'text': 'Here N1, N2.', 'start': 8030.876, 'duration': 0.22}, {'end': 8031.777, 'text': 'N1 has 10, 20, 30.', 'start': 8031.116, 'duration': 0.661}, {'end': 8036.999, 'text': 'N2 has 40, 50, 60.', 'start': 8031.777, 'duration': 5.222}, {'end': 8042.403, 'text': 'If I want to stack vertically, i.e. on top of one array, I am stacking the other array vertically.', 'start': 8037, 'duration': 5.403}, {'end': 8048.587, 'text': 'So here I just have to use the np.vstack method and I will pass both these numpy arrays in it.', 'start': 8042.443, 'duration': 6.144}], 'summary': "Demonstrates stacking arrays vertically using numpy's np.vstack method.", 'duration': 28.618, 'max_score': 8019.969, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E8019969.jpg'}, {'end': 8608.588, 'src': 'embed', 'start': 8582.152, 'weight': 0, 'content': [{'end': 8588.216, 'text': 'Suppose we have a NumPy array and we want to do scalar operations to it.', 'start': 8582.152, 'duration': 6.064}, {'end': 8594.579, 'text': 'Suppose the array is 10, 20, 30 and I want to add 1 to all these individual values.', 'start': 8588.296, 'duration': 6.283}, {'end': 8596.2, 'text': "So I'll just write N1 plus 1.", 'start': 8594.6, 'duration': 1.6}, {'end': 8599.823, 'text': "N1 is the name of the NumPy array so I'll just write N1 plus 1 and 1 has been added.", 'start': 8596.2, 'duration': 3.623}, {'end': 8608.588, 'text': 'Similarly, if I want to multiply a NumPy array with a scalar value, here N1 is my array, I will do N1 into 2, so here 10, 20, 30 is there.', 'start': 8602.244, 'duration': 6.344}], 'summary': 'Numpy array: scalar operations - add 1, multiply by 2.', 'duration': 26.436, 'max_score': 8582.152, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E8582152.jpg'}, {'end': 8818.602, 'src': 'embed', 'start': 8789.914, 'weight': 3, 'content': [{'end': 8793.798, 'text': 'So, here we have created a NumPy array in which all these values are present.', 'start': 8789.914, 'duration': 3.884}, {'end': 8799.525, 'text': 'So if we want to save it, we will save np.', 'start': 8797.403, 'duration': 2.122}, {'end': 8803.389, 'text': 'And here we will save it with a name.', 'start': 8799.685, 'duration': 3.704}, {'end': 8807.673, 'text': 'And I am saving it with the name of myNumpy.', 'start': 8803.529, 'duration': 4.144}, {'end': 8812.678, 'text': 'And here we have the second parameter that I have to save the numpy array, I will pass that object.', 'start': 8807.693, 'duration': 4.985}, {'end': 8814.92, 'text': 'We have saved this.', 'start': 8814.099, 'duration': 0.821}, {'end': 8818.602, 'text': 'After saving, if I want to use it, I will have to load it again.', 'start': 8815.04, 'duration': 3.562}], 'summary': "Created a numpy array, saved it as 'mynumpy', and can load it later.", 'duration': 28.688, 'max_score': 8789.914, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E8789914.jpg'}], 'start': 7769.784, 'title': 'Numpy array operations', 'summary': 'Covers array initialization, shape manipulation, stacking arrays, intersection, difference operations, addition, scalar operations, arithmetic operations, mean, median, standard deviation, and saving/loading arrays.', 'chapters': [{'end': 7867.815, 'start': 7769.784, 'title': 'Initializing numpy arrays with random numbers', 'summary': "Demonstrates how to use numpy's random sub-module to generate random integers within specified ranges, such as generating 5 random integers between 1 and 100 and 10 random integers between 100 and 200, with the ability to obtain different sets of random numbers upon each execution.", 'duration': 98.031, 'highlights': ['The chapter explains the use of np.random.randint to generate 10 random numbers between 100 and 200, showcasing the ability to obtain different sets of random numbers upon each execution.', 'It demonstrates the use of np.random.randint to generate 5 random integers between 1 and 100, emphasizing the range and quantity of random values obtained.']}, {'end': 8430.811, 'start': 7868.075, 'title': 'Numpy array operations', 'summary': 'Covers numpy array initialization, shape manipulation, stacking arrays vertically, horizontally, and column-wise, and performing intersection and difference operations on arrays.', 'duration': 562.736, 'highlights': ['The chapter covers NumPy array initialization, shape manipulation, stacking arrays vertically, horizontally, and column-wise, and performing intersection and difference operations on arrays. NumPy array initialization, shape manipulation, stacking arrays vertically, horizontally, and column-wise, intersection and difference operations.', 'The example demonstrates changing the shape of a NumPy array from 2x3 to 3x2 and then to 4x2, showcasing the ability to alter array dimensions. Changing the shape of a NumPy array from 2x3 to 3x2 and then to 4x2.', 'The process of stacking arrays vertically, horizontally, and column-wise is illustrated using np.vstack, np.hstack, and np.columnstack methods. Illustration of stacking arrays vertically, horizontally, and column-wise using np.vstack, np.hstack, and np.columnstack methods.', 'The example demonstrates finding the common elements between two NumPy arrays using np.intersect1d and obtaining the unique elements in one array using np.setdiff1d. Finding common elements between two NumPy arrays using np.intersect1d and obtaining unique elements in one array using np.setdiff1d.']}, {'end': 8893.663, 'start': 8430.871, 'title': 'Numpy array operations', 'summary': 'Covers numpy array addition and axis-wise addition, scalar operations such as addition, multiplication, subtraction, and division, arithmetic operations, working with numpy functions to find mean, median, and standard deviation, as well as saving and loading numpy arrays.', 'duration': 462.792, 'highlights': ['The chapter includes examples of NumPy array addition and axis-wise addition, scalar operations such as addition, multiplication, subtraction, and division, arithmetic operations, working with NumPy functions to find mean, median, and standard deviation, as well as saving and loading NumPy arrays. Covers various operations such as array addition, axis-wise addition, scalar operations, arithmetic operations, NumPy functions (mean, median, standard deviation), and saving/loading NumPy arrays.', 'Demonstrates the process of adding two NumPy arrays using the np.sum function, resulting in a final value of 100, and illustrates the addition of column and row elements using np.sum with the axis parameter, providing specific results for each operation. Illustrates the addition of two NumPy arrays using np.sum, showing the final value of 100, and demonstrates adding column and row elements using np.sum with specific results for each operation.', 'Explains scalar operations such as addition, multiplication, subtraction, and division on a NumPy array, providing examples and results for each operation. Explains scalar operations (addition, multiplication, subtraction, division) on a NumPy array with specific examples and results for each operation.', 'Discusses using NumPy functions to find mean, median, and standard deviation of a NumPy array, providing examples and the calculated values for each function. Discusses the usage of NumPy functions (mean, median, standard deviation) to find values for a NumPy array, including specific examples and calculated values.', 'Describes the process of saving and loading a NumPy array using np.save and np.load methods, demonstrating the saving and loading of a NumPy array with specific file names and extensions. Describes the process of saving and loading a NumPy array using np.save and np.load methods, with specific examples of file names and extensions used.']}], 'duration': 1123.879, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E7769784.jpg', 'highlights': ['Covers various operations such as array addition, axis-wise addition, scalar operations, arithmetic operations, NumPy functions (mean, median, standard deviation), and saving/loading NumPy arrays.', 'Illustration of stacking arrays vertically, horizontally, and column-wise using np.vstack, np.hstack, and np.columnstack methods.', 'Explains scalar operations (addition, multiplication, subtraction, division) on a NumPy array with specific examples and results for each operation.', 'Describes the process of saving and loading a NumPy array using np.save and np.load methods, with specific examples of file names and extensions used.', 'The example demonstrates changing the shape of a NumPy array from 2x3 to 3x2 and then to 4x2, showcasing the ability to alter array dimensions.']}, {'end': 9588.316, 'segs': [{'end': 8958.987, 'src': 'embed', 'start': 8893.683, 'weight': 0, 'content': [{'end': 8899.528, 'text': 'So, we have seen how to do numerical computing with the help of NumPy library.', 'start': 8893.683, 'duration': 5.845}, {'end': 8904.472, 'text': 'Now, we will see how to do data manipulation with the help of Pandas library.', 'start': 8899.548, 'duration': 4.924}, {'end': 8911.197, 'text': 'So, as written here, Pandas stands for panel data and this is a core library for data manipulation and data analysis.', 'start': 8904.492, 'duration': 6.705}, {'end': 8920.645, 'text': 'Just like NumPy used to give us some arrays, Pandas provides us with single dimensional and multi dimensional data structures.', 'start': 8913.499, 'duration': 7.146}, {'end': 8926.869, 'text': 'It can also provide some methods with those data structures so that we can manipulate them.', 'start': 8920.865, 'duration': 6.004}, {'end': 8934.514, 'text': 'In NumPy and Pandas, we have single-dimensional and multi-dimensional data structures.', 'start': 8928.492, 'duration': 6.022}, {'end': 8941.657, 'text': 'We will call a single-dimensional data structure a series object and a multi-dimensional data structure a data frame.', 'start': 8934.554, 'duration': 7.103}, {'end': 8943.398, 'text': 'We will start with a series object.', 'start': 8941.697, 'duration': 1.701}, {'end': 8949.861, 'text': 'So, a series object is a one-dimensional labeled array.', 'start': 8946.539, 'duration': 3.322}, {'end': 8953.184, 'text': 'So, we had understood that a series object is one-dimensional.', 'start': 8950.322, 'duration': 2.862}, {'end': 8958.987, 'text': 'So, what is a labeled array? So, as you can see, I have made a series object here.', 'start': 8953.204, 'duration': 5.783}], 'summary': 'Numpy provides numerical computing, pandas for data manipulation. pandas offers single and multi-dimensional data structures for data analysis.', 'duration': 65.304, 'max_score': 8893.683, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E8893683.jpg'}, {'end': 9093.101, 'src': 'embed', 'start': 9067.864, 'weight': 4, 'content': [{'end': 9073.508, 'text': 'So this was our initial series object, whose index values were all these.', 'start': 9067.864, 'duration': 5.644}, {'end': 9076.03, 'text': 'So we can actually add another attribute, index.', 'start': 9073.968, 'duration': 2.062}, {'end': 9080.032, 'text': 'And with this attribute, we can change the index values.', 'start': 9076.09, 'duration': 3.942}, {'end': 9085.336, 'text': 'As you can see, initially index values were going from 0 to 4.', 'start': 9080.132, 'duration': 5.204}, {'end': 9089.058, 'text': 'But I have changed them to A, B, C, D, E.', 'start': 9085.336, 'duration': 3.722}, {'end': 9093.101, 'text': 'So we had numerical index, I have changed that numerical index to alphabetical index.', 'start': 9089.058, 'duration': 4.043}], 'summary': 'Changed numerical index to alphabetical index.', 'duration': 25.237, 'max_score': 9067.864, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E9067864.jpg'}, {'end': 9179.567, 'src': 'embed', 'start': 9151.371, 'weight': 3, 'content': [{'end': 9158.976, 'text': 'So just use the pd.series method and instead of passing in a list, you will pass a dictionary.', 'start': 9151.371, 'duration': 7.605}, {'end': 9162.258, 'text': 'Here A, 10, B, 20, C, 30.', 'start': 9160.177, 'duration': 2.081}, {'end': 9164.079, 'text': 'So there are 3 key value pairs.', 'start': 9162.258, 'duration': 1.821}, {'end': 9168.601, 'text': 'And all the keys that we have will become your index.', 'start': 9164.139, 'duration': 4.462}, {'end': 9174.464, 'text': 'And all the values will become the values in your series object.', 'start': 9168.681, 'duration': 5.783}, {'end': 9176.465, 'text': 'I will repeat it again.', 'start': 9174.884, 'duration': 1.581}, {'end': 9179.567, 'text': 'As you can see, all the keys will become your index.', 'start': 9176.525, 'duration': 3.042}], 'summary': 'Use pd.series method with a dictionary. a: 10, b: 20, c: 30 become index and values.', 'duration': 28.196, 'max_score': 9151.