title
Artificial Intelligence with Python | Artificial Intelligence Tutorial using Python | Edureka

description
🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy NIT Warangal: https://www.edureka.co/executive-programs/machine-learning-and-ai This Edureka video on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples. Python Course: https://www.youtube.com/watch?v=vaysJAMDaZw Statistics and Probability Tutorial: https://www.youtube.com/watch?v=XcLO4f1i4Yo Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4 Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV ----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐲𝐭𝐡𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠𝐬----------- 🔵Python Programming Certification: http://bit.ly/37rEsnA 🔵Python Certification Training for Data Science: http://bit.ly/2Gj6fux ----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦---------- 🔵Data Scientist Masters Program: http://bit.ly/2t1snGM 🔵Machine Learning Engineer Masters Program: https://bit.ly/3Hi1sXN -----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦---------- 🌕Post Graduate Diploma in Artificial Intelligence Course offered by E&ICT Academy NIT Warangal: https://bit.ly/3qdRRdw #edureka #aiEdureka #artificialIntelligence #artificialIntelligenceTutorial #artificialIntelligenceWithPython #artificialIntelligenceEngineer Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Slideshare: https://www.slideshare.net/EdurekaIN/ ------------------------------------- About the Masters Program Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning. The Master's Program Covers Topics LIke: Python Programming PySpark HDFS Spark SQL Machine Learning Techniques and Artificial Intelligence Types Tokenization Named Entity Recognition Lemmatization Supervised Algorithms Unsupervised Algorithms Tensor Flow Deep learning Keras Neural Networks Bayesian and Markov’s Models Inference Decision Making Bandit Algorithms Bellman Equation Policy Gradient Methods. ---------------------- Prerequisites There are no prerequisites for enrolment to the Masters Program. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills. This program is designed and developed for an aspirant planning to build a career in Machine Learning or an experienced professional working in the IT industry. -------------------------------------- Please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information

detail
{'title': 'Artificial Intelligence with Python | Artificial Intelligence Tutorial using Python | Edureka', 'heatmap': [{'end': 1476.712, 'start': 1407.719, 'weight': 1}, {'end': 2955.309, 'start': 2885.933, 'weight': 0.929}, {'end': 3260.832, 'start': 3193.674, 'weight': 0.735}], 'summary': "This tutorial covers python's role in ai, machine learning process, types of machine learning, building rain prediction models with 84% accuracy, limitations of machine learning, neural networks, back propagation, and deep learning achieving 96% accuracy in nlp.", 'chapters': [{'end': 187.4, 'segs': [{'end': 131.986, 'src': 'embed', 'start': 107.39, 'weight': 0, 'content': [{'end': 115.034, 'text': 'perceptrons and multi-layer perceptrons will end the deep learning module by discussing a practical implementation with python.', 'start': 107.39, 'duration': 7.644}, {'end': 119.357, 'text': 'right after that, we move on to our last module, which is natural language processing here.', 'start': 115.034, 'duration': 4.323}, {'end': 126.962, 'text': "We'll discuss what exactly NLP is where it is used its applications and the various terminologies in natural language processing.", 'start': 119.397, 'duration': 7.565}, {'end': 129.244, 'text': "So guys, there's a lot to cover in today's session.", 'start': 127.422, 'duration': 1.822}, {'end': 131.986, 'text': "Let's move ahead and take a look at our first topic.", 'start': 129.584, 'duration': 2.402}], 'summary': 'Discusses perceptrons, multi-layer perceptrons, and natural language processing in the deep learning module.', 'duration': 24.596, 'max_score': 107.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng107390.jpg'}], 'start': 11.46, 'title': 'Python for ai', 'summary': 'Discusses using python for ai, highlighting its popularity due to less coding and ease of testing, covering popular python packages for ai, machine learning, deep learning, and natural language processing.', 'chapters': [{'end': 187.4, 'start': 11.46, 'title': 'Python for artificial intelligence', 'summary': "Discusses the implementation of artificial intelligence using python, covering the reasons for choosing python, popular python packages for ai, machine learning and deep learning, the introduction of ai, machine learning and deep learning, the limitations of machine learning, introduction of deep learning, and natural language processing, highlighting python's popularity due to less coding and ease of testing.", 'duration': 175.94, 'highlights': ["Python is popular for AI, machine learning, and deep learning due to less coding required, which eases the process of testing with its 'check as you code' methodology, making it easier to implement and test numerous machine learning and deep learning algorithms.", 'The session covers the reasons for choosing Python for artificial intelligence, the popular Python packages for machine learning, deep learning, and AI, the introduction of AI, machine learning, and deep learning, the limitations of machine learning, the introduction of deep learning, and natural language processing.', 'Tech giants like Tesla, Amazon, and Netflix are implementing AI techniques to derive useful insights from data, showcasing the practical applications and significance of AI in business growth and development.']}], 'duration': 175.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng11460.jpg', 'highlights': ["Python is popular for AI, machine learning, and deep learning due to less coding required, easing testing with 'check as you code' methodology.", 'Tech giants like Tesla, Amazon, and Netflix implement AI techniques for deriving useful insights from data, showcasing practical applications and significance of AI in business growth.']}, {'end': 1612.573, 'segs': [{'end': 418.037, 'src': 'embed', 'start': 385.078, 'weight': 0, 'content': [{'end': 389.221, 'text': "and so that's why python is the best choice for artificial intelligence.", 'start': 385.078, 'duration': 4.143}, {'end': 393.764, 'text': "for those of you who are not aware of python programming and don't know much about python.", 'start': 389.221, 'duration': 4.543}, {'end': 397.126, 'text': "I'm going to leave a couple of links in the description box, right?", 'start': 394.204, 'duration': 2.922}, {'end': 403.03, 'text': 'You can go through those links and study a little bit more about how python works or how the coding part works.', 'start': 397.146, 'duration': 5.884}, {'end': 408.314, 'text': "right?. I'm going to be focusing mainly on artificial intelligence and I'll be showing you a lot of demos.", 'start': 403.03, 'duration': 5.284}, {'end': 410.595, 'text': 'So those of you are not aware of python.', 'start': 408.714, 'duration': 1.881}, {'end': 418.037, 'text': "Make sure you check the description box, right? Next I'm going to discuss the different python packages for artificial intelligence.", 'start': 410.735, 'duration': 7.302}], 'summary': 'Python is the best choice for ai. links provided for more information. focus on ai demos and discussing python packages.', 'duration': 32.959, 'max_score': 385.078, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng385078.jpg'}, {'end': 470.736, 'src': 'embed', 'start': 438.624, 'weight': 8, 'content': [{'end': 442.468, 'text': 'This library was developed by Google in collaboration with Brainteam.', 'start': 438.624, 'duration': 3.844}, {'end': 446.472, 'text': 'TensorFlow is used in almost every Google application for machine learning.', 'start': 442.848, 'duration': 3.624}, {'end': 449.456, 'text': 'Now, let me just discuss a few features of TensorFlow.', 'start': 446.892, 'duration': 2.564}, {'end': 457.089, 'text': 'It has a responsive construct, meaning that with TensorFlow, we can easily visualize each and every part of the graph,', 'start': 449.997, 'duration': 7.092}, {'end': 