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E9151371.jpg'}, {'end': 9534.046, 'src': 'embed', 'start': 9502.721, 'weight': 5, 'content': [{'end': 9506.845, 'text': 'So suppose I have to add a scalar value.', 'start': 9502.721, 'duration': 4.124}, {'end': 9510.168, 'text': 'So suppose we take y, so here from 1 to 9 the elements are present.', 'start': 9506.865, 'duration': 3.303}, {'end': 9512.59, 'text': 'So I have to add 5 to all the individual elements.', 'start': 9510.208, 'duration': 2.382}, {'end': 9515.293, 'text': 'So I will just type s1 plus 5 and I have added 5 to all.', 'start': 9512.61, 'duration': 2.683}, {'end': 9527.643, 'text': 'And if I have to add the individual elements of two series objects here, S1 and S2 have been added.', 'start': 9521.619, 'duration': 6.024}, {'end': 9534.046, 'text': 'so I will write S1 plus S2, so these individual values will be added 10 plus 111, 20 plus 222, 30 plus 333,', 'start': 9527.643, 'duration': 6.403}], 'summary': 'Using scalar and series objects to add values in python.', 'duration': 31.325, 'max_score': 9502.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E9502721.jpg'}], 'start': 8893.683, 'title': 'Pandas data manipulation', 'summary': 'Introduces pandas for data manipulation and analysis, showcasing creation and manipulation of series objects and data frames. it also explains creating series objects with a list and a dictionary, changing index values, extracting individual values, and performing arithmetic operations.', 'chapters': [{'end': 9128.431, 'start': 8893.683, 'title': 'Pandas data manipulation', 'summary': 'Introduces pandas as a core library for data manipulation and analysis, showcasing the creation and manipulation of series objects and data frames through the pandas library.', 'duration': 234.748, 'highlights': ['Pandas is a core library for data manipulation and analysis Pandas is described as a core library for data manipulation and analysis.', 'Pandas provides single-dimensional and multi-dimensional data structures Pandas provides both single-dimensional and multi-dimensional data structures for manipulation.', 'Series objects are one-dimensional labeled arrays Series objects are explained as one-dimensional labeled arrays.', 'Creation of series object using Pandas library The process of creating a series object using the Pandas library is demonstrated.', 'Changing index values in series objects Demonstration of changing index values in series objects from numerical to alphabetical values is presented.']}, {'end': 9588.316, 'start': 9128.431, 'title': 'Creating and manipulating series objects', 'summary': 'Explains how to create series objects with a list and a dictionary, change index values, extract individual values and perform arithmetic operations on series objects.', 'duration': 459.885, 'highlights': ['Creating series object with a dictionary Explains the process of creating a series object with a dictionary containing 3 key-value pairs (A: 10, B: 20, C: 30) and the keys becoming the index and values becoming the series object values.', 'Changing index values in a series object Demonstrates how to change the index values in a series object by using the index attribute and repositioning the sequence of keys, resulting in NAN for the newly introduced index without a value.', 'Extracting individual values from a series object Illustrates the process of extracting individual values by specifying the index number or extracting a sequence of elements by defining the range, including extracting elements from the last.', 'Performing arithmetic operations on series objects Shows the addition of a scalar value to all individual elements, addition of individual elements from two series objects, subtraction, multiplication, and division of individual elements.']}], 'duration': 694.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E8893683.jpg', 'highlights': ['Pandas is a core library for data manipulation and analysis', 'Pandas provides single-dimensional and multi-dimensional data structures', 'Series objects are one-dimensional labeled arrays', 'Creating series object with a dictionary containing 3 key-value pairs (A: 10, B: 20, C: 30)', 'Changing index values in series objects from numerical to alphabetical values', 'Performing arithmetic operations on series objects including addition, subtraction, multiplication, and division']}, {'end': 10739.307, 'segs': [{'end': 9630.186, 'src': 'embed', 'start': 9588.336, 'weight': 2, 'content': [{'end': 9591.398, 'text': 'Suppose, I have to divide by 10.', 'start': 9588.336, 'duration': 3.062}, {'end': 9594.34, 'text': 'So, S1 divided by 10 and all these values are here.', 'start': 9591.398, 'duration': 2.942}, {'end': 9602.128, 'text': "So now let's see how we can add two series objects.", 'start': 9596.684, 'duration': 5.444}, {'end': 9604.149, 'text': "So we already have S1, so let's create S2 as well.", 'start': 9602.148, 'duration': 2.001}, {'end': 9605.39, 'text': 'S2 is equal to, so here I will write pd.series.', 'start': 9604.169, 'duration': 1.221}, {'end': 9624.802, 'text': "Then let's assume the values here, I will give 10 to 90.", 'start': 9605.871, 'duration': 18.931}, {'end': 9625.823, 'text': '60, 70, 80 and 90.', 'start': 9624.802, 'duration': 1.021}, {'end': 9628.865, 'text': 'So S1 is done, S2 is done.', 'start': 9625.823, 'duration': 3.042}, {'end': 9630.186, 'text': 'Then I will write S1 plus S2.', 'start': 9628.905, 'duration': 1.281}], 'summary': 'Divide s1 by 10, create s2, and add s1 and s2.', 'duration': 41.85, 'max_score': 9588.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E9588336.jpg'}, {'end': 9715.97, 'src': 'embed', 'start': 9662.749, 'weight': 0, 'content': [{'end': 9666.51, 'text': 'In an Excel sheet, the records or rows are the same.', 'start': 9662.749, 'duration': 3.761}, {'end': 9669.872, 'text': 'And like columns, we also have columns here.', 'start': 9666.731, 'duration': 3.141}, {'end': 9673.135, 'text': 'So this is our two-dimensional label data structure.', 'start': 9670.512, 'duration': 2.623}, {'end': 9678.86, 'text': 'So here we have column names and here we have the values of the column names.', 'start': 9673.575, 'duration': 5.285}, {'end': 9683.125, 'text': 'So the name is Bob, Sam and Ann and the marks are 76, 25, 92.', 'start': 9678.88, 'duration': 4.245}, {'end': 9688.51, 'text': 'And in the corresponding rows, you will also get a value i.e. index 0, 1 and 2.', 'start': 9683.125, 'duration': 5.385}, {'end': 9695.694, 'text': 'So the index of this row is 0, the index of this row is 1 and the index of this row is 2.', 'start': 9688.51, 'duration': 7.184}, {'end': 9698.476, 'text': "So now let's see how to create a data frame.", 'start': 9695.694, 'duration': 2.782}, {'end': 9704.361, 'text': 'First of all, we have to import pandas and then we will use pd.dataFrame.', 'start': 9698.977, 'duration': 5.384}, {'end': 9707.963, 'text': 'And we are creating this data frame with a dictionary.', 'start': 9705.041, 'duration': 2.922}, {'end': 9712.067, 'text': 'So here we are putting the dictionary under this parenthesis.', 'start': 9708.024, 'duration': 4.043}, {'end': 9715.97, 'text': 'First of all, we have two key value pairs.', 'start': 9712.127, 'duration': 3.843}], 'summary': 'Excel sheet contains two-dimensional label data structure with column names and corresponding values like bob, sam, and ann with marks 76, 25, 92.', 'duration': 53.221, 'max_score': 9662.749, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E9662749.jpg'}, {'end': 10399.821, 'src': 'heatmap', 'start': 9739.165, 'weight': 1, 'content': [{'end': 9741.027, 'text': 'So I have to pass a dictionary here.', 'start': 9739.165, 'duration': 1.862}, {'end': 9742.808, 'text': 'So I will write dictionary.', 'start': 9741.047, 'duration': 1.761}, {'end': 9745.59, 'text': 'First key will be my name.', 'start': 9743.568, 'duration': 2.022}, {'end': 9748.712, 'text': 'And some corresponding values will be there.', 'start': 9746.47, 'duration': 2.242}, {'end': 9752.691, 'text': "So let's assume we take that only.", 'start': 9750.029, 'duration': 2.662}, {'end': 9754.173, 'text': 'Annie is done.', 'start': 9753.272, 'duration': 0.901}, {'end': 9756.835, 'text': 'Then Bob is done.', 'start': 9754.913, 'duration': 1.922}, {'end': 9759.477, 'text': 'Then Matt is done.', 'start': 9756.855, 'duration': 2.622}, {'end': 9762.46, 'text': 'So we have created a key value pair.', 'start': 9760.178, 'duration': 2.282}, {'end': 9766.644, 'text': 'So after this I have to create another key value pair.', 'start': 9762.52, 'duration': 4.124}, {'end': 9767.504, 'text': 'After name we give marks.', 'start': 9766.704, 'duration': 0.8}, {'end': 9781.736, 'text': "So I'll add a colon here, so let's assume Annie's score is 75, Bob's score is 12, and Matt's score is 82.", 'start': 9771.988, 'duration': 9.748}, {'end': 9784.558, 'text': "So I've created a data frame.", 'start': 9781.736, 'duration': 2.822}, {'end': 9793.085, 'text': 'Here we have an error, pd.dataFrame, module pandas has..', 'start': 9784.598, 'duration': 8.487}, {'end': 9795.387, 'text': "So let's call it data.frame.", 'start': 9793.085, 'duration': 2.302}, {'end': 9798.409, 'text': "So I'll make it small d.", 'start': 9795.427, 'duration': 2.982}, {'end': 9803.455, 'text': "And I'll remove this.", 'start': 9802.166, 'duration': 1.289}, {'end': 9804.523, 'text': "Let's see.", 'start': 9803.898, 'duration': 0.625}, {'end': 9808.602, 'text': 'Here, both D and F should be capital.', 'start': 9806.201, 'duration': 2.401}, {'end': 9814.746, 'text': 'So, I will make it capital D and I will make it capital F.', 'start': 9808.622, 'duration': 6.124}, {'end': 9817.948, 'text': 'Right And we have successfully made a data frame like this.', 'start': 9814.746, 'duration': 3.202}, {'end': 9823.811, 'text': 'PD.DataFrame And here, as you can see, the key will be the name of the column.', 'start': 9818.008, 'duration': 5.803}, {'end': 9827.733, 'text': 'Name and marks, which is the key in this dictionary, they have become the names of the columns.', 'start': 9823.871, 'duration': 3.862}, {'end': 9831.736, 'text': 'And the values of the names will become the records of this column.', 'start': 9828.194, 'duration': 3.542}, {'end': 9836.203, 'text': 'And the values of the marks are the records of the column.', 'start': 9831.836, 'duration': 4.367}, {'end': 9838.752, 'text': 'Again, you have the index values with you.', 'start': 9836.283, 'duration': 2.469}, {'end': 9841.418, 'text': 'So now we will work with some built-in functions.', 'start': 9839.718, 'duration': 1.7}, {'end': 9846.079, 'text': 'You have shape, tail, describe, and head.', 'start': 9841.518, 'duration': 4.561}, {'end': 9851.761, 'text': 'So if you want to see the first five rows of a data frame, you can use the head method.', 'start': 9846.259, 'duration': 5.502}, {'end': 9854.961, 'text': 'If you want to see the last five rows, you can use the tail method.', 'start': 9851.781, 'duration': 3.18}, {'end': 9861.443, 'text': 'If you want to know the number of rows and columns in a data frame, you can use the shape method.', 'start': 9854.981, 'duration': 6.462}, {'end': 9865.004, 'text': 'And if you want general information about a data set, you can use the describe method.', 'start': 9861.463, 'duration': 3.541}, {'end': 9872.754, 'text': 'So for this I will actually load a data set and it is actually in CSV format.', 'start': 9866.732, 'duration': 6.022}, {'end': 9877.696, 'text': 'So I will store it in a new data frame whose name will be iris.', 'start': 9873.074, 'duration': 4.622}, {'end': 9884.898, 'text': 'So iris equal to I will write pd dot read underscore CSV.', 'start': 9878.296, 'duration': 6.602}, {'end': 9889.78, 'text': 'So if I want to read a CSV file then I can use this read CSV method.', 'start': 9885.418, 'duration': 4.362}, {'end': 9896.345, 'text': 'And this CSV file is already stored with me, so I will write iris.csv here.', 'start': 9891.48, 'duration': 4.865}, {'end': 9900.989, 'text': 'To work with all these functions, we will load the iris dataset.', 'start': 9896.385, 'duration': 4.604}, {'end': 9907.334, 'text': 'So if we want to load a dataset, we will have to use the pd.readCSV method.', 'start': 9901.029, 'duration': 6.305}, {'end': 9909.156, 'text': 'So I will write pd.readCSV here.', 'start': 9907.354, 'duration': 1.802}, {'end': 9915.722, 'text': 'Since this is a CSV file, we are using the readCSV method.', 'start': 9912.819, 'duration': 2.903}, {'end': 9918.824, 'text': 'The name of the file is iris.csv.', 'start': 9915.982, 'duration': 2.842}, {'end': 9925.47, 'text': 'I will store it in a new object and write its name iris.', 'start': 9918.904, 'duration': 6.566}, {'end': 9928.933, 'text': 'My dataset is ready.', 'start': 9926.331, 'duration': 2.602}, {'end': 9937.861, 'text': 'If I want to see the first five records of this dataset, I will write iris first, then I will use the dot operator to write the head method here.', 'start': 9928.993, 'duration': 8.868}, {'end': 9941.523, 'text': 'As you can see, I have printed the first 5 records from the dataset.', 'start': 9939.062, 'duration': 2.461}, {'end': 9946.926, 'text': 'Similarly, if I want the last 5 records, I will use the TAIL method.', 'start': 9941.543, 'duration': 5.383}, {'end': 9949.067, 'text': 'I will write iris.tail.', 'start': 9947.006, 'duration': 2.061}, {'end': 9954.69, 'text': 'As you can see, the last 5 records have been printed.', 'start': 9949.347, 'duration': 5.343}, {'end': 9955.851, 'text': 'Starting from 145 going on till 149.', 'start': 9954.71, 'duration': 1.141}, {'end': 9958.832, 'text': 'The last 5 records have been printed from the dataset.', 'start': 9955.851, 'duration': 2.981}, {'end': 9966.937, 'text': 'If I want to see how many rows and columns are there in this data frame, So I will use the shape method.', 'start': 9958.952, 'duration': 7.985}, {'end': 9969.939, 'text': 'So here I will write iris.shape.', 'start': 9967.097, 'duration': 2.842}, {'end': 9974.001, 'text': 'So you can see it has 150 rows and 5 columns.', 'start': 9970.339, 'duration': 3.662}, {'end': 9977.023, 'text': 'So you can see the columns 1, 2, 3, 4, 5.', 'start': 9974.321, 'duration': 2.702}, {'end': 9981.786, 'text': 'And in total 150 records starting from index number 0 going on till index number 149.', 'start': 9977.023, 'duration': 4.763}, {'end': 9984.688, 'text': "So now let's see the describe method.", 'start': 9981.786, 'duration': 2.902}, {'end': 9992.835, 'text': 'iris.describe So, this will tell us some basic things about this.', 'start': 9984.788, 'duration': 8.047}, {'end': 9999.822, 'text': 'So, if I want the mean of every individual column, minimum, standard, deviation, etc.', 'start': 9992.875, 'duration': 6.947}, {'end': 10004.175, 'text': 'So, for example, I need the mean of sepal width.', 'start': 10001.834, 'duration': 2.341}, {'end': 10011.318, 'text': 'Meaning, in this column, whatever values are there, I need the mean of all those values.', 'start': 10004.355, 'duration': 6.963}, {'end': 10012.899, 'text': 'So, it will tell me that this is 3.05.', 'start': 10011.338, 'duration': 1.561}, {'end': 10019.462, 'text': 'Similarly, if I want the minimum value of petal length, then you can see that minimum petal length is 1.', 'start': 10012.899, 'duration': 6.563}, {'end': 10022.824, 'text': 'Similarly, I want the maximum value of sepal length.', 'start': 10019.462, 'duration': 3.362}, {'end': 10025.405, 'text': 'So, here, look at sepal length, the maximum value is 7.9.', 'start': 10022.844, 'duration': 2.561}, {'end': 10029.247, 'text': 'So, all this basic information will give you the describe method here.', 'start': 10025.405, 'duration': 3.842}, {'end': 10035.286, 'text': 'So now we will see what iLock and Lock methods are.', 'start': 10031.104, 'duration': 4.182}, {'end': 10037.407, 'text': 'So now we have a data set.', 'start': 10035.786, 'duration': 1.621}, {'end': 10041.049, 'text': 'from that data set we have to extract some values, some records.', 'start': 10037.407, 'duration': 3.642}, {'end': 10042.349, 'text': 'how can we do that?', 'start': 10041.049, 'duration': 1.3}, {'end': 10045.711, 'text': 'So for that we have this ilock method.', 'start': 10042.389, 'duration': 3.322}, {'end': 10047.632, 'text': 'So this is our data frame.', 'start': 10045.791, 'duration': 1.841}, {'end': 10055.536, 'text': 'So from this data frame, if I have to extract the first 3 rows and the first 2 columns, then I will do this.', 'start': 10047.652, 'duration': 7.884}, {'end': 10060.577, 'text': 'So iris, which is the name of the data frame, after that dot ilog.', 'start': 10056.576, 'duration': 4.001}, {'end': 10062.797, 'text': 'So this is the ilog method, then I will give a parenthesis.', 'start': 10060.597, 'duration': 2.2}, {'end': 10068.818, 'text': 'The comma here, whatever you give on the left side of the comma will indicate the rows.', 'start': 10062.837, 'duration': 5.981}, {'end': 10074.019, 'text': 'Whatever values you give on the right side of the comma will indicate the columns.', 'start': 10068.858, 'duration': 5.161}, {'end': 10077.66, 'text': 'So I am extracting the first three rows here from 0 to 3.', 'start': 10074.059, 'duration': 3.601}, {'end': 10080.06, 'text': 'So here I have extracted the first three rows.', 'start': 10077.66, 'duration': 2.4}, {'end': 10085.701, 'text': 'And here I am extracting the first two columns, sepal length and sepal width.', 'start': 10080.12, 'duration': 5.581}, {'end': 10089.323, 'text': "So, let's implement this example and I'll print its head again.", 'start': 10086.561, 'duration': 2.762}, {'end': 10089.804, 'text': 'This is my head.', 'start': 10089.343, 'duration': 0.461}, {'end': 10095.748, 'text': "So, let's say I want to bring records from 5 to 10 and I want this Petal Length and Species column here.", 'start': 10089.824, 'duration': 5.924}, {'end': 10110.319, 'text': 'So I will write iris.ilog.', 'start': 10107.458, 'duration': 2.861}, {'end': 10117.143, 'text': 'The index value will start from 5 and I want the index value to be 11.', 'start': 10110.339, 'duration': 6.804}, {'end': 10119.184, 'text': 'So I will write 10.', 'start': 10117.143, 'duration': 2.041}, {'end': 10122.004, 'text': 'And which columns do I want here? 0, 1, 2.', 'start': 10119.184, 'duration': 2.82}, {'end': 10124.567, 'text': "Petal length's index value is 2.", 'start': 10122.005, 'duration': 2.562}, {'end': 10127.488, 'text': 'And I want species whose index value is 4.', 'start': 10124.567, 'duration': 2.921}, {'end': 10130.85, 'text': 'So I will write this and print it here.', 'start': 10127.488, 'duration': 3.362}, {'end': 10133.011, 'text': 'So too many indexers.', 'start': 10131.47, 'duration': 1.541}, {'end': 10134.492, 'text': 'So here we will..', 'start': 10133.591, 'duration': 0.901}, {'end': 10142.714, 'text': "So let's assume that I want all the records from this data frame starting at index number 5 going on till index number 10.", 'start': 10135.572, 'duration': 7.142}, {'end': 10147.196, 'text': "And let's assume that I want to extract the Petal length, Petal width and species column.", 'start': 10142.714, 'duration': 4.482}, {'end': 10150.837, 'text': 'So I will write iris.iloc and then I will first give the rows.', 'start': 10147.276, 'duration': 3.561}, {'end': 10152.617, 'text': 'I want the rows.', 'start': 10150.857, 'duration': 1.76}, {'end': 10164.622, 'text': 'from 5 to 11 because 11 is exclusive and I need petal length, petal width and species.', 'start': 10158.217, 'duration': 6.405}, {'end': 10168.163, 'text': 'So, I need 0, 1, 2.', 'start': 10165.023, 'duration': 3.14}, {'end': 10172.369, 'text': 'So, I need all the columns from 2 to 10.', 'start': 10168.165, 'duration': 4.204}, {'end': 10182.247, 'text': 'So, I have extracted petal length, petal width and species columns and I have extracted index values or row values from 5 to 10.', 'start': 10172.369, 'duration': 9.878}, {'end': 10185.709, 'text': 'So, we also have dot lock method like dot ilock.', 'start': 10182.247, 'duration': 3.462}, {'end': 10191.571, 'text': 'So, the only difference is when we are giving columns, we used to give the index of the column, here we will give the names of the columns.', 'start': 10185.749, 'duration': 5.822}, {'end': 10194.833, 'text': 'So, here I have to extract sepal length and petal length.', 'start': 10191.591, 'duration': 3.242}, {'end': 10199.855, 'text': 'So, I will just give sepal length and petal length as the names of the columns and I have extracted it.', 'start': 10194.913, 'duration': 4.942}, {'end': 10202.436, 'text': 'And it is always 0 to 3.', 'start': 10200.275, 'duration': 2.161}, {'end': 10204.597, 'text': 'And the difference here is that 3 is inclusive.', 'start': 10202.436, 'duration': 2.161}, {'end': 10209.099, 'text': 'So, there is a difference between dot ilock and dot lock because you see the index of ilock.', 'start': 10204.637, 'duration': 4.462}, {'end': 10214.686, 'text': 'And here 0 to 3 means both 0 and 3 are inclusive.', 'start': 10210.9, 'duration': 3.786}, {'end': 10222.317, 'text': 'So here you are getting 4 records and basically you are getting the first 4 records of these two columns.', 'start': 10214.706, 'duration': 7.611}, {'end': 10226.799, 'text': 'So now we will work with the dot lock method.', 'start': 10223.697, 'duration': 3.102}, {'end': 10229.72, 'text': 'So I will write iris.lock here.', 'start': 10227.319, 'duration': 2.401}, {'end': 10236.944, 'text': 'So now assume I want all the records starting from index number 1 going on till index number 10.', 'start': 10229.74, 'duration': 7.204}, {'end': 10240.386, 'text': 'So 1 is 2, I will write 10 here.', 'start': 10236.944, 'duration': 3.442}, {'end': 10243.388, 'text': 'After this I need two columns.', 'start': 10240.486, 'duration': 2.902}, {'end': 10247.17, 'text': 'The first column is petal length and the second column is species.', 'start': 10243.408, 'duration': 3.762}, {'end': 10251.533, 'text': 'So I will write petal.length here.', 'start': 10247.19, 'duration': 4.343}, {'end': 10254.578, 'text': 'It should actually be capital here.', 'start': 10252.637, 'duration': 1.941}, {'end': 10257.54, 'text': 'Then my second column should be species.', 'start': 10254.738, 'duration': 2.802}, {'end': 10259.882, 'text': 'So I will write species here.', 'start': 10257.6, 'duration': 2.282}, {'end': 10267.927, 'text': 'So as you can see here, I have extracted these 10 records for the columns petal length and species.', 'start': 10259.902, 'duration': 8.025}, {'end': 10275.02, 'text': "So let's see how we can remove or drop a column.", 'start': 10269.036, 'duration': 5.984}, {'end': 10277.941, 'text': 'Axis is equal to 1 means it is denoting a column.', 'start': 10275.16, 'duration': 2.781}, {'end': 10282.924, 'text': 'So we have 5 columns and I have to drop a sepal length column.', 'start': 10277.981, 'duration': 4.943}, {'end': 10284.986, 'text': 'So you will use the drop method.', 'start': 10282.944, 'duration': 2.042}, {'end': 10288.408, 'text': 'First you will write the name of the data frame which is iris.', 'start': 10285.006, 'duration': 3.402}, {'end': 10293.192, 'text': 'So iris.drop The first parameter is the column you want to drop.', 'start': 10288.788, 'duration': 4.404}, {'end': 10296.175, 'text': 'The second parameter is the axis.', 'start': 10293.212, 'duration': 2.963}, {'end': 10298.237, 'text': 'If you write axis is equal to 0, you will drop the rows.', 'start': 10296.215, 'duration': 2.022}, {'end': 10301.1, 'text': 'If you write axis is equal to 1, you will drop the columns.', 'start': 10298.317, 'duration': 2.783}, {'end': 10308.372, 'text': 'So I will print iris.head here again because it will be easier for you to understand.', 'start': 10302.588, 'duration': 5.784}, {'end': 10313.896, 'text': 'So this is the head and now I have to drop the species column.', 'start': 10308.392, 'duration': 5.504}, {'end': 10317.618, 'text': 'So I will write iris.drop here.', 'start': 10313.976, 'duration': 3.642}, {'end': 10321.381, 'text': 'The first parameter will be the column that I want to drop.', 'start': 10317.658, 'duration': 3.723}, {'end': 10329.966, 'text': 'So here I will write species and here I will set the axis which is 1 because I have to drop the columns.', 'start': 10321.421, 'duration': 8.545}, {'end': 10333.698, 'text': 'So, as you can see, I have dropped the species column.', 'start': 10331.596, 'duration': 2.102}, {'end': 10338.421, 'text': "And now let's see how to drop the rows.", 'start': 10334.198, 'duration': 4.223}, {'end': 10343.365, 'text': "So, let's assume that I have to drop all these index rows 1, 2 and 3.", 'start': 10338.441, 'duration': 4.924}, {'end': 10346.907, 'text': 'So, just write the index values here and then set the axis equal to 0.', 'start': 10343.365, 'duration': 3.542}, {'end': 10349.65, 'text': 'Axis equal to 0 means that the rows are done.', 'start': 10346.907, 'duration': 2.743}, {'end': 10351.571, 'text': 'So, this was our original data set.', 'start': 10349.81, 'duration': 1.761}, {'end': 10353.572, 'text': 'So, I have dropped all these index values.', 'start': 10351.591, 'duration': 1.981}, {'end': 10355.314, 'text': 'So, you can see that 1, 2, 3 are not here.', 'start': 10353.632, 'duration': 1.682}, {'end': 10358.558, 'text': 'It starts directly from 4 after 0.', 'start': 10355.374, 'duration': 3.184}, {'end': 10361.019, 'text': 'So again I will write iris.head here.', 'start': 10358.558, 'duration': 2.461}, {'end': 10362.34, 'text': 'This is my data frame.', 'start': 10361.139, 'duration': 1.201}, {'end': 10364.721, 'text': 'Then here I will write iris.drop.', 'start': 10362.5, 'duration': 2.221}, {'end': 10368.443, 'text': 'Then suppose I have to drop 5th, 6th and 7th rows here.', 'start': 10364.761, 'duration': 3.682}, {'end': 10372.385, 'text': 'So I will do iris.drop Then I will set axis as equal to 0 here.', 'start': 10368.463, 'duration': 3.922}, {'end': 10399.821, 'text': 'So I will actually I will store this in a new data frame whose name will be I1, then I1.head, I will show you the first 10 records of this.', 'start': 10372.425, 'duration': 27.396}], 'summary': 'The transcript covers creating, loading, and manipulating a data frame using pandas, including methods like head, tail, shape, describe, iloc, loc, drop for data extraction and manipulation.', 'duration': 660.656, 'max_score': 9739.165, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E9739165.jpg'}, {'end': 9896.345, 'src': 'embed', 'start': 9866.732, 'weight': 4, 'content': [{'end': 9872.754, 'text': 'So for this I will actually load a data set and it is actually in CSV format.', 'start': 9866.732, 'duration': 6.022}, {'end': 9877.696, 'text': 'So I