461.336, 'text': "which is not an option when you're using other packages such as numpy or psychic right?", 'start': 457.089, 'duration': 4.247}, {'end': 464.175, 'text': "Another feature is that it's very flexible.", 'start': 462.034, 'duration': 2.141}, {'end': 470.736, 'text': 'Now, one of the most important TensorFlow features is that it is flexible in operability,', 'start': 464.575, 'duration': 6.161}], 'summary': 'Google and brainteam developed tensorflow, used in almost every google ml app, with responsive and flexible features.', 'duration': 32.112, 'max_score': 438.624, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng438624.jpg'}, {'end': 1134.936, 'src': 'embed', 'start': 1105.47, 'weight': 5, 'content': [{'end': 1112.251, 'text': 'So through neural networks were actually able to solve a lot of problems including health care problems fraud detection problems and so on.', 'start': 1105.47, 'duration': 6.781}, {'end': 1114.812, 'text': 'Another reason is broad investment.', 'start': 1112.751, 'duration': 2.061}, {'end': 1122.193, 'text': 'So our universities and governments and startups and any tech Giants like Google Amazon and Facebook.', 'start': 1115.052, 'duration': 7.141}, {'end': 1127.394, 'text': 'They are all investing heavily in artificial intelligence, which also led to the demand of AI.', 'start': 1122.533, 'duration': 4.861}, {'end': 1134.936, 'text': "So AI is rapidly growing both as a field of study and also as an economy, right? It's adding a lot to the economy.", 'start': 1127.775, 'duration': 7.161}], 'summary': 'Neural networks solve healthcare, fraud detection; ai investment grows economy.', 'duration': 29.466, 'max_score': 1105.47, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1105470.jpg'}, {'end': 1217.787, 'src': 'embed', 'start': 1187.084, 'weight': 13, 'content': [{'end': 1192.387, 'text': 'in a sense, artificial intelligence is a technique of getting machines to work and behave like humans.', 'start': 1187.084, 'duration': 5.303}, {'end': 1193.913, 'text': 'In the recent past.', 'start': 1193.032, 'duration': 0.881}, {'end': 1201.857, 'text': 'He has actually been able to accomplish this by creating machines and robots that have been used in a wide range of fields, including healthcare,', 'start': 1193.933, 'duration': 7.924}, {'end': 1205.6, 'text': 'robotics, marketing, business analytics and many more, right?', 'start': 1201.857, 'duration': 3.743}, {'end': 1207.561, 'text': 'So AI is actually a very vast field.', 'start': 1205.62, 'duration': 1.941}, {'end': 1217.787, 'text': 'It covers a lot of domains including machine learning natural language processing knowledge base deep learning computer vision and expert systems.', 'start': 1207.981, 'duration': 9.806}], 'summary': 'Artificial intelligence has been used in healthcare, robotics, marketing, business analytics, and more, covering domains such as machine learning, natural language processing, and computer vision.', 'duration': 30.703, 'max_score': 1187.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1187084.jpg'}, {'end': 1348.606, 'src': 'embed', 'start': 1321.672, 'weight': 6, 'content': [{'end': 1327.557, 'text': "humans who are limited by slow biological evolution couldn't compete and would be superseded right?", 'start': 1321.672, 'duration': 5.885}, {'end': 1334.943, 'text': 'So we have a lot of tech Giants and a lot of geniuses who are actually worried if strong AI is ever implemented.', 'start': 1327.577, 'duration': 7.366}, {'end': 1336.504, 'text': 'It might take over the world.', 'start': 1335.343, 'duration': 1.161}, {'end': 1341.058, 'text': 'right so guys let me tell you that strong AI is something that has not been implemented yet.', 'start': 1337.074, 'duration': 3.984}, {'end': 1348.606, 'text': 'We are only at the first stage of artificial intelligence, which is artificial narrow intelligence, also known as weak AI right?', 'start': 1341.218, 'duration': 7.388}], 'summary': 'Tech giants and geniuses worry about strong ai taking over the world, but we are currently only at the first stage of artificial intelligence - weak ai.', 'duration': 26.934, 'max_score': 1321.672, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1321672.jpg'}, {'end': 1476.712, 'src': 'heatmap', 'start': 1407.719, 'weight': 1, 'content': [{'end': 1414.185, 'text': "So now let's move on and talk about how artificial intelligence is different from machine learning and deep learning.", 'start': 1407.719, 'duration': 6.466}, {'end': 1422.653, 'text': 'a lot of people tend to assume that artificial intelligence, machine learning and deep learning are the same because they have common applications.', 'start': 1414.185, 'duration': 8.468}, {'end': 1427.477, 'text': 'right. for example, City is an application of AI machine learning and deep learning.', 'start': 1422.653, 'duration': 4.824}, {'end': 1431.068, 'text': 'So how are these technologies related right?', 'start': 1427.967, 'duration': 3.101}, {'end': 1432.669, 'text': 'Or how are they different from each other??', 'start': 1431.148, 'duration': 1.521}, {'end': 1439.032, 'text': 'Now, artificial intelligence is the science of getting machines to mimic the behavior of human beings.', 'start': 1433.109, 'duration': 5.923}, {'end': 1448.176, 'text': 'Machine learning is the subset of artificial intelligence that focuses on getting machines to make decisions by feeding them data.', 'start': 1439.632, 'duration': 8.544}, {'end': 1456.1, 'text': 'Deep learning on the other hand is a subset of machine learning that uses the concept of neural networks to solve complex problems.', 'start': 1448.596, 'duration': 7.504}, {'end': 1463.793, 'text': 'So, to sum it up to you, artificial intelligence, machine learning and deep learning are heavily interconnected fields.', 'start': 1456.869, 'duration': 6.924}, {'end': 1471.978, 'text': 'right machine learning and deep learning aids artificial intelligence by providing a set of algorithms and neural networks to solve data-driven problems.', 'start': 1463.793, 'duration': 8.185}, {'end': 1476.712, 'text': 'However, AI is not restricted to only machine learning and deep learning, right?', 'start': 1472.549, 'duration': 4.163}], 'summary': 'Ai, machine learning, and deep learning are interconnected fields with ai mimicking human behavior and machine learning focusing on decision-making using data.', 'duration': 68.993, 'max_score': 1407.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1407719.jpg'}, {'end': 1448.176, 'src': 'embed', 'start': 1422.653, 'weight': 11, 'content': [{'end': 1427.477, 'text': 'right. for example, City is an application of AI machine learning and deep learning.', 'start': 1422.653, 'duration': 4.824}, {'end': 1431.068, 'text': 'So how are these technologies related right?', 'start': 1427.967, 'duration': 3.101}, {'end': 1432.669, 'text': 'Or how are they different from each other??', 'start': 1431.148, 'duration': 1.521}, {'end': 1439.032, 'text': 'Now, artificial intelligence is the science of getting machines to mimic the behavior of human beings.', 'start': 1433.109, 'duration': 5.923}, {'end': 1448.176, 'text': 'Machine learning is the subset of artificial intelligence that focuses on getting machines to make decisions by feeding them data.', 'start': 1439.632, 'duration': 8.544}], 'summary': 'Ai, machine learning, and deep learning explained; ai mimics human behavior, machine learning focuses on decision-making.', 'duration': 25.523, 'max_score': 1422.653, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1422653.jpg'}, {'end': 1519.244, 'src': 'embed', 'start': 1485.239, 'weight': 4, 'content': [{'end': 1487.56, 'text': 'right?. So AI is a very vast field, guys.', 'start': 1485.239, 'duration': 2.321}, {'end': 1492.004, 'text': 'I hope I cleared the difference between AI machine learning and deep learning.', 'start': 1487.601, 'duration': 4.403}, {'end': 1495.326, 'text': 'also, a lot of you might be confused about data science.', 'start': 1492.004, 'duration': 3.322}, {'end': 1497.588, 'text': 'data science is now an umbrella term.', 'start': 1495.326, 'duration': 2.262}, {'end': 1501.291, 'text': 'right data science basically means to derive useful insights from data.', 'start': 1497.588, 