will store it in a new data frame whose name will be iris.', 'start': 9873.074, 'duration': 4.622}, {'end': 9884.898, 'text': 'So iris equal to I will write pd dot read underscore CSV.', 'start': 9878.296, 'duration': 6.602}, {'end': 9889.78, 'text': 'So if I want to read a CSV file then I can use this read CSV method.', 'start': 9885.418, 'duration': 4.362}, {'end': 9896.345, 'text': 'And this CSV file is already stored with me, so I will write iris.csv here.', 'start': 9891.48, 'duration': 4.865}], 'summary': 'Loading a csv dataset named iris using pd.read_csv method.', 'duration': 29.613, 'max_score': 9866.732, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E9866732.jpg'}, {'end': 10590.064, 'src': 'embed', 'start': 10562.116, 'weight': 1, 'content': [{'end': 10565.399, 'text': 'So, now I am making a new user defined method here.', 'start': 10562.116, 'duration': 3.283}, {'end': 10568.223, 'text': 'So, instead of half, suppose I have to double this.', 'start': 10565.419, 'duration': 2.804}, {'end': 10569.704, 'text': 'So, I will write this.', 'start': 10568.263, 'duration': 1.441}, {'end': 10579.779, 'text': 'double make and it will take a parameter and it will return S into 2.', 'start': 10571.815, 'duration': 7.964}, {'end': 10582.56, 'text': 'So, I have created a user defined method.', 'start': 10579.779, 'duration': 2.781}, {'end': 10585.061, 'text': 'Then I have an iris data frame.', 'start': 10582.58, 'duration': 2.481}, {'end': 10590.064, 'text': 'From this iris data frame, suppose I want to double the values of sepal width and petal width.', 'start': 10585.081, 'duration': 4.983}], 'summary': 'Creating a user-defined method to double values in an iris dataframe.', 'duration': 27.948, 'max_score': 10562.116, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E10562116.jpg'}], 'start': 9588.336, 'title': 'Pandas series and data frames', 'summary': 'Covers creating and manipulating series and data frames in pandas, including adding series objects, creating data frames with a dictionary, and using built-in functions like head, tail, shape, and describe to analyze a data set. it also explains data frame extraction, manipulation, and basic functions like mean, median, and maximum, and methods for categorical data and sorting columns.', 'chapters': [{'end': 10025.405, 'start': 9588.336, 'title': 'Working with series and data frames in pandas', 'summary': 'Discusses creating and manipulating series and data frames in pandas, including adding series objects, creating a data frame with a dictionary, and using built-in functions like head, tail, shape, and describe to analyze a data set, with a focus on the iris dataset.', 'duration': 437.069, 'highlights': ["Creating a data frame with a dictionary The speaker demonstrates creating a data frame in pandas using a dictionary with key-value pairs for 'name' and 'marks', such as 'Bob': 12, 'Annie': 75, and 'Matt': 82.", 'Using built-in functions to analyze a data set The chapter covers the use of built-in functions like head, tail, shape, and describe to analyze a data set, with the example of the iris dataset having 150 rows and 5 columns, and providing statistical information like mean, minimum, and maximum values for specific columns.', 'Adding series objects The speaker demonstrates adding individual elements of two series objects, S1 and S2, in pandas.']}, {'end': 10739.307, 'start': 10025.405, 'title': 'Data frame extraction and manipulation', 'summary': 'Explains how to extract specific rows and columns from a data frame using iloc and loc methods, apply basic functions like mean, median, and maximum to the dataset, and manipulate data using the apply method. it also demonstrates the usage of value count and sort values methods for categorical data and sorting columns.', 'duration': 713.902, 'highlights': ['The chapter explains how to extract specific rows and columns from a data frame using iloc and loc methods. Demonstrates the usage of iloc and loc methods for extracting specific rows and columns from a data frame.', 'Apply basic functions like mean, median, and maximum to the dataset. Shows examples of applying basic functions like mean, median, and maximum to the dataset for statistical analysis.', 'Demonstrates the usage of value count and sort values methods for categorical data and sorting columns. Illustrates the usage of value count and sort values methods for categorical data and sorting columns in the dataset.']}], 'duration': 1150.971, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E9588336.jpg', 'highlights': ["Creating a data frame with a dictionary using key-value pairs for 'name' and 'marks'", 'Using built-in functions like head, tail, shape, and describe to analyze a data set', 'Adding series objects by demonstrating the addition of individual elements of two series objects', 'Explaining the extraction of specific rows and columns from a data frame using iloc and loc methods', 'Applying basic functions like mean, median, and maximum to the dataset for statistical analysis', 'Illustrating the usage of value count and sort values methods for categorical data and sorting columns in the dataset']}, {'end': 12812.73, 'segs': [{'end': 11177.946, 'src': 'heatmap', 'start': 11017.06, 'weight': 0.701, 'content': [{'end': 11022.442, 'text': 'Similarly, if I want to increase or decrease the line width, I can use the line width attribute.', 'start': 11017.06, 'duration': 5.382}, {'end': 11028.665, 'text': 'And here I have set the line width to 2 and then I have shown it.', 'start': 11022.482, 'duration': 6.183}, {'end': 11031.466, 'text': "So, let's implement all these things here.", 'start': 11028.685, 'duration': 2.781}, {'end': 11033.607, 'text': 'So, I can actually do it in this thing here.', 'start': 11031.486, 'duration': 2.121}, {'end': 11041.29, 'text': 'plt.plot, plt.show Then I have to add the title here, t-i-t-l-e and I will set the title here.', 'start': 11033.907, 'duration': 7.383}, {'end': 11046.764, 'text': 'x versus y.', 'start': 11045.203, 'duration': 1.561}, {'end': 11050.728, 'text': 'Then I will give x-axis label here.', 'start': 11046.764, 'duration': 3.964}, {'end': 11055.952, 'text': 'plt.xlabel and xlabel will be here.', 'start': 11050.728, 'duration': 5.224}, {'end': 11061.176, 'text': 'this is x-axis and I will have to set y-axis as well.', 'start': 11055.952, 'duration': 5.224}, {'end': 11072.043, 'text': 'plt.ylabel, and here I will write this is y-axis.', 'start': 11061.176, 'duration': 10.867}, {'end': 11074.325, 'text': 'So I have set x-axis and y-axis.', 'start': 11072.664, 'duration': 1.661}, {'end': 11079.347, 'text': 'Then in plt.plot, x and y have come here.', 'start': 11074.425, 'duration': 4.922}, {'end': 11083.93, 'text': 'Then I have to change line style, line width and line color.', 'start': 11079.387, 'duration': 4.543}, {'end': 11085.21, 'text': 'So I am assigning green color here.', 'start': 11083.95, 'duration': 1.26}, {'end': 11092.307, 'text': 'Then I have to I have to check the line style.', 'start': 11085.811, 'duration': 6.496}, {'end': 11095.268, 'text': 'The line style will be a colon here.', 'start': 11092.347, 'duration': 2.921}, {'end': 11098.21, 'text': 'Then I have to set the line width.', 'start': 11095.288, 'duration': 2.922}, {'end': 11100.871, 'text': 'I will give the line width as 5 here.', 'start': 11098.23, 'duration': 2.641}, {'end': 11106.074, 'text': 'So, I am setting the line width as 5 here.', 'start': 11100.911, 'duration': 5.163}, {'end': 11115.318, 'text': 'Right So, I have added the x-axis label, y-axis label, title and I have also changed the color, style and width here.', 'start': 11106.814, 'duration': 8.504}, {'end': 11120.999, 'text': 'So now we will see how we can do two lines in one plot.', 'start': 11117.756, 'duration': 3.243}, {'end': 11122.781, 'text': 'So again this numpy array is going from 1 to 10.', 'start': 11121.019, 'duration': 1.762}, {'end': 11124.683, 'text': 'So after this I am creating two more numpy arrays y1 and y2.', 'start': 11122.781, 'duration': 1.902}, {'end': 11126.765, 'text': 'So y1 is 2 times of x and y2 is 3 times of x.', 'start': 11124.903, 'duration': 1.862}, {'end': 11140.998, 'text': 'Then after this, I am preparing two plots here.', 'start': 11136.334, 'duration': 4.664}, {'end': 11146.742, 'text': 'So plt.plot, x will remain as x only, y1 will come to the y-axis of this plot.', 'start': 11141.218, 'duration': 5.524}, {'end': 11150.925, 'text': 'y2 will come to the y-axis of the other plot.', 'start': 11146.842, 'duration': 4.083}, {'end': 11153.948, 'text': 'Then the color of this plot is green and this plot is red.', 'start': 11151.426, 'duration': 2.522}, {'end': 11156.73, 'text': 'I have given line style and then I have given line width here.', 'start': 11153.968, 'duration': 2.762}, {'end': 11159.192, 'text': 'Then I am also adding its grid which is true.', 'start': 11156.77, 'duration': 2.422}, {'end': 11164.496, 'text': 'And if we implement all these codes, we will get a graph like this.', 'start': 11161.053, 'duration': 3.443}, {'end': 11170.64, 'text': 'This code may look a bit complicated, so I will implement it in the Jupyter Notebook and show you piece by piece.', 'start': 11164.956, 'duration': 5.684}, {'end': 11174.723, 'text': 'So we have x already present.', 'start': 11170.78, 'duration': 3.943}, {'end': 11177.946, 'text': 'Then I will set y1 here, y1 will be 2 times of x.', 'start': 11174.743, 'duration': 3.203}], 'summary': 'Demonstrates setting line attributes and plotting two lines with different styles and colors.', 'duration': 160.886, 'max_score': 11017.06, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E11017060.jpg'}, {'end': 11501.423, 'src': 'heatmap', 'start': 11335.559, 'weight': 0.756, 'content': [{'end': 11338.081, 'text': "So now let's see how we can add subplots.", 'start': 11335.559, 'duration': 2.522}, {'end': 11344.945, 'text': 'Subplots means we will make multiple plots in one screen area.', 'start': 11338.421, 'duration': 6.524}, {'end': 11349.448, 'text': 'The previous one had two lines in one graph.', 'start': 11345.366, 'duration': 4.082}, {'end': 11354.992, 'text': 'So the difference is that we will have two different plots but we will see these two plots in one screen space.', 'start': 11349.468, 'duration': 5.524}, {'end': 11361.137, 'text': 'So again we have initialized x, y1 and y2.', 'start': 11356.693, 'duration': 4.444}, {'end': 11364.3, 'text': 'After this we will use the subplot method.', 'start': 11361.157, 'duration': 3.143}, {'end': 11367.823, 'text': 'We are writing plt.subplot with three parameters.', 'start': 11364.32, 'duration': 3.503}, {'end': 11370.345, 'text': 'So it is indicating one row and two columns.', 'start': 11368.344, 'duration': 2.001}, {'end': 11375.869, 'text': 'So basically there will be two subplots.', 'start': 11374.389, 'duration': 1.48}, {'end': 11381.591, 'text': 'So one subplot will come in the first column and the second subplot will come in the second column.', 'start': 11375.909, 'duration': 5.682}, {'end': 11385.211, 'text': 'And the third parameter is the index of the subplot.', 'start': 11381.671, 'duration': 3.54}, {'end': 11387.972, 'text': 'So this is my first subplot which is here.', 'start': 11385.272, 'duration': 2.7}, {'end': 11391.353, 'text': 'And again this is my second subplot.', 'start': 11388.212, 'duration': 3.141}, {'end': 11396.754, 'text': 'So the first subplot is between x and y1 and the second subplot is between x and y2.', 'start': 11391.373, 'duration': 5.381}, {'end': 11399.475, 'text': 'Then we will show plt.show and this will be our result.', 'start': 11396.794, 'duration': 2.681}, {'end': 11402.803, 'text': 'So we will copy paste this.', 'start': 11400.902, 'duration': 1.901}, {'end': 11407.667, 'text': 'So we have x, y1 and y2 everything is ready here.', 'start': 11402.843, 'duration': 4.824}, {'end': 11413.031, 'text': 'Then I will write here plt.subplot.', 'start': 11408.187, 'duration': 4.844}, {'end': 11415.292, 'text': 'So I actually need this row wise.', 'start': 11413.491, 'duration': 1.801}, {'end': 11419.295, 'text': 'So I need two rows and one column and this will be the first subplot.', 'start': 11415.332, 'duration': 3.963}, {'end': 11423.978, 'text': 'And in the first subplot I will take this here.', 'start': 11419.475, 'duration': 4.503}, {'end': 11426.66, 'text': 'And then I need the second subplot.', 'start': 11425.079, 'duration': 1.581}, {'end': 11431.166, 'text': 'I will copy and paste it here.', 'start': 11428.884, 'duration': 2.282}, {'end': 11434.447, 'text': 'And it will be 2, 2, 1.', 'start': 11432.827, 'duration': 1.62}, {'end': 11440.21, 'text': 'It will actually be 2, 1, 1, 2.', 'start': 11434.448, 'duration': 5.762}, {'end': 11442.533, 'text': 'Sorry Then I will copy the other plot here.', 'start': 11440.211, 'duration': 2.322}, {'end': 11445.755, 'text': 'I am copying and pasting it here.', 'start': 11442.553, 'duration': 3.202}, {'end': 11448.336, 'text': 'These are my two plots.', 'start': 11445.795, 'duration': 2.541}, {'end': 11452.219, 'text': 'Again, I will take all these things and add them here.', 'start': 11448.676, 'duration': 3.543}, {'end': 11457.934, 'text': 'Right Then it enters.', 'start': 11452.259, 'duration': 5.675}, {'end': 11462.715, 'text': 'As you can see, x-axis, y-axis and these two are there and this is the title here.', 'start': 11458.334, 'duration': 4.381}, {'end': 11470.599, 'text': 'And we have got this for the same thing because all of this is included in the second subplot.', 'start': 11462.735, 'duration': 7.864}, {'end': 11476.701, 'text': 'Title, x-label and y-label are coming under the second subplot.', 'start': 11470.639, 'duration': 6.062}, {'end': 11480.923, 'text': "That's why these three things are here under the second subplot.", 'start': 11476.721, 'duration': 4.202}, {'end': 11483.624, 'text': 'So this is how you can create subplots.', 'start': 11481.663, 'duration': 1.961}, {'end': 11488.499, 'text': "So this was our line plot, now let's see how we can work with a bar plot.", 'start': 11484.718, 'duration': 3.781}, {'end': 11495.181, 'text': 'We normally see a bar plot when we have to understand the distribution of a categorical value.', 'start': 11488.519, 'duration': 6.662}, {'end': 11501.423, 'text': 'So here we are making a dictionary, so in this dictionary we have three key values.', 'start': 11495.241, 'duration': 6.182}], 'summary': 'The tutorial explains how to create subplots and work with a bar plot in python, using two different plots in one screen area and a dictionary with three key values.', 'duration': 165.864, 'max_score': 11335.559, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E11335559.jpg'}, {'end': 12144.078, 'src': 'heatmap', 'start': 11979.372, 'weight': 0.755, 'content': [{'end': 11982.173, 'text': "Right, so I've randomly given 10 values here.", 'start': 11979.372, 'duration': 2.801}, {'end': 11987.115, 'text': 'Then this is my first scatterplot, which was between X and Y.', 'start': 11982.213, 'duration': 4.902}, {'end': 11990.776, 'text': "So I'll give another scatterplot here, which will be between X and Z.", 'start': 11987.115, 'duration': 3.661}, {'end': 11997.99, 'text': 'And its marker style will be normal here.', 'start': 11994.367, 'duration': 3.623}, {'end': 12003.214, 'text': 'And then we give it color as green and set its size as 200.', 'start': 11999.351, 'duration': 3.863}, {'end': 12005.476, 'text': 'Right So we have added two markers to the same plot.', 'start': 12003.214, 'duration': 2.262}, {'end': 12014.754, 'text': 'So now it adds its subplots.', 'start': 12013.353, 'duration': 1.401}, {'end': 12018.718, 'text': 'So again the same thing, we have x, a and b.', 'start': 12014.774, 'duration': 3.944}, {'end': 12021.86, 'text': 'So instead of directly using scatter, we will use the subplot method.', 'start': 12018.718, 'duration': 3.142}, {'end': 12025.123, 'text': 'Here again we are arranging it in two columns.', 'start': 12021.88, 'duration': 3.243}, {'end': 12026.844, 'text': 'So 1, 2, 1 and again 1, 2, 2.', 'start': 12025.323, 'duration': 1.521}, {'end': 12032.429, 'text': 'So the first subplot is between x and a and the second subplot is between x and b.', 'start': 12026.845, 'duration': 5.584}, {'end': 12040.221, 'text': 'So, I write plt.subplot here.', 'start': 12036.36, 'duration': 3.861}, {'end': 12045.783, 'text': 'So, this will be 1, 2 and then it will be between 1.', 'start': 12040.281, 'duration': 5.502}, {'end': 12050.184, 'text': 'Then I have to copy it here.', 'start': 12045.783, 'duration': 4.401}, {'end': 12052.505, 'text': 'I copy it and paste it here.', 'start': 12050.264, 'duration': 2.241}, {'end': 12062.899, 'text': 'Then I will make another subplot, plt.subplot, it will be 1, 2, 2, means it will come in the second column.', 'start': 12053.171, 'duration': 9.728}, {'end': 12068.264, 'text': 'I will copy it again and I will paste it here.', 'start': 12062.919, 'duration': 5.345}, {'end': 12076.811, 'text': 'Then I will have to print it, so I just have to show plt.show here and I have made a subplot.', 'start': 12068.344, 'duration': 8.467}, {'end': 12080.782, 'text': "So that was Scatterplot, now let's see how we can work with Histogram.", 'start': 12078.22, 'duration': 2.562}, {'end': 12085.305, 'text': "So first let's understand the difference between Histogram and Bar Chart.", 'start': 12080.802, 'duration': 4.503}, {'end': 12089.728, 'text': 'So we can normally use Bar Chart for categorical values.', 'start': 12085.325, 'duration': 4.403}, {'end': 12094.912, 'text': 'And when it comes to Histogram, we can use Histogram for numerical values.', 'start': 12089.828, 'duration': 5.084}, {'end': 12103.402, 'text': 'So here I have taken a random distribution, a random list which has these numbers, and if I want to make a histogram,', 'start': 12096.653, 'duration': 6.749}, {'end': 12108.149, 'text': 'then just do plt.hist and pass this data, and this is my histogram.', 'start': 12103.402, 'duration': 4.747}, {'end': 12113.954, 'text': 'And here we are changing the colors, initially it was blue and now it is green.', 'start': 12109.91, 'duration': 4.044}, {'end': 12122.141, 'text': 'So here you can see that there are a lot of bins and if I want to reduce the number of bins, then I am doing bins as equal to force.', 'start': 12113.974, 'duration': 8.167}, {'end': 12128.527, 'text': 'So here you can change the number of bins by using the bins attribute.', 'start': 12122.161, 'duration': 6.366}, {'end': 12132.771, 'text': 'So first of all, I will make a list of values here, I will write some random values.', 'start': 12128.587, 'duration': 4.184}, {'end': 12144.078, 'text': 'Right, so my data is ready now.', 'start': 12142.256, 'duration': 1.822}], 'summary': 'Created scatterplots with marker style, color, and size. also generated histograms with bin adjustments.', 'duration': 164.706, 'max_score': 11979.372, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E11979372.jpg'}, {'end': 12333.613, 'src': 'embed', 'start': 12287.043, 'weight': 1, 'content': [{'end': 12290.206, 'text': 'So now we will work with boxplot.', 'start': 12287.043, 'duration': 3.163}, {'end': 12299.033, 'text': 'So boxplot normally gives you 25 percentile, 50 percentile and 75 percentile or it is called phi number summary.', 'start': 12290.686, 'duration': 8.347}, {'end': 12309.379, 'text': 'So, this is the minimum value, this is the 25% value, this is the median value, this is the 75% value and this is the maximum value.', 'start': 12300.034, 'duration': 9.345}, {'end': 12315.042, 'text': 'So, we call this as Phi Number Summary and if we want to see the Phi Number Summary pictorially then we can use Box Plot.', 'start': 12309.979, 'duration': 5.063}, {'end': 12318.023, 'text': 'So I will repeat it again this is the minimum value, this is the 25% value, this is the 50% value,', 'start': 12315.062, 'duration': 2.961}, {'end': 12320.064, 'text': 'this is the 75% value and this is the maximum value.', 'start': 12318.023, 'duration': 2.041}, {'end': 12333.613, 'text': 'So, if I want 3 BoxPlots, then I am making 3 lists here and again I am storing it in the data by doing list of lists.', 'start': 12327.048, 'duration': 6.565}], 'summary': 'Boxplot provides 25th, 50th, and 75th percentiles visually.', 'duration': 46.57, 'max_score': 12287.043, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E12287043.jpg'}, {'end': 12689.836, 'src': 'embed', 'start': 12646.963, 'weight': 0, 'content': [{'end': 12649.244, 'text': 'So 0.1f%% this is how we can set the percentage.', 'start': 12646.963, 'duration': 2.281}, {'end': 12666.654, 'text': 'After that, if I want to change the colors, I have 4 here, so I will assign 4 new colors here and they will show up here.', 'start': 12658.769, 'duration': 7.885}, {'end': 12668.775, 'text': 'So, I just do changes here.', 'start': 12667.314, 'duration': 1.461}, {'end': 12674.598, 'text': 'So, here I will add another attribute which will be AutoPCT.', 'start': 12668.795, 'duration': 5.803}, {'end': 12684.873, 'text': 'So, here I have to write %0.1f%% So I have set the percentages here.', 'start': 12674.618, 'duration': 10.255}, {'end': 12687.795, 'text': 'After percentages, I have to see the color.', 'start': 12684.993, 'duration': 2.802}, {'end': 12689.836, 'text': 'The color is done.', 'start': 12687.855, 'duration': 1.981}], 'summary': 'Demonstrating how to set percentages and colors in a chart.', 'duration': 42.873, 'max_score': 12646.963, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E12646963.jpg'}, {'end': 12785.295, 'src': 'embed', 'start': 12755.777, 'weight': 3, 'content': [{'end': 12760.999, 'text': 'After that we are creating another pie chart and its radius is 1.', 'start': 12755.777, 'duration': 5.222}, {'end': 12763.119, 'text': 'And finally we will show it here.', 'start': 12760.999, 'duration': 2.12}, {'end': 12766.44, 'text': 'And here we will set the color of the inner circle as white.', 'start': 12763.56, 'duration': 2.88}, {'end': 12769.842, 'text': 'Right? So this is outside and this is inside.', 'start': 12766.46, 'duration': 3.382}, {'end': 12776.644, 'text': 'So there is no data inside, there is only one value and we are creating a circle of that same value.', 'start': 12770.022, 'duration': 6.622}, {'end': 12783.792, 'text': 'So, I will copy this again and paste it here.', 'start': 12780.905, 'duration': 2.887}, {'end': 12785.295, 'text': 'I will remove these colors as well.', 'start': 12783.852, 'duration': 1.443}], 'summary': 'Creating a pie chart with radius 1, with inner circle color set to white.', 'duration': 29.518, 'max_score': 12755.777, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E12755777.jpg'}], 'start': 10739.347, 'title': 'Data visualization techniques', 'summary': 'Covers creating line plots, bar plots, scatter plots, histograms, boxplots, and pie charts using matplotlib library in python. it showcases techniques for visualizing student marks, distribution of categorical values, and differentiating between categorical values. quantifiable examples are provided for visualization and distribution percentages.', 'chapters': [{'end': 11253.368, 'start': 10739.347, 'title': 'Data visualization with matplotlib', 'summary': 'Covers the process of creating line plots, including importing numpy and matplotlib, creating numpy arrays, plotting the data, and customizing the chart with labels, title, color, line style, and width. it also demonstrates plotting multiple lines on the same plot and applying different styles and colors.', 'duration': 514.021, 'highlights': ['The process of creating line plots with numpy and matplotlib is explained in detail, including importing the necessary libraries, creating numpy arrays, plotting the data, and customizing the chart with labels, title, color, line style, and width. The chapter covers the step-by-step process of creating line plots using numpy and matplotlib, including importing numpy and matplotlib, creating numpy arrays, plotting the data using plt.plot and customizing the chart with labels using plt.xlabel and plt.ylabel, title using plt.title, color, line style, and width.', "Demonstration of plotting multiple lines on the same plot and applying different styles and colors is provided, showcasing the process of creating two numpy arrays, preparing two plots, setting colors, line styles, line width, and adding grids. The chapter demonstrates plotting multiple lines on the same plot using numpy arrays, preparing two plots with different numpy arrays, setting colors using 'green' and 'red', line styles using 'dotted' and 'normal', line width, and adding grid using 'true'."]}, {'end': 11608.658, 'start': 11253.528, 'title': 'Creating line and bar plots', 'summary': 'Covers creating line plots with x and y values, adding subplots to visualize multiple plots in one screen space, and creating a bar plot to understand the distribution of a categorical value, showcasing the marks of bob, matt, sam, julia, and annie.', 'duration': 355.13, 'highlights': ['The process of creating subplots to visualize multiple plots in one screen space is explained, utilizing plt.subplot with parameters indicating one row and two columns. The process of creating subplots to visualize multiple plots in one screen space is explained, utilizing plt.subplot with parameters indicating one row and two columns.', 'The creation of a bar plot to understand the distribution of a categorical value is detailed, showcasing the marks of Bob, Matt, Sam, Julia, and Annie. The creation of a bar plot to understand the distribution of a categorical value is detailed, showcasing the marks of Bob, Matt, Sam, Julia, and Annie.', 'The process of creating line plots with x and y values is demonstrated, with the addition of grid and aesthetic elements such as title, x-axis label, and y-axis label. The process of creating line plots with x and y values is demonstrated, with the addition of grid and aesthetic elements such as title, x-axis label, and y-axis label.']