'duration': 3.703}, {'end': 1506.858, 'text': 'So data science actually uses AI, machine learning and deep learning right,', 'start': 1501.775, 'duration': 5.083}, {'end': 1512.7, 'text': 'so it implements all of these three Technologies in order to derive useful insights from data.', 'start': 1506.858, 'duration': 5.842}, {'end': 1519.244, 'text': "right now, let's move on to the most interesting topic in artificial intelligence, which is machine learning.", 'start': 1512.7, 'duration': 6.544}], 'summary': 'Ai includes data science, machine learning, and deep learning to derive insights from data.', 'duration': 34.005, 'max_score': 1485.239, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1485239.jpg'}, {'end': 1589.525, 'src': 'embed', 'start': 1541.27, 'weight': 7, 'content': [{'end': 1553.978, 'text': 'machine learning is a subset of artificial intelligence which provides machines the ability to learn automatically and improve from experience without being explicitly programmed to do so.', 'start': 1541.27, 'duration': 12.708}, {'end': 1560.602, 'text': 'In a sense, it is the practice of getting machines to solve problems by gaining the ability to think.', 'start': 1554.64, 'duration': 5.962}, {'end': 1565.884, 'text': 'Now the question here is can a machine think or can a machine make decisions??', 'start': 1561.302, 'duration': 4.582}, {'end': 1571.205, 'text': 'Well, if you feed a machine a good amount of data, it will learn how to interpret,', 'start': 1566.504, 'duration': 4.701}, {'end': 1576.127, 'text': 'process and analyze this data by using something known as machine learning algorithms.', 'start': 1571.205, 'duration': 4.922}, {'end': 1582.169, 'text': 'To give you a basic idea of how the machine learning process works, look at the figure on this slide.', 'start': 1576.627, 'duration': 5.542}, {'end': 1589.525, 'text': 'A machine learning process always begins by feeding the machine lots and lots of data now by using this data.', 'start': 1582.842, 'duration': 6.683}], 'summary': 'Machine learning enables machines to learn and make decisions from data.', 'duration': 48.255, 'max_score': 1541.27, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1541270.jpg'}], 'start': 188.12, 'title': "Python's role in ai and ai overview", 'summary': "Explores python's significance in ai with pre-built libraries, learning ease, platform independence, and community support. it also covers key python libraries for ai, the rise of ai, and an overview of ai, including types and applications.", 'chapters': [{'end': 366.649, 'start': 188.12, 'title': 'Python for ai: libraries, learning, platform, community', 'summary': "Discusses the reasons for choosing python for ai, including its pre-built libraries for machine learning algorithms, ease of learning due to simple syntax and english-like readability, platform independence with package support, and massive community support, emphasized by stack overflow's statistic of python being the fastest growing programming language, overtaking javascript, java, c#, c++, and php.", 'duration': 178.529, 'highlights': ["Python's support for pre-built libraries makes it convenient for AI developers as it has predefined machine learning and deep learning algorithms, saving significant coding time.", "Python's simplicity and English-like readability make it the easiest programming language, suitable for solving both simple and complex problems, including machine learning and deep learning models.", "Python's platform independence is supported by packages like pi installer, which resolves dependency issues when transferring code between operating systems, ensuring smooth project execution.", 'The massive community support for Python is evidenced by its exponential growth as the fastest growing programming language, surpassing JavaScript, Java, C#, C++, and PHP, particularly in the domains of data science and artificial intelligence.', "Stack Overflow's statistic highlights Python's popularity, indicating its rapid growth as the fastest growing programming language, especially in data science and artificial intelligence, surpassing other languages like JavaScript, Java, C#, C++, and PHP."]}, {'end': 805.159, 'start': 366.649, 'title': 'Python for ai: libraries and features', 'summary': 'Discusses the importance of python in ai development, highlighting key packages like tensorflow, scikit-learn, numpy, theano, and keras, emphasizing their features and relevance to machine learning and deep learning.', 'duration': 438.51, 'highlights': ['The chapter emphasizes the significance of Python in AI development, particularly for machine learning and deep learning, as it is widely used due to its simplicity and the availability of various libraries like TensorFlow, scikit-learn, numpy, Theano, and Keras.', 'TensorFlow is highlighted for its wide usage in Google applications for machine learning, its flexible operability, support for parallel neural network training, and large community, making it a crucial library for AI development.', 'Scikit-learn is described as one of the best libraries for working with complex data, implementing cross-validation for model accuracy, providing a wide range of algorithms for unsupervised learning, and being essential for feature extraction in images and texts.', 'Numpy is recognized as a popular machine learning library, particularly used internally by TensorFlow and other libraries, known for its array interface, simplifying complex mathematical implementations, and being essential for statistical and data analysis involving mathematical computations.', 'Theano is highlighted for its computational framework, allowing the implementation of multi-dimensional arrays, integration with numpy, utilization of GPU for faster data-intensive computations, and its significance in handling computations required for large neural network algorithms.', 'Keras is emphasized for being the most popular Python package, providing functionalities for compiling models, processing datasets, visualizing graphs, and implementing neural networks, with smooth support for various neural network models and being completely python-based for easy debugging and exploration.']}, {'end': 1147.765, 'start': 805.775, 'title': 'Python libraries and the rise of artificial intelligence', 'summary': 'Discusses essential python libraries for machine learning and deep learning, the history and popularity of artificial intelligence, and the main reasons behind its recent surge, including computational power, data availability, improved algorithms, and broad investment.', 'duration': 341.99, 'highlights': ['The emergence of AI in 1950s led to an exponential growth in its potential, covering domains like machine learning, deep learning, neural networks, natural language processing, knowledge base, computer vision, and image processing.', "Key reasons for the recent surge in AI's popularity include more computational power due to advances in technology like GPUs, the availability of more data, better algorithms based on neural networks, and broad investment from various entities like universities, governments, and tech giants.", 'The Natural Language Toolkit is an open source Python library mainly used for natural language processing, text analysis, and text mining, and it performs tasks such as stemming, lemmatization, and tokenization to draw useful information from natural language text.']