}, {'end': 12204.641, 'start': 11608.678, 'title': 'Data visualization with matplotlib', 'summary': 'Covers creating bar plots, scatter plots, changing plot aesthetics, adding multiple markers to a plot, subplots, and working with histograms using matplotlib. it includes techniques to visualize student marks, scatterplot, changing aesthetics, adding multiple markers, subplots, and histograms.', 'duration': 595.963, 'highlights': ['Creating a bar plot to visualize student marks, with Annie scoring the highest and Julia scoring the lowest.', 'Explaining the process of creating a scatterplot and how to change the aesthetics of the points, such as using a different marker style and color.', 'Adding multiple markers to a plot and differentiating between them using different aesthetics like marker style, color, and size.', 'Utilizing subplots to showcase multiple scatterplots in a single graph, arranging them in two columns.', 'Detailing the process of creating a histogram and explaining the difference between histograms and bar charts, using numerical values for histograms.']}, {'end': 12445.479, 'start': 12205.021, 'title': 'Data visualization with histograms and boxplots', 'summary': 'Covers creating histograms and boxplots using the matplotlib library in python, demonstrating how to visualize the distribution of data and five-number summary pictorially with quantifiable examples.', 'duration': 240.458, 'highlights': ['The chapter demonstrates how to create histograms for sepal length and petal length columns using the plt.hist method, specifying the number of bins and color, providing hands-on guidance for data visualization.', 'It explains the concept of boxplots, depicting the 25th, 50th, and 75th percentiles along with the minimum and maximum values, and illustrates the creation of boxplots with quantifiable examples.', 'The chapter provides a step-by-step explanation of creating lists and a list of lists, followed by storing the data in a new object, offering practical insights into data manipulation and organization.']}, {'end': 12812.73, 'start': 12446.179, 'title': 'Data visualization techniques', 'summary': "Demonstrates the creation of boxplots, violin plots, and pie charts using python's matplotlib library, showcasing their usage and characteristics, such as representing distribution percentages and differentiating between categorical values.", 'duration': 366.551, 'highlights': ['Demonstrating the creation of boxplots and violin plots using plt.boxplot and plt.violinplot, showcasing their characteristics and differences. The transcript provides a demonstration of creating boxplots and violin plots using plt.boxplot and plt.violinplot, highlighting their characteristics and differences.', 'Illustrating the creation of pie charts to represent distribution percentages of different categorical values, such as fruits, and highlighting the usage of labels and quantities. The transcript illustrates the creation of pie charts to represent the distribution percentages of different categorical values, such as fruits, showcasing the usage of labels and quantities.', "Explaining the creation of a donut chart, highlighting its difference from a pie chart by showcasing the presence of a hole and its creation using Python's matplotlib library. The transcript explains the creation of a donut chart, highlighting its difference from a pie chart by showcasing the presence of a hole and its creation using Python's matplotlib library."]}], 'duration': 2073.383, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E10739347.jpg', 'highlights': ['Demonstration of plotting multiple lines on the same plot and applying different styles and colors is provided, showcasing the process of creating two numpy arrays, preparing two plots, setting colors, line styles, line width, and adding grids.', 'The chapter demonstrates how to create histograms for sepal length and petal length columns using the plt.hist method, specifying the number of bins and color, providing hands-on guidance for data visualization.', 'Illustrating the creation of pie charts to represent distribution percentages of different categorical values, such as fruits, and highlighting the usage of labels and quantities.', 'The process of creating subplots to visualize multiple plots in one screen space is explained, utilizing plt.subplot with parameters indicating one row and two columns.', 'Explaining the process of creating a scatterplot and how to change the aesthetics of the points, such as using a different marker style and color.']}, {'end': 13505.273, 'segs': [{'end': 13251.979, 'src': 'heatmap', 'start': 12928.047, 'weight': 0.77, 'content': [{'end': 12936.611, 'text': 'So as our brain learns by looking at different images of fish, it will automatically tell that these are all fish.', 'start': 12928.047, 'duration': 8.564}, {'end': 12939.939, 'text': 'We will apply the same concept in machine learning as well.', 'start': 12937.438, 'duration': 2.501}, {'end': 12942.821, 'text': 'This is our machine or computer.', 'start': 12939.999, 'duration': 2.822}, {'end': 12946.883, 'text': 'We will feed this machine this image of fish.', 'start': 12942.841, 'duration': 4.042}, {'end': 12951.806, 'text': 'And this computer will learn all the features from it.', 'start': 12947.243, 'duration': 4.563}, {'end': 12954.107, 'text': 'It will understand that fish have tails.', 'start': 12951.886, 'duration': 2.221}, {'end': 12959.33, 'text': 'It will understand that fish have tails, fins, eyes and a fish looks like this.', 'start': 12954.127, 'duration': 5.203}, {'end': 12971.765, 'text': 'So, it learns all the features of the fish and along with it, we will also add a label.', 'start': 12963.88, 'duration': 7.885}, {'end': 12979.991, 'text': 'So, whenever we feed an image, we will add a label along with the image and the fish will be written in that label.', 'start': 12971.785, 'duration': 8.206}, {'end': 12986.776, 'text': 'So, whenever this computer will see this image, it will learn that the fish looks like this and its label is fish.', 'start': 12980.011, 'duration': 6.765}, {'end': 12992.96, 'text': 'So, what is it doing here? This machine is learning from itself that this fish looks like this.', 'start': 12986.796, 'duration': 6.164}, {'end': 12996.258, 'text': 'So this is the brain concept that we are applying in the machine.', 'start': 12993.769, 'duration': 2.489}, {'end': 12998.084, 'text': 'And this is the basic concept of machine learning.', 'start': 12996.358, 'duration': 1.726}, {'end': 13006.814, 'text': 'Suppose we give a new fish to this computer because it has learned all the features.', 'start': 13000.171, 'duration': 6.643}, {'end': 13011.636, 'text': 'So when we give it a new fish, it will automatically tag it.', 'start': 13006.934, 'duration': 4.702}, {'end': 13015.777, 'text': 'So the concept is that first we will give the machine raw data or training data.', 'start': 13011.656, 'duration': 4.121}, {'end': 13022.46, 'text': 'And this machine will learn all the features of that image from raw data or training data.', 'start': 13015.797, 'duration': 6.663}, {'end': 13034.429, 'text': 'And after learning, we will give it a test data and the machine will understand how correctly it can predict.', 'start': 13025.681, 'duration': 8.748}, {'end': 13038.152, 'text': 'So we have understood what machine learning is.', 'start': 13034.469, 'duration': 3.683}, {'end': 13044.978, 'text': "So now let's see the two categories of machine learning, which are supervised learning and unsupervised learning.", 'start': 13038.172, 'duration': 6.806}, {'end': 13047.14, 'text': 'So first of all, we understand supervised learning.', 'start': 13045.479, 'duration': 1.661}, {'end': 13053.179, 'text': 'So, in supervised learning, we have an input variable and an output variable.', 'start': 13048.376, 'duration': 4.803}, {'end': 13057.782, 'text': 'And normally, we will denote the input variable as x and the output variable as y.', 'start': 13053.259, 'duration': 4.523}, {'end': 13060.824, 'text': 'And we want to understand the relation between y and x.', 'start': 13057.782, 'duration': 3.042}, {'end': 13063.266, 'text': 'So, as you can see, y is a function of x.', 'start': 13060.824, 'duration': 2.442}, {'end': 13066.148, 'text': 'So, here we want to know the relation between y and x.', 'start': 13063.266, 'duration': 2.882}, {'end': 13078.277, 'text': 'So again there are two categories in supervised learning.', 'start': 13075.396, 'duration': 2.881}, {'end': 13082.019, 'text': 'One is regression and the other is classification.', 'start': 13078.337, 'duration': 3.682}, {'end': 13089.283, 'text': 'And again keep in mind that in supervised learning we have dependent and independent variables.', 'start': 13082.159, 'duration': 7.124}, {'end': 13095.638, 'text': 'So, first of all, we understand the concept of classification in twice learning.', 'start': 13090.296, 'duration': 5.342}, {'end': 13104.021, 'text': 'So, as it is written here, the meaning of classification is that we predict the class of the new variable that comes.', 'start': 13095.658, 'duration': 8.363}, {'end': 13105.481, 'text': 'So, here is a simple example.', 'start': 13104.041, 'duration': 1.44}, {'end': 13111.283, 'text': 'So, suppose we have a list of many patients and we have to tag that patient.', 'start': 13105.501, 'duration': 5.782}, {'end': 13115.525, 'text': 'Either that patient has cancer or that patient does not have cancer.', 'start': 13111.303, 'duration': 4.222}, {'end': 13126.192, 'text': 'So this is our dependent variable, and independent variable is smoking if the patient smokes or not.', 'start': 13115.625, 'duration': 10.567}, {'end': 13131.878, 'text': 'so on this basis we want to predict or classify if the patient has cancer or not.', 'start': 13126.192, 'duration': 5.686}, {'end': 13135.341, 'text': 'so here it is happening if the patient smokes or not.', 'start': 13131.878, 'duration': 3.463}, {'end': 13149.09, 'text': 'according to that, we to classify or predict whether the patient has cancer or not.', 'start': 13135.341, 'duration': 13.749}, {'end': 13151.952, 'text': 'This is a simple concept in classification.', 'start': 13149.11, 'duration': 2.842}, {'end': 13157.095, 'text': 'And again, this is our independent variable and this is our dependent variable.', 'start': 13152.032, 'duration': 5.063}, {'end': 13164.297, 'text': 'And in classification, you have to keep in mind that your dependent variable is categorical.', 'start': 13158.035, 'duration': 6.262}, {'end': 13167.559, 'text': 'Categorical means that it cannot be a numerical value.', 'start': 13164.938, 'duration': 2.621}, {'end': 13170.26, 'text': 'There are two categories in it.', 'start': 13167.599, 'duration': 2.661}, {'end': 13175.342, 'text': 'Either yes, no or different categories like color is blue, green, red.', 'start': 13170.46, 'duration': 4.882}, {'end': 13181.504, 'text': 'Right So here your dependent variable will always be a categorical value in classification.', 'start': 13175.842, 'duration': 5.662}, {'end': 13183.585, 'text': 'So this is classification.', 'start': 13181.724, 'duration': 1.861}, {'end': 13185.266, 'text': 'Then we understand regression.', 'start': 13183.605, 'duration': 1.661}, {'end': 13191.548, 'text': 'So, as written here, we can understand the relationship between different entities through regression.', 'start': 13186.385, 'duration': 5.163}, {'end': 13199.051, 'text': 'Again, because this is supervised learning, we want to understand the relation between y and x.', 'start': 13191.628, 'duration': 7.423}, {'end': 13202.433, 'text': 'So, y is our dependent variable and x is our independent variable.', 'start': 13199.051, 'duration': 3.382}, {'end': 13206.955, 'text': 'And we want to know how dependent variable changes with independent variable.', 'start': 13202.553, 'duration': 4.402}, {'end': 13215.444, 'text': 'And in regression you have to keep in mind that your dependent variable is numerical in nature.', 'start': 13209.316, 'duration': 6.128}, {'end': 13217.687, 'text': 'Because you are predicting a numerical value.', 'start': 13215.464, 'duration': 2.223}, {'end': 13226.863, 'text': 'So this is regression.', 'start': 13225.242, 'duration': 1.621}, {'end': 13231.626, 'text': 'So regression and classification are subdomains of supervised learning.', 'start': 13227.083, 'duration': 4.543}, {'end': 13234.008, 'text': 'So now we know what unsupervised learning is.', 'start': 13231.646, 'duration': 2.362}, {'end': 13238.991, 'text': 'So in unsupervised learning, we deal with such data that has no class labels.', 'start': 13234.448, 'duration': 4.543}, {'end': 13243.354, 'text': 'So for example, we have all these data where we have cars and cycles.', 'start': 13239.051, 'duration': 4.303}, {'end': 13251.979, 'text': 'But there is no label associated with it.', 'start': 13248.157, 'duration': 3.822}], 'summary': 'Machine learning uses brain-like learning to classify and predict data, with supervised learning categorizing data and unsupervised learning dealing with unlabeled data.', 'duration': 323.932, 'max_score': 12928.047, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E12928047.jpg'}, {'end': 13022.46, 'src': 'embed', 'start': 12980.011, 'weight': 0, 'content': [{'end': 12986.776, 'text': 'So, whenever this computer will see this image, it will learn that the fish looks like this and its label is fish.', 'start': 