}, {'end': 1612.573, 'start': 1148.406, 'title': 'Artificial intelligence overview', 'summary': 'Provides an overview of artificial intelligence (ai), covering its definition, evolution, types including narrow, general, and super intelligence, and the distinction between ai, machine learning, and deep learning. it also delves into the application of ai in data science.', 'duration': 464.167, 'highlights': ['Artificial intelligence (AI) was first coined in 1956 by John McCarthy, defined as the science and engineering of making intelligent machines, and encompasses various domains including machine learning, natural language processing, and computer vision.', "AI is structured along three evolutionary stages: artificial narrow intelligence (weak AI), artificial general intelligence (strong AI), and artificial superintelligence, with examples such as Alexa representing weak AI and the concerns surrounding strong AI's potential impact on humanity.", 'Artificial superintelligence, a hypothetical situation where computer capabilities surpass humans, is considered a distant concept, but there are predictions by tech experts like Elon Musk that it may occur by 2040, sparking debates about its potential impact on human civilization.', 'The chapter distinguishes between AI, machine learning, and deep learning, highlighting their interconnectedness and their roles in deriving useful insights from data, with AI covering a vast domain of fields including natural language processing, object detection, and robotics.', 'Machine learning, a subset of AI, enables machines to learn and improve from experience without explicit programming, utilizing algorithms to detect insights and trends in data, subsequently building models to solve problems.']}], 'duration': 1424.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng188120.jpg', 'highlights': ["Python's pre-built libraries save significant coding time for AI developers.", "Python's simplicity and readability make it the easiest language for AI.", "Python's platform independence ensures smooth project execution.", "Python's massive community support is evidenced by its exponential growth.", 'TensorFlow is crucial for AI with wide usage in Google applications.', 'Scikit-learn is essential for working with complex data and model accuracy.', 'Numpy simplifies complex mathematical implementations for AI.', 'Theano allows for faster data-intensive computations in AI.', 'Keras provides functionalities for compiling models and implementing neural networks.', "AI's surge in popularity is due to more computational power and better algorithms.", 'The Natural Language Toolkit is an open source Python library for text analysis.', 'AI encompasses machine learning, natural language processing, and computer vision.', 'AI is structured along three evolutionary stages: narrow intelligence, general intelligence, and superintelligence.', 'Artificial superintelligence is a distant concept with potential impact on humanity.', 'AI, machine learning, and deep learning are interconnected in deriving insights from data.']}, {'end': 2054.772, 'segs': [{'end': 1809.628, 'src': 'embed', 'start': 1774.858, 'weight': 1, 'content': [{'end': 1777.219, 'text': 'There are thousands of data resources on the web.', 'start': 1774.858, 'duration': 2.361}, {'end': 1780.24, 'text': 'You can just download the data set and you can get going.', 'start': 1777.359, 'duration': 2.881}, {'end': 1790.143, 'text': 'Coming back to the problem at hand, the data needed for weather forecasting includes measures such as humidity level, your temperature, the pressure,', 'start': 1780.92, 'duration': 9.223}, {'end': 1794.485, 'text': 'the locality, whether or not you live in a hill station, and so on.', 'start': 1790.143, 'duration': 4.342}, {'end': 1798.706, 'text': 'Such data must be collected and it has to be stored for analysis.', 'start': 1795.205, 'duration': 3.501}, {'end': 1800.807, 'text': 'This is where you collect all the data.', 'start': 1799.186, 'duration': 1.621}, {'end': 1809.628, 'text': 'Now moving on to step number three is data preparation the data that you collected is almost never in the right format.', 'start': 1801.403, 'duration': 8.225}], 'summary': 'Thousands of web data resources. weather forecasting data includes humidity, temperature, pressure, and locality. data preparation is crucial.', 'duration': 34.77, 'max_score': 1774.858, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1774858.jpg'}, {'end': 1878.341, 'src': 'embed', 'start': 1839.524, 'weight': 3, 'content': [{'end': 1846.066, 'text': 'Therefore at this stage you can scan the entire data set for any missing values and you have to fix them here itself.', 'start': 1839.524, 'duration': 6.542}, {'end': 1851.188, 'text': 'Now actually this is one of the most time-consuming steps in a machine learning process.', 'start': 1846.967, 'duration': 4.221}, {'end': 1858.186, 'text': 'If you ask a data scientist which step he hates the most or which step is the most time consuming,', 'start': 1851.741, 'duration': 6.445}, {'end': 1860.928, 'text': "they're probably going to tell you data processing and data cleaning.", 'start': 1858.186, 'duration': 2.742}, {'end': 1866.212, 'text': "It's one of the most tiresome tasks because you need to look at all the values that are there.", 'start': 1861.528, 'duration': 4.684}, {'end': 1870.475, 'text': 'You need to find any missing values, any data that is not relevant to you.', 'start': 1866.232, 'duration': 4.243}, {'end': 1874.478, 'text': 'All of this has to be removed such that you can analyze the data in a better way.', 'start': 1870.555, 'duration': 3.923}, {'end': 1878.341, 'text': 'Now step number four is exploratory data analysis.', 'start': 1875.118, 'duration': 3.223}], 'summary': 'Data cleaning is time-consuming; exploratory data analysis is next step.', 'duration': 38.817, 'max_score': 1839.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1839524.jpg'}, {'end': 1948.13, 'src': 'embed', 'start': 1904.221, 'weight': 0, 'content': [{'end': 1911.886, 'text': 'For example, in the case of predicting rainfall, we know that there is a strong possibility of rain if the temperature has fallen low.', 'start': 1904.221, 'duration': 7.665}, {'end': 1915.969, 'text': 'Such correlations have to be understood and mapped at this stage.', 'start': 1912.467, 'duration': 3.502}, {'end': 1923.215, 'text': 'ADA is actually the most important step in a machine learning process because here is where you understand your data.', 'start': 1916.79, 'duration': 6.425}, {'end': 1927.718, 'text': 'You understand how your data is going to help you predict the outcome.', 'start': 1923.735, 'duration': 3.983}, {'end': 1932.201, 'text': 'Moving on to step number five, we have building a machine learning model.', 'start': 1928.458, 'duration': 3.743}, {'end': 1941.586, 'text': 'So all the insights and all the patterns that you got from your data exploration stage those insights are used to build the machine learning model.', 'start': 1932.842, 'duration': 8.744}, {'end': 1948.13, 'text': 'So this stage always begins by splitting the data set into two parts that is training and testing data.', 'start': 1942.127, 'duration': 6.003}], 'summary': 'Understanding data correlations is crucial in machine learning. data is split into training and testing sets in the model-building phase.', 'duration': 43.909, 'max_score': 1904.221, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1904221.jpg'}, {'end': 1995.125, 'src': 'embed', 'start': 1971.455, 'weight': 2, 'content': [{'end': 1978.558, 'text': 'First, we focus on what the machine learning process is, but anyway, choosing the right algorithm will depend on several factors.', 'start': 1971.455, 'duration': 7.103}, {'end': 1985.042, 'text': "It depends on the type of problem you're trying to solve, the data set, and the level of complexity of the problem.", 'start': 1979.079, 'duration': 5.963}, {'end': 1990.623, 'text': "In the upcoming sections, we'll discuss all the different types of problems that can be solved by using machine learning.", 'start': 1985.78, 'duration': 4.843}, {'end': 1995.125, 'text': 'Moving on to step number six, we have model evaluation and optimization.', 'start': 1991.163, 'duration': 3.962}], 'summary': 'Machine learning process depends on problem type, data set, and complexity. various problems can be solved using machine learning.', 'duration': 23.67, 'max_score': 1971.455, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1971455.jpg'}], 'start': 1612.593, 'title': 'Steps in machine learning', 'summary': 'Explains the seven key steps of the machine learning process, including defining the objective, data gathering, data preparation, exploration, model building, evaluation, and predictions, with an emphasis on their impact on accuracy and efficiency. it uses the example of predicting rain occurrence.', 'chapters': [{'end': 1774.061, 'start': 1612.593, 'title': 'Machine learning process', 'summary': 'Explains the seven steps of the machine learning process, starting with defining the objective and data gathering, and then moving on to data preparation, exploration, model building, evaluation, and predictions, using the example of predicting rain occurrence.', 'duration': 161.468, 'highlights': ['The machine learning process involves seven steps, including defining the objective, data gathering, data preparation, exploration, model building, evaluation, and predictions.', 'An example of predicting rain occurrence is used to illustrate the machine learning process, emphasizing the importance of understanding the problem, defining target features, and gathering the required data.', 'Understanding the type of problem (e.g., binary classification or clustering) and the data needed to solve it is crucial in the machine learning process.', 'Data collection methods such as manual collection and web scraping are mentioned, with reassurance that beginners need not worry about obtaining the data.']}, {'end': 2054.772, 'start': 1774.858, 'title': 'Steps in machine learning process', 'summary': 'Discusses the key steps in the machine learning process, including data collection, data preparation, exploratory data analysis, building a machine learning model, model evaluation, and optimization, emphasizing the importance of each step and its impact on the accuracy and efficiency of the model.', 'duration': 279.914, 'highlights': ['Data preparation involves scanning the entire data set for any missing values and fixing inconsistencies, which is one of the most time-consuming steps in a machine learning process.', 'Exploratory data analysis is crucial as it involves understanding the patterns and the trends in the data, drawing useful insights, and mapping correlations between variables to help predict outcomes.', 'Building a machine learning model begins by splitting the data set into training and testing data, and the choice of algorithm depends on the type of problem, data set, and complexity.', 'Model evaluation and optimization involve testing the efficiency of the model using the testing data set, calculating accuracy, and implementing methods like parameter tuning and cross-validation to improve performance.']}], 'duration': 442.179, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng1612593.jpg', 'highlights': ['Data preparation involves scanning the entire data set for any missing values and fixing inconsistencies, a time-consuming step.', 'Exploratory data analysis is crucial for understanding patterns, trends, and mapping correlations.', 'Model evaluation involves testing efficiency using the testing data set and implementing methods like parameter tuning and cross-validation.', 'Understanding the type of problem and the data needed to solve it is crucial in the machine learning process.', 'Building a machine learning model begins by splitting the data set into training and testing data, and the choice of algorithm depends on the type of problem, data set, and complexity.', 'An example of predicting rain occurrence is used to illustrate the machine learning process, emphasizing the importance of understanding the problem and defining target features.', 'Data collection methods such as manual collection and web scraping are mentioned, reassuring beginners about obtaining the data.']}, {'end': 2786.11, 'segs': [{'end': 2102.32, 'src': 'embed', 'start': 2076.592, 'weight': 5, 'content': [{'end': 2081.237, 'text': 'because obviously you are training and building the model by using the training data set.', 'start': 2076.592, 'duration': 4.645}, {'end': 2085.261, 'text': 'the testing data set is just for evaluating the performance of your model.', 'start': 2081.237, 'duration': 4.024}, {'end': 2090.233, 'text': "Now let's move on and understand step number seven, which is predictions.", 'start': 2085.989, 'duration': 4.244}, {'end': 2096.717, 'text': "Now once the model is evaluated and you've improved the model, it is finally used to make predictions.", 'start': 2090.753, 'duration': 5.964}, {'end': 2102.32, 'text': 'The final output can be a categorical variable or it can be a continuous quantity.', 'start': 2097.377, 'duration': 4.943}], 'summary': 'Model is trained using training data, evaluated with testing data, and used for predictions.', 'duration': 25.728, 'max_score': 2076.592, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2076592.jpg'}, {'end': 2212.454, 'src': 'embed', 'start': 2179.306, 'weight': 4, 'content': [{'end': 2181.527, 'text': "So first, let's discuss supervised learning.", 'start': 2179.306, 'duration': 2.221}, {'end': 2190.177, 'text': 'So what is supervised learning? Supervised learning is a technique in which we teach or train the machine by using data which is labeled.', 'start': 2182.227, 'duration': 7.95}, {'end': 2193.78, 'text': "To understand this better, let's consider an analogy.", 'start': 2190.758, 'duration': 3.022}, {'end': 2197.843, 'text': 'As kids, we all needed guidance to solve math problems.', 'start': 2194.501, 'duration': 3.342}, {'end': 2201.106, 'text': 'At least I had a really tough time solving math problems.', 'start': 2198.384, 'duration': 2.722}, {'end': 2206.57, 'text': 'So our teachers always helped us understand what addition is and how it is done.', 'start': 2201.966, 'duration': 4.604}, {'end': 2212.454, 'text': 'Similarly, you can think of supervised learning as a type of machine learning that involves a guide.', 'start': 2207.19, 'duration': 5.264}], 'summary': 'Supervised learning is teaching machines using labeled data, similar to how teachers guide kids in learning math.', 'duration': 33.148, 'max_score': 2179.306, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2179306.jpg'}, {'end': 2370.483, 'src': 'embed', 'start': 2333.945, 'weight': 0, 'content': [{'end': 2339.769, 'text': 'It figures out patterns and the differences between Tom and Jerry on its own by taking in tons of data.', 'start': 2333.945, 'duration': 5.824}, {'end': 2349.316, 'text': 'For example, it identifies prominent features of Tom such as pointy ears, bigger in size and so on to understand that this image is of type one.', 'start': 2340.269, 'duration': 9.047}, {'end': 2356.218, 'text': 'Similarly, it finds such features in Jerry and knows that this is another type of image, maybe type two.', 'start': 2350.029, 'duration': 6.189}, {'end': 2362.186, 'text': 'Therefore, it classifies the images into two different clusters without knowing who is Tom and who is Jerry.', 'start': 2356.338, 'duration': 5.848}, {'end': 2370.483, 'text': 'Now the main idea behind unsupervised learning is to understand the patterns in your data set and form clusters based on feature similarity.', 'start': 2362.737, 'duration': 7.746}], 'summary': 'Unsupervised learning identifies patterns and clusters images based on feature similarity, classifying them into two types without prior knowledge of tom or jerry.', 'duration': 36.538, 'max_score': 2333.945, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2333945.jpg'}, {'end': 2425.374, 'src': 'embed', 'start': 2394.325, 'weight': 6, 'content': [{'end': 2397.148, 'text': "It's pretty different from supervised and unsupervised.", 'start': 2394.325, 'duration': 2.823}, {'end': 2411.321, 'text': 'It is basically a part of machine learning where you put an agent in an environment and this agent learns to behave in the environment by performing certain actions and observing the rewards which it gets from these actions.', 'start': 2397.848, 'duration': 13.473}, {'end': 2417.749, 'text': 'To understand reinforcement learning, imagine that you were dropped off at an isolated island.', 'start': 2412.025, 'duration': 5.724}, {'end': 2425.374, 'text': "What would you do? Initially, we'd all panic, right? But as time passes by, you will learn how to live on the island.", 'start': 2418.149, 'duration': 7.225}], 'summary': 'Reinforcement learning is an aspect of machine learning where an agent learns to behave in an environment by performing actions and observing rewards, akin to learning to survive on an isolated island.', 'duration': 31.049, 'max_score': 2394.