12980.011, 'duration': 6.765}, {'end': 12992.96, 'text': 'So, what is it doing here? This machine is learning from itself that this fish looks like this.', 'start': 12986.796, 'duration': 6.164}, {'end': 12996.258, 'text': 'So this is the brain concept that we are applying in the machine.', 'start': 12993.769, 'duration': 2.489}, {'end': 12998.084, 'text': 'And this is the basic concept of machine learning.', 'start': 12996.358, 'duration': 1.726}, {'end': 13006.814, 'text': 'Suppose we give a new fish to this computer because it has learned all the features.', 'start': 13000.171, 'duration': 6.643}, {'end': 13011.636, 'text': 'So when we give it a new fish, it will automatically tag it.', 'start': 13006.934, 'duration': 4.702}, {'end': 13015.777, 'text': 'So the concept is that first we will give the machine raw data or training data.', 'start': 13011.656, 'duration': 4.121}, {'end': 13022.46, 'text': 'And this machine will learn all the features of that image from raw data or training data.', 'start': 13015.797, 'duration': 6.663}], 'summary': 'Machine learns from images to automatically tag new ones.', 'duration': 42.449, 'max_score': 12980.011, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E12980011.jpg'}, {'end': 13170.26, 'src': 'embed', 'start': 13135.341, 'weight': 2, 'content': [{'end': 13149.09, 'text': 'according to that, we to classify or predict whether the patient has cancer or not.', 'start': 13135.341, 'duration': 13.749}, {'end': 13151.952, 'text': 'This is a simple concept in classification.', 'start': 13149.11, 'duration': 2.842}, {'end': 13157.095, 'text': 'And again, this is our independent variable and this is our dependent variable.', 'start': 13152.032, 'duration': 5.063}, {'end': 13164.297, 'text': 'And in classification, you have to keep in mind that your dependent variable is categorical.', 'start': 13158.035, 'duration': 6.262}, {'end': 13167.559, 'text': 'Categorical means that it cannot be a numerical value.', 'start': 13164.938, 'duration': 2.621}, {'end': 13170.26, 'text': 'There are two categories in it.', 'start': 13167.599, 'duration': 2.661}], 'summary': 'Classify cancer presence using categorical variables.', 'duration': 34.919, 'max_score': 13135.341, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E13135341.jpg'}, {'end': 13359.158, 'src': 'embed', 'start': 13332.552, 'weight': 6, 'content': [{'end': 13340.574, 'text': 'But when you compare these two clusters, when you compare cars and cycles, then these two will be quite dissimilar.', 'start': 13332.552, 'duration': 8.022}, {'end': 13351.396, 'text': 'So a clustering algorithm will give you multiple clusters in which the properties of all the data present in one cluster will be similar.', 'start': 13340.594, 'duration': 10.802}, {'end': 13359.158, 'text': 'And if we compare two different clusters, then this data and this data will be quite dissimilar.', 'start': 13351.516, 'duration': 7.642}], 'summary': 'Clustering algorithm creates similar properties within clusters, dissimilar between clusters.', 'duration': 26.606, 'max_score': 13332.552, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E13332552.jpg'}], 'start': 12812.73, 'title': 'Python data visualization, machine learning basics & introduction to machine learning', 'summary': 'Covers python basics, data visualization, and machine learning concepts, including working with numpy, pandas, and matplotlib, and introduces supervised and unsupervised machine learning techniques such as classification, regression, and linear regression.', 'chapters': [{'end': 12866.002, 'start': 12812.73, 'title': 'Python data visualization & machine learning basics', 'summary': 'Covers the basics of python including creating pi and donut charts, working with numpy, pandas, and matplotlib, and introduces the concepts of machine learning with python implementation.', 'duration': 53.272, 'highlights': ['We created Pi and donut charts and set the color to white, demonstrating data visualization techniques.', 'We covered basics of Python, NumPy, Pandas, and Matplotlib, providing a comprehensive foundation for data analysis.', 'The chapter introduced the concepts of machine learning and its implementation in Python, setting the stage for further learning and application.']}, {'end': 13505.273, 'start': 12866.022, 'title': 'Introduction to machine learning', 'summary': "Explains the brain's learning process, applies it to machine learning, and discusses supervised learning, unsupervised learning, classification, regression, and linear regression in machine learning.", 'duration': 639.251, 'highlights': ["The brain's learning process is applied to machine learning, where a computer learns features and labels of fish images to classify them, demonstrating the concept of machine learning. Brain's learning process applied to machine learning, computer learns features and labels of fish images, demonstrating the concept of machine learning.", 'Supervised learning is explained, covering input and output variables denoted as x and y, and discussing regression and classification as subdomains of supervised learning. Supervised learning explained, input and output variables denoted as x and y, regression and classification discussed as subdomains.', 'Unsupervised learning is defined as dealing with data that has no class labels, and the process of clustering similar data into clusters based on intracluster similarity and intercluster dissimilarity is explained. Unsupervised learning defined, process of clustering similar data into clusters based on intracluster similarity and intercluster dissimilarity explained.', 'Linear regression in supervised learning is exemplified by a case study on college students to determine the relation between CGPA and GRE score. Linear regression exemplified by a case study on college students to determine the relation between CGPA and GRE score.']}], 'duration': 692.543, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E12812730.jpg', 'highlights': ['Covers python basics, data visualization, and machine learning concepts, including working with numpy, pandas, and matplotlib, and introduces supervised and unsupervised machine learning techniques such as classification, regression, and linear regression.', 'The chapter introduced the concepts of machine learning and its implementation in Python, setting the stage for further learning and application.', 'We covered basics of Python, NumPy, Pandas, and Matplotlib, providing a comprehensive foundation for data analysis.', 'Supervised learning is explained, covering input and output variables denoted as x and y, and discussing regression and classification as subdomains of supervised learning.', 'Unsupervised learning is defined as dealing with data that has no class labels, and the process of clustering similar data into clusters based on intracluster similarity and intercluster dissimilarity is explained.', 'Linear regression in supervised learning is exemplified by a case study on college students to determine the relation between CGPA and GRE score.', 'We created Pi and donut charts and set the color to white, demonstrating data visualization techniques.', "The brain's learning process is applied to machine learning, where a computer learns features and labels of fish images to classify them, demonstrating the concept of machine learning."]}, {'end': 15962.511, 'segs': [{'end': 14064.483, 'src': 'heatmap', 'start': 13884.651, 'weight': 0.826, 'content': [{'end': 13890.973, 'text': 'And before I make a model, I have to divide my data set into train and test.', 'start': 13884.651, 'duration': 6.322}, {'end': 13894.895, 'text': 'Which you have seen in theoretical phase.', 'start': 13891.013, 'duration': 3.882}, {'end': 13900.617, 'text': 'So, to divide train test, I have one more module.', 'start': 13894.935, 'duration': 5.682}, {'end': 13907.9, 'text': 'From sklearn.modelselection, I have to import train test split.', 'start': 13900.717, 'duration': 7.183}, {'end': 13910.277, 'text': 'So train test split has been imported.', 'start': 13908.615, 'duration': 1.662}, {'end': 13913.021, 'text': 'And again I use this function.', 'start': 13910.297, 'duration': 2.724}, {'end': 13915.704, 'text': 'So it will take three parameters.', 'start': 13913.061, 'duration': 2.643}, {'end': 13919.229, 'text': 'The first parameter is our independent variable.', 'start': 13915.844, 'duration': 3.385}, {'end': 13921.812, 'text': 'The second parameter is dependent variable.', 'start': 13919.269, 'duration': 2.543}, {'end': 13924.095, 'text': 'And after this I have to do test.', 'start': 13921.892, 'duration': 2.203}, {'end': 13927.317, 'text': 'So, I am giving test size as 0.3.', 'start': 13924.315, 'duration': 3.002}, {'end': 13932.521, 'text': 'This means that 30% of our data will go to test set and rest 70% will go to test set.', 'start': 13927.317, 'duration': 5.204}, {'end': 13947.811, 'text': 'So here test size is 0.3.', 'start': 13946.35, 'duration': 1.461}, {'end': 13950.092, 'text': 'I will repeat it again.', 'start': 13947.811, 'duration': 2.281}, {'end': 13961.118, 'text': 'So when we set test size to 0.3, 30% observations will go to test size and 70% observations will go to train size.', 'start': 13950.112, 'duration': 11.006}, {'end': 13967.221, 'text': 'And I will store it in 4 new objects because it gives 4 results.', 'start': 13961.158, 'duration': 6.063}, {'end': 13976.605, 'text': 'We have x-train, x-test, then y-train, And y test is done.', 'start': 13967.321, 'duration': 9.284}, {'end': 13978.767, 'text': 'This function gives four results.', 'start': 13977.086, 'duration': 1.681}, {'end': 13983.25, 'text': 'So x train is the training set of our independent variable.', 'start': 13978.787, 'duration': 4.463}, {'end': 13986.812, 'text': 'x test is the test set of independent variable.', 'start': 13983.27, 'duration': 3.542}, {'end': 13989.995, 'text': 'y train is the training set of dependent variable.', 'start': 13986.832, 'duration': 3.163}, {'end': 13993.677, 'text': 'And y test is the test set of dependent variable.', 'start': 13990.115, 'duration': 3.562}, {'end': 13996.604, 'text': 'So we have all these things.', 'start': 13995.262, 'duration': 1.342}, {'end': 14004.214, 'text': 'So now, because we have imported the linear regression module, we will make an instance of it.', 'start': 13996.624, 'duration': 7.59}, {'end': 14007.838, 'text': 'So to make an instance, I will make a new object.', 'start': 14004.234, 'duration': 3.604}, {'end': 14010.862, 'text': 'Then I will write linear regression here.', 'start': 14007.879, 'duration': 2.983}, {'end': 14020.367, 'text': 'Right, I am storing the instance of linear regression in LR and I am fitting a model on the training data.', 'start': 14012.063, 'duration': 8.304}, {'end': 14027.69, 'text': 'I will write LR.fit and in this I will pass X frame and Y frame.', 'start': 14020.487, 'duration': 7.203}, {'end': 14034.719, 'text': 'So, I have fitted the model above the training data.', 'start': 14031.556, 'duration': 3.163}, {'end': 14038.283, 'text': 'After fitting the model, I have to predict the values.', 'start': 14034.799, 'duration': 3.484}, {'end': 14041.406, 'text': 'To predict the values, I will write LR.PREDICT and I have to predict on XTEST.', 'start': 14038.323, 'duration': 3.083}, {'end': 14064.483, 'text': 'because we are training on training set and obviously we will predict on test set and the final result will be stored in yPRED.', 'start': 14051.856, 'duration': 12.627}], 'summary': 'Data set is divided into 70% train and 30% test using train test split, and linear regression model is fitted and used for prediction.', 'duration': 179.832, 'max_score': 13884.651, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E13884651.jpg'}, {'end': 14661.337, 'src': 'embed', 'start': 14638.041, 'weight': 4, 'content': [{'end': 14646.725, 'text': "So we want to know on the basis of customer's tenure, will the customer use this service or leave it.", 'start': 14638.041, 'duration': 8.684}, {'end': 14651.849, 'text': 'So, we will separate it into independent and dependent variables.', 'start': 14649.147, 'duration': 2.702}, {'end': 14653.11, 'text': 'So, my independent variable is tenure.', 'start': 14651.869, 'duration': 1.241}, {'end': 14655.092, 'text': 'So, I will extract tenure from customer first.', 'start': 14653.13, 'duration': 1.962}, {'end': 14656.353, 'text': 'And I will store it in X.', 'start': 14655.232, 'duration': 1.121}, {'end': 14658.655, 'text': 'After that, I need my dependent variable.', 'start': 14656.353, 'duration': 2.302}, {'end': 14661.337, 'text': 'So, this time I will extract churn from customer.', 'start': 14658.675, 'duration': 2.662}], 'summary': 'Analyzing customer tenure to predict service usage or churn.', 'duration': 23.296, 'max_score': 14638.041, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E14638041.jpg'}, {'end': 14795.917, 'src': 'embed', 'start': 14752.248, 'weight': 1, 'content': [{'end': 14759.931, 'text': 'here I will pass x and y and after this I have to give test size, and this time I am giving test size 0.35.', 'start': 14752.248, 'duration': 7.683}, {'end': 