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2394325.jpg'}, {'end': 2611.874, 'src': 'embed', 'start': 2584.293, 'weight': 1, 'content': [{'end': 2591.098, 'text': 'Popular algorithms under supervised learning are linear regression, logistic regression, support vector machines and so on.', 'start': 2584.293, 'duration': 6.805}, {'end': 2595.522, 'text': 'Under unsupervised learning, we have the famous key means clustering algorithm.', 'start': 2591.739, 'duration': 3.783}, {'end': 2601.847, 'text': 'Under reinforcement learning, we have the Q learning algorithm which is one of the most important algorithms.', 'start': 2596.002, 'duration': 5.845}, {'end': 2605.73, 'text': 'It is basically the logic behind the famous AlphaGo game.', 'start': 2602.327, 'duration': 3.403}, {'end': 2607.211, 'text': "I'm sure all of you have heard of that.", 'start': 2605.87, 'duration': 1.341}, {'end': 2611.874, 'text': 'So guys, these were the differences between supervised, unsupervised, and reinforcement learning.', 'start': 2607.871, 'duration': 4.003}], 'summary': 'Popular supervised learning algorithms are linear regression, logistic regression, support vector machines. in unsupervised learning, k-means clustering is famous. reinforcement learning includes the important q learning algorithm, the logic behind the famous alphago game.', 'duration': 27.581, 'max_score': 2584.293, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2584293.jpg'}], 'start': 2055.473, 'title': 'Machine learning process, types, and applications', 'summary': 'Delves into the machine learning process, focusing on supervised learning and covers the types of machine learning - supervised, unsupervised, and reinforcement learning - along with their characteristics, applications, and specific algorithms.', 'chapters': [{'end': 2268.964, 'start': 2055.473, 'title': 'Machine learning process and supervised learning', 'summary': 'Explains the machine learning process, emphasizing the concepts of training and testing data sets, predictions, and the focus on supervised learning, which involves training the machine using labeled data to understand patterns.', 'duration': 213.491, 'highlights': ['The training data set is always larger in size when compared to the testing data set.', 'Supervised learning involves training the machine by using data which is labeled, acting as a guide for the machine to understand the patterns in the data.', 'The output of the machine learning model can be a categorical variable or a continuous quantity, depending on the type of problem being solved.', 'Three approaches to machine learning are discussed: supervised learning, unsupervised learning, and reinforcement learning.']}, {'end': 2786.11, 'start': 2269.671, 'title': 'Types of machine learning', 'summary': 'Covers the three types of machine learning: supervised, unsupervised, and reinforcement learning, with emphasis on their characteristics, applications, and specific algorithms, providing a comprehensive understanding of each type.', 'duration': 516.439, 'highlights': ['Reinforcement learning involves an agent learning to behave in an environment by performing actions and observing the rewards, used in areas such as self-driving cars and AlphaGo.', 'Unsupervised learning allows the model to learn without guidance by identifying patterns and differences in unlabeled data, forming clusters based on feature similarity.', 'Supervised learning involves training the machine using labeled data to produce a labeled output, used for regression and classification problems with algorithms like support vector machines and logistic regression.', 'The main differences between supervised, unsupervised, and reinforcement learning lie in the type of data used, the presence of external supervision, and the approach to solving problems.', 'Regression problems involve predicting continuous quantities, classification problems involve categorizing data, and clustering problems involve grouping data based on feature similarity, each solved using specific algorithms under supervised and unsupervised learning.']}], 'duration': 730.637, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2055473.jpg', 'highlights': ['Supervised learning involves training the machine by using labeled data to produce a labeled output.', 'Reinforcement learning involves an agent learning to behave in an environment by performing actions and observing the rewards.', 'Unsupervised learning allows the model to learn without guidance by identifying patterns and differences in unlabeled data.', 'The training data set is always larger in size when compared to the testing data set.', 'The output of the machine learning model can be a categorical variable or a continuous quantity, depending on the type of problem being solved.', 'Three approaches to machine learning are discussed: supervised learning, unsupervised learning, and reinforcement learning.', 'The main differences between supervised, unsupervised, and reinforcement learning lie in the type of data used, the presence of external supervision, and the approach to solving problems.', 'Regression problems involve predicting continuous quantities, classification problems involve categorizing data, and clustering problems involve grouping data based on feature similarity.']}, {'end': 3517.388, 'segs': [{'end': 2817.888, 'src': 'embed', 'start': 2786.43, 'weight': 0, 'content': [{'end': 2789.433, 'text': 'You can check out the entire content of reinforcement learning there.', 'start': 2786.43, 'duration': 3.003}, {'end': 2796.224, 'text': 'Now, regression problems can be solved by using linear regression algorithms such as linear regression.', 'start': 2790.077, 'duration': 6.147}, {'end': 2803.512, 'text': 'decision trees and random forests can also be used in regression problems, but usually decision trees and random forests.', 'start': 2796.224, 'duration': 7.288}, {'end': 2805.955, 'text': 'all of these are used to solve classification problems.', 'start': 2803.512, 'duration': 2.443}, {'end': 2810.265, 'text': 'Famous classification algorithms include K nearest neighbor,', 'start': 2806.584, 'duration': 3.681}, {'end': 2817.888, 'text': 'which is basically KNN decision trees and random forest logistic regression naive bias support vector machines.', 'start': 2810.265, 'duration': 7.623}], 'summary': 'Reinforcement learning, linear regression, decision trees, and random forests are used for solving classification and regression problems. famous algorithms include knn, decision trees, random forest, logistic regression, naive bias, and support vector machines.', 'duration': 31.458, 'max_score': 2786.43, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2786430.jpg'}, {'end': 2955.309, 'src': 'heatmap', 'start': 2885.933, 'weight': 0.929, 'content': [{'end': 2889.377, 'text': "So I'm using PyCharm in order to run the demo.", 'start': 2885.933, 'duration': 3.444}, {'end': 2894.302, 'text': "So guys, like I said, if you don't know Python, I'll leave a couple of links in the description box.", 'start': 2890.498, 'duration': 3.804}, {'end': 2896.885, 'text': 'You can go through those videos as well.', 'start': 2894.783, 'duration': 2.102}, {'end': 2906.176, 'text': 'The main aim of our demo is to build a machine learning model that will predict whether or not it will rain tomorrow by studying the past data set.', 'start': 2897.406, 'duration': 8.77}, {'end': 2915.778, 'text': 'Now this data set contains around 145, 000 observations on the daily weather conditions as observed in Australia night.', 'start': 2906.792, 'duration': 8.986}, {'end': 2923.984, 'text': 'The data set has around 24 features and we will be using 23 features out of that to predict the target variable, which is rain tomorrow.', 'start': 2915.798, 'duration': 8.186}, {'end': 2926.939, 'text': 'So this data set I collected from Kaggle.', 'start': 2924.657, 'duration': 2.282}, {'end': 2932.523, 'text': "For those of you who don't know, Kaggle is an online platform where you can find hundreds of data sets,", 'start': 2927.399, 'duration': 5.124}, {'end': 2937.166, 'text': 'and there are a lot of competitions held by machine learning engineers and all of that.', 'start': 2932.523, 'duration': 4.643}, {'end': 2939.168, 'text': "It's an interesting website.", 'start': 2937.887, 'duration': 1.281}, {'end': 2946.373, 'text': 'Now the problem statement itself is to build a machine learning model that will predict whether or not it will rain tomorrow.', 'start': 2939.888, 'duration': 6.485}, {'end': 2949.124, 'text': 'This is clearly a classification problem.', 'start': 2947.003, 'duration': 2.121}, {'end': 2955.309, 'text': 'The machine learning model has to classify the output into two classes that is either yes or no.', 'start': 2949.545, 'duration': 5.764}], 'summary': 'Using pycharm, building a machine learning model to predict rain tomorrow from a dataset of 145,000 observations with 24 features, collected from kaggle.', 'duration': 69.376, 'max_score': 2885.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2885933.jpg'}, {'end': 3073.021, 'src': 'embed', 'start': 3044.895, 'weight': 3, 'content': [{'end': 3047.556, 'text': 'So rain tomorrow is basically my target variable.', 'start': 3044.895, 'duration': 2.661}, {'end': 3051.377, 'text': "I'll be finding out whether it's going to rain tomorrow or not.", 'start': 3048.216, 'duration': 3.161}, {'end': 3054.858, 'text': 'So this is my target variable, also known as your output variable.', 'start': 3051.417, 'duration': 3.441}, {'end': 3058.819, 'text': 'My input variables will be the other 23 variables.', 'start': 3055.478, 'duration': 3.341}, {'end': 3064.735, 'text': 'Date, location, minimum temperature, rain today, risk all of this will be my input variables.', 'start': 3059.259, 'duration': 5.476}, {'end': 3068.057, 'text': 'Now these variables are also known as predictor variables.', 'start': 3065.255, 'duration': 2.802}, {'end': 3070.519, 'text': "Basically they're used to predict your outcome.", 'start': 3068.337, 'duration': 2.182}, {'end': 3073.021, 'text': 'So these are also known as predictor variables.', 'start': 3071.079, 'duration': 1.942}], 'summary': 'Target variable is rain tomorrow; input variables include 23 predictors like date, location, minimum temperature, and rain today.', 'duration': 28.126, 'max_score': 3044.895, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng3044895.jpg'}, {'end': 3260.832, 'src': 'heatmap', 'start': 3193.674, 'weight': 0.735, 'content': [{'end': 3200.618, 'text': "Then if you print the shape of your data frame, we'll have around 112, 000 rows with 17 variables.", 'start': 3193.674, 'duration': 6.944}, {'end': 3207.382, 'text': 'This is the shape of the data set after removing all the null values and all the redundant or unnecessary variables.', 'start': 3201.218, 'duration': 6.164}, {'end': 3210.728, 'text': "Now it's time to remove the outliers in the data.", 'start': 3208.126, 'duration': 2.602}, {'end': 3215.772, 'text': 'So after you remove any null values, we should also check our dataset for any outliers.', 'start': 3211.229, 'duration': 4.543}, {'end': 3221.777, 'text': 'An outlier is a data point that is very different from your other observations.', 'start': 3216.393, 'duration': 5.384}, {'end': 3226.801, 'text': 'Outliers usually occur because of miscalculations while collecting the data.', 'start': 3222.578, 'duration': 4.223}, {'end': 3229.404, 'text': 'These are some sort of errors in your dataset.', 'start': 3227.382, 'duration': 2.022}, {'end': 3234.268, 'text': "So in this whole code snippet, we're just getting rid of outliers.", 'start': 3230.104, 'duration': 4.164}, {'end': 3236.97, 'text': 'This is the output that we get.', 'start': 3235.629, 'duration': 1.341}, {'end': 3238.535, 'text': 'all our outliers.', 'start': 3237.654, 'duration': 0.881}, {'end': 3245.42, 'text': "Next what we'll be doing is we will be assigning zeros and ones in the place of yes and no.", 'start': 3239.415, 'duration': 6.005}, {'end': 3250.524, 'text': "The only thing is we're going to change the categorical variables from yes and no to zero and one.", 'start': 3246.041, 'duration': 4.483}, {'end': 3252.826, 'text': "That's exactly what we're doing over here.", 'start': 3251.125, 'duration': 1.701}, {'end': 3260.832, 'text': "Now if there are any unique values such as any character values which are not supposed to be there, we'll be changing them into integer values.", 'start': 3253.566, 'duration': 7.266}], 'summary': 'Data frame has 112,000 rows and 17 variables. outliers and null values removed.', 'duration': 67.158, 'max_score': 3193.674, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng3193674.jpg'}, {'end': 3226.801, 'src': 'embed', 'start': 3201.218, 'weight': 2, 'content': [{'end': 3207.382, 'text': 'This is the shape of the data set after removing all the null values and all the redundant or unnecessary variables.', 'start': 3201.218, 'duration': 6.164}, {'end': 3210.728, 'text': "Now it's time to remove the outliers in the data.", 'start': 3208.126, 'duration': 2.602}, {'end': 3215.772, 'text': 'So after you remove any null values, we should also check our dataset for any outliers.', 'start': 3211.229, 'duration': 4.543}, {'end': 3221.777, 'text': 'An outlier is a data point that is very different from your other observations.', 'start': 3216.393, 'duration': 5.384}, {'end': 3226.801, 'text': 'Outliers usually occur because of miscalculations while collecting the data.', 'start': 3222.578, 'duration': 4.223}], 'summary': 'Data set cleaned, outliers to be removed for accurate analysis.', 'duration': 25.583, 'max_score': 3201.218, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng3201218.jpg'}, {'end': 3486.241, 'src': 'embed', 'start': 3454.733, 'weight': 4, 'content': [{'end': 3456.393, 'text': "That's exactly what we're doing over here.", 'start': 3454.733, 'duration': 1.66}, {'end': 3464.495, 'text': 'So 25% of your data is assigned for the testing data and the remaining 75% is your training data here.', 'start': 3456.974, 'duration': 7.521}, {'end': 3467.736, 'text': "You're creating the instance of the logistic regression algorithm.", 'start': 3464.515, 'duration': 3.221}, {'end': 3473.077, 'text': "This is an instance that you created then you'll fit the model by using your training data set.", 'start': 3468.196, 'duration': 4.881}, {'end': 3477.438, 'text': "So basically to build your machine learning algorithm, you'll be fitting your training data set.", 'start': 3473.517, 'duration': 3.921}, {'end': 3486.241, 'text': 'So X train and Y train variables have your training data set After that, you will be evaluating the model by using your testing data set.', 'start': 3477.978, 'duration': 8.263}], 'summary': 'Creating a logistic regression algorithm with 25% testing data and 75% training data, then evaluating the model.', 'duration': 31.508, 'max_score': 3454.733, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng3454733.jpg'}], 'start': 2786.43, 'title': 'Machine learning models for rain prediction', 'summary': 'Covers reinforcement learning, regression, and classification algorithms, along with the process of building a machine learning model to predict rain, using a dataset of around 145,000 observations with 24 features, aiming for 84% accuracy using logistic regression.', 'chapters': [{'end': 2828.572, 'start': 2786.43, 'title': 'Reinforcement learning and classification algorithms', 'summary': 'Covers reinforcement learning and various algorithms for solving regression and classification problems, including linear regression, decision trees, random forests, k nearest neighbor, logistic regression, naive bias, support vector machines, clustering, and association analysis.', 'duration': 42.142, 'highlights': ['The chapter covers reinforcement learning and various algorithms for solving regression and classification problems, including linear regression, decision trees, random forests, K nearest neighbor, logistic regression, naive bias, support vector machines, clustering, and Association analysis.', 'Linear regression, decision trees, and random forests can be used to solve regression problems.', 'The famous classification algorithms include K nearest neighbor, decision trees, random forest, logistic regression, naive bias, and support vector machines.', 'Clustering problems can be solved by using K means in unsupervised learning.']