14765.173, 'text': 'this means that 35% observations will remain in test set and rest 65% observations will remain in train set.', 'start': 14759.931, 'duration': 5.242}, {'end': 14779.205, 'text': 'so I Let me change it.', 'start': 14765.173, 'duration': 14.032}, {'end': 14788.932, 'text': 'I have xtrain, xtest, then ytrain, then ytest.', 'start': 14779.265, 'duration': 9.667}, {'end': 14791.934, 'text': 'I have four new variables.', 'start': 14789.933, 'duration': 2.001}, {'end': 14795.917, 'text': 'Then I have to fit this log model to the training set.', 'start': 14792.155, 'duration': 3.762}], 'summary': 'Training a log model with 35% test size.', 'duration': 43.669, 'max_score': 14752.248, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E14752248.jpg'}, {'end': 15317.075, 'src': 'embed', 'start': 15281.01, 'weight': 0, 'content': [{'end': 15286.236, 'text': 'So my data frame is ready and I have to see its head.', 'start': 15281.01, 'duration': 5.226}, {'end': 15294.104, 'text': 'So, we have already seen the example of IRIS when we were doing data manipulation in the Pandas session.', 'start': 15286.296, 'duration': 7.808}, {'end': 15295.526, 'text': 'So, this is our IRIS data frame.', 'start': 15294.124, 'duration': 1.402}, {'end': 15309.492, 'text': 'So here we have 5 columns, sepal length, sepal width, petal length, petal width species.', 'start': 15302.509, 'duration': 6.983}, {'end': 15317.075, 'text': 'So here we have 3 species of this iris flower, setosa, virginica and versicolor.', 'start': 15309.992, 'duration': 7.083}], 'summary': 'Data frame with 5 columns: sepal length, sepal width, petal length, petal width species, and 3 iris species: setosa, virginica, and versicolor.', 'duration': 36.065, 'max_score': 15281.01, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E15281010.jpg'}, {'end': 15791.743, 'src': 'embed', 'start': 15749.721, 'weight': 2, 'content': [{'end': 15756.574, 'text': 'then I have to divide it in train test, train test split.', 'start': 15749.721, 'duration': 6.853}, {'end': 15758.495, 'text': 'I will pass x and y here.', 'start': 15756.574, 'duration': 1.921}, {'end': 15765.137, 'text': 'then I will have to give test size and I will give test size 0.25, which means 25%.', 'start': 15758.495, 'duration': 6.642}, {'end': 15770.958, 'text': 'observations will go to test set and rest 75% observations will go to train set.', 'start': 15765.137, 'duration': 5.821}, {'end': 15788.46, 'text': 'so I will store this x train, x test after that Y train and after that Y test.', 'start': 15770.958, 'duration': 17.502}, {'end': 15791.743, 'text': 'My four variables are ready.', 'start': 15789.341, 'duration': 2.402}], 'summary': 'Data is split into 75% train and 25% test sets, creating four variables for analysis.', 'duration': 42.022, 'max_score': 15749.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E15749721.jpg'}, {'end': 15868.924, 'src': 'embed', 'start': 15831.008, 'weight': 3, 'content': [{'end': 15835.631, 'text': 'Because obviously I have to fit the model on the training set.', 'start': 15831.008, 'duration': 4.623}, {'end': 15839.102, 'text': 'This is the model I have fitted.', 'start': 15836.88, 'duration': 2.222}, {'end': 15840.823, 'text': 'After this, I have to predict.', 'start': 15839.282, 'duration': 1.541}, {'end': 15841.644, 'text': 'So, dtr.predict.', 'start': 15840.843, 'duration': 0.801}, {'end': 15847.069, 'text': 'And I will predict on the test set.', 'start': 15841.784, 'duration': 5.285}, {'end': 15851.472, 'text': 'And I will store the values again in y thread.', 'start': 15847.229, 'duration': 4.243}, {'end': 15855.676, 'text': 'So, I have actual values.', 'start': 15851.512, 'duration': 4.164}, {'end': 15857.617, 'text': 'ytest.head Which I will show you.', 'start': 15855.696, 'duration': 1.921}, {'end': 15865.361, 'text': 'These are my actual values.', 'start': 15863.88, 'duration': 1.481}, {'end': 15868.924, 'text': 'And after this, I will show you the predicted values.', 'start': 15865.501, 'duration': 3.423}], 'summary': 'Fitting model on training set, predicting on test set, showing actual and predicted values.', 'duration': 37.916, 'max_score': 15831.008, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E15831008.jpg'}], 'start': 13506.767, 'title': 'Understanding and implementing regression models in python', 'summary': 'Covers the concept of linear regression, demonstrating its application with the boston pricing dataset achieving a mean squared error of 73, comparing linear regression models with different independent variables, introducing logistic regression for categorical values, and implementing decision tree algorithm in python achieving 63% accuracy and a final error in prediction of 0.133.', 'chapters': [{'end': 13692.002, 'start': 13506.767, 'title': 'Understanding linear regression', 'summary': 'Explains the concept of linear regression, highlighting the process of finding the best fit line using residual sum of squares, where the second line has the lowest residual sum of square of 22.', 'duration': 185.235, 'highlights': ['The concept of linear regression is explained, emphasizing the process of finding the best fit line using residual sum of squares, with the second line having the lowest residual sum of square of 22.', 'The relationship between y and x is illustrated as a straight line, and the process of identifying the best fit line is highlighted using the residual sum of squares, where the second line has the lowest residual sum of square of 22.', 'The importance of residual sum of squares in determining the best fit line is emphasized, with the second line having the lowest residual sum of square of 22.']}, {'end': 14177.583, 'start': 13697.253, 'title': 'Linear regression with boston pricing dataset', 'summary': 'Demonstrates loading the boston pricing dataset, extracting dependent and independent variables, dividing the dataset into training and test sets, creating a linear regression model, and evaluating the model with a mean squared error of 73.', 'duration': 480.33, 'highlights': ["Creating linear regression model instance and fitting the model on training data The instance of linear regression is created and fitted on the training data using the 'fit' method, indicating the process of model training.", "Dividing the dataset into training and test sets with a 70-30 split The dataset is split into training and test sets with 70% for training and 30% for testing, ensuring a proper evaluation of the model's performance.", "Loading the Boston Pricing dataset and extracting the median pricing column (MEDV) The Boston Pricing dataset is loaded, and the 'MEDV' column, representing the median pricing of houses, is extracted for analysis."]}, {'end': 14488.229, 'start': 14178.084, 'title': 'Comparing linear regression models', 'summary': 'Demonstrates the process of creating and comparing linear regression models using crim and lstat as independent variables, achieving mean squared errors of 73 and 108 respectively, indicating the superiority of the first model. it also introduces the concept of logistic regression for dealing with categorical values.', 'duration': 310.145, 'highlights': ['The first linear regression model using the independent variable CRIM achieved a mean squared error of 73, while the second model using the independent variable LSTAT resulted in a mean squared error of 108, indicating the superiority of the first model.', 'Logistic regression is introduced as a method for predicting categorical values by providing probabilities between 0 and 1, enabling easier classification, unlike linear regression which is used for predicting numerical values.']}, {'end': 15201.405, 'start': 14488.289, 'title': 'Logistic regression and decision tree', 'summary': 'Explains logistic regression using customer churn data, achieving 72.99% accuracy, and introduces decision tree as a machine learning algorithm for classification and regression.', 'duration': 713.116, 'highlights': ['The logistic regression model achieved 72.99% accuracy in predicting customer churn based on tenure. The left diagonal of the confusion matrix divided by all values gives the accuracy of the logistic regression model as 72.99%.', 'Introduction to decision tree as a machine learning algorithm for classification and regression. The decision tree is described as a structure wherein decisions are made at nodes, and the final result is shown on the leaf nodes, allowing implementation for both classification and regression.', 'Explanation of the decision tree using an example of making decisions to watch a movie based on liking Marvel movies and Robert Downey Jr. An example is provided to illustrate the decision-making process within the decision tree, demonstrating how decisions about watching a movie are made based on liking Marvel movies and a specific actor.']}, {'end': 15631.544, 'start': 15201.445, 'title': 'Decision tree in python', 'summary': 'Discusses the implementation of decision tree algorithm for classification and regression using the iris dataset in python, achieving 22 correct classifications for setosa, 9 for virginica, and 7 for versicolor.', 'duration': 430.099, 'highlights': ['The chapter discusses the implementation of decision tree algorithm for classification and regression using the iris dataset in Python, achieving 22 correct classifications for Setosa, 9 for Virginica, and 7 for Versicolor. Implementation of decision tree algorithm, classification and regression, iris dataset in Python, 22 correct classifications for Setosa, 9 for Virginica, 7 for Versicolor.', 'The confusion matrix shows 22 correctly classified instances for Setosa, 7 for Versicolor, and 9 for Virginica. Confusion matrix, 22 correctly classified instances for Setosa, 7 for Versicolor, 9 for Virginica.', 'The decision tree model achieved an accuracy of 79.16% for classifying the iris species. Accuracy of 79.16% for classifying the iris species.']}, {'end': 15962.511, 'start': 15631.584, 'title': 'Implementing machine learning in python', 'summary': 'Discusses the implementation of three machine learning algorithms (linear regression, logistic regression, and decision tree) in python, achieving 63% accuracy with the decision tree classifier and a final error in prediction of 0.133 using decision tree regressor.', 'duration': 330.927, 'highlights': ['The decision tree classifier achieved 63% accuracy, showcasing the effectiveness of the machine learning model.', "The decision tree regressor resulted in a final error in prediction of 0.133, demonstrating the model's predictive capability.", 'The implementation covered three machine learning algorithms: linear regression, logistic regression, and decision tree algorithm, providing a comprehensive overview of machine learning in Python.']}], 'duration': 2455.744, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OuPMVdski3E/pics/OuPMVdski3E13506767.jpg', 'highlights': ['Linear regression concept explained with emphasis on finding best fit line using residual sum of squares', 'Introduction to logistic regression for predicting categorical values with probabilities between 0 and 1', 'Decision tree algorithm implementation in Python achieved 63% accuracy and a final error in prediction of 0.133', 'Boston Pricing dataset used for linear regression model with independent variables achieving mean squared error of 73', 'Introduction to decision tree as a machine learning algorithm for classification and regression', 'Confusion matrix showing 22 correctly classified instances for Setosa, 7 for Versicolor, and 9 for Virginica']}], 'highlights': ['The course is designed to address the lack of high-quality tutorials in Hindi, providing comprehensive learning and implementation of Machine Learning with Python.', 'Python has four basic data structures: tuple, list, dictionary, and set.', "Introduction of lambda functions in Python, showcasing the creation of an anonymous function using the 'Lambda' keyword and demonstrating its usage with quantifiable data of the computed results.", 'NumPy is a core library for numeric and scientific computing, offering multi-dimensional array objects and methods for processing them, making it efficient for complex mathematical operations.', 'Pandas is a core library for data manipulation and analysis.', 'Demonstration of plotting multiple lines on the same plot and applying different styles and colors is provided, showcasing the process of creating two numpy arrays, preparing two plots, setting colors, line styles, line width, and adding grids.', 'Covers python basics, data visualization, and machine learning concepts, including working with numpy, pandas, and matplotlib, and introduces supervised and unsupervised machine learning techniques such as classification, regression, and linear regression.', 'Linear regression concept explained with emphasis on finding best fit line using residual sum of squares', 'Introduction to logistic regression for predicting categorical values with probabilities between 0 and 1', 'Decision tree algorithm implementation in Python achieved 63% accuracy and a final error in prediction of 0.133']}