}, {'end': 3517.388, 'start': 2828.572, 'title': 'Machine learning model for rain prediction', 'summary': 'Discusses the process of building a machine learning model to predict whether it will rain tomorrow, using a dataset of around 145,000 observations with 24 features, aiming for a classification problem with the goal of achieving 84% accuracy using logistic regression.', 'duration': 688.816, 'highlights': ["The dataset contains around 145,000 observations with 24 features, and the target variable is 'rain tomorrow' aiming at a classification problem.", 'Pre-processing involves removing null values, unnecessary variables, and outliers, resulting in around 112,000 rows with 17 variables, and normalizing the dataset to avoid biasness in the output.', "Exploratory data analysis involves using the 'select k best' function to identify the most significant predictor variables, where only one significant variable, 'humidity level', is selected as the input.", 'The process includes data modeling using logistic regression, random forest classifier, decision tree classifier, and support vector machine to achieve an accuracy of approximately 84% using logistic regression.']}], 'duration': 730.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng2786430.jpg', 'highlights': ['The dataset contains around 145,000 observations with 24 features, aiming at a classification problem.', 'The process includes data modeling using logistic regression, random forest classifier, decision tree classifier, and support vector machine to achieve an accuracy of approximately 84% using logistic regression.', 'Pre-processing involves removing null values, unnecessary variables, and outliers, resulting in around 112,000 rows with 17 variables, and normalizing the dataset to avoid biasness in the output.', 'The chapter covers reinforcement learning and various algorithms for solving regression and classification problems, including linear regression, decision trees, random forests, K nearest neighbor, logistic regression, naive bias, support vector machines, clustering, and Association analysis.', "Exploratory data analysis involves using the 'select k best' function to identify the most significant predictor variables, where only one significant variable, 'humidity level', is selected as the input."]}, {'end': 4493.857, 'segs': [{'end': 3738.489, 'src': 'embed', 'start': 3715.203, 'weight': 5, 'content': [{'end': 3723.569, 'text': "you're selecting the important features for the machine learning model and you're telling them like these are the important features and this is what you should use in order to build the model.", 'start': 3715.203, 'duration': 8.366}, {'end': 3726.171, 'text': 'This process is known as feature extraction.', 'start': 3724.189, 'duration': 1.982}, {'end': 3729.127, 'text': 'Now in machine learning, this is a manual process.', 'start': 3726.806, 'duration': 2.321}, {'end': 3731.707, 'text': "You're going to manually input as a programmer.", 'start': 3729.147, 'duration': 2.56}, {'end': 3738.489, 'text': "You're going to tell that these are the important predictor variables, but what happens when your data set has hundreds of variables?", 'start': 3731.747, 'duration': 6.742}], 'summary': 'Feature extraction in machine learning involves manually selecting important predictor variables, a challenge with large datasets.', 'duration': 23.286, 'max_score': 3715.203, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng3715203.jpg'}, {'end': 4241.666, 'src': 'embed', 'start': 4194.591, 'weight': 0, 'content': [{'end': 4197.852, 'text': 'This X1, X2 till Xn basically stands for input.', 'start': 4194.591, 'duration': 3.261}, {'end': 4205.393, 'text': 'W1, W2 till Wn stands for the weight assigned to each of these inputs.', 'start': 4198.772, 'duration': 6.621}, {'end': 4210.914, 'text': "There is a specific weight that'll be randomly initialized in the beginning for each of your input.", 'start': 4205.513, 'duration': 5.401}, {'end': 4213.995, 'text': 'Next you have something known as the summation element.', 'start': 4211.655, 'duration': 2.34}, {'end': 4221.755, 'text': 'Here what you do is you multiply the respective input with the respective bit and you add all these products.', 'start': 4214.611, 'duration': 7.144}, {'end': 4224.316, 'text': 'That is basically your summation function.', 'start': 4222.475, 'duration': 1.841}, {'end': 4228.799, 'text': 'After this is what is your transfer function also known as activation function.', 'start': 4224.877, 'duration': 3.922}, {'end': 4233.862, 'text': 'The activation function will basically map your input to your desired output.', 'start': 4229.539, 'duration': 4.323}, {'end': 4237.003, 'text': 'So your input will go through these processes.', 'start': 4234.422, 'duration': 2.581}, {'end': 4241.666, 'text': "It'll go through summation and activation function in order to get to the output.", 'start': 4237.544, 'duration': 4.122}], 'summary': 'Inputs are weighted, summed, and passed through activation function to produce output.', 'duration': 47.075, 'max_score': 4194.591, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng4194591.jpg'}, {'end': 4304.633, 'src': 'embed', 'start': 4281.047, 'weight': 4, 'content': [{'end': 4289.349, 'text': 'The neurons become active in our brain after a certain potential is reached that threshold is known as the activation potential.', 'start': 4281.047, 'duration': 8.302}, {'end': 4294.09, 'text': 'So mathematically, there are a few functions which represent the activation function.', 'start': 4290.069, 'duration': 4.021}, {'end': 4298.872, 'text': 'Basically the signum the sigmoid the tanh all of these are activation functions.', 'start': 4294.53, 'duration': 4.342}, {'end': 4304.633, 'text': 'You can think of activation function as a function that maps the input to the respective output.', 'start': 4299.452, 'duration': 5.181}], 'summary': 'Neurons in the brain become active after reaching a certain potential, known as the activation potential, which can be represented by functions like signum, sigmoid, and tanh.', 'duration': 23.586, 'max_score': 4281.047, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng4281047.jpg'}, {'end': 4378.772, 'src': 'embed', 'start': 4351.511, 'weight': 2, 'content': [{'end': 4355.912, 'text': "There'll only be an input layer, an output layer, and a couple of transformation functions in between.", 'start': 4351.511, 'duration': 4.401}, {'end': 4358.222, 'text': "That's all will be there in a perceptron.", 'start': 4356.621, 'duration': 1.601}, {'end': 4362.264, 'text': 'Now a perceptron, like I mentioned, is used to solve only linear problems.', 'start': 4358.882, 'duration': 3.382}, {'end': 4369.067, 'text': 'If you look at this data distribution, how do you think we can solve this? This data is not linearly separable.', 'start': 4362.784, 'duration': 6.283}, {'end': 4372.929, 'text': 'So you cannot use a single layer perceptron to separate this data.', 'start': 4369.507, 'duration': 3.422}, {'end': 4378.772, 'text': "That's why we need something known as a multi-layer perceptron with back propagation.", 'start': 4373.689, 'duration': 5.083}], 'summary': 'Perceptron has input, output layers, used for linear problems. requires multi-layer perceptron for non-linear data.', 'duration': 27.261, 'max_score': 4351.511, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng4351511.jpg'}], 'start': 3517.789, 'title': 'Machine learning and neural networks', 'summary': 'Covers the performance of classification algorithms with an accuracy of approximately 83-84%, discusses the limitations of machine learning, introduces deep learning as a solution, and explains the role of perceptrons and neural networks in solving complex, non-linear problems.', 'chapters': [{'end': 3601.657, 'start': 3517.789, 'title': 'Classification algorithm performance', 'summary': 'Covers the implementation of random forest, decision tree, and support vector machine classifiers, achieving an accuracy of approximately 84% for random forest, 83-84% for decision tree, and support vector machine.', 'duration': 83.868, 'highlights': ['The accuracy using random forest is again approximately 84%, which is a really good number.', 'Again, we have an accuracy of around 83 to 84% when using the decision tree classifier.', 'Finally, printing the accuracy, achieving a good performance across the three classifiers.']}, {'end': 4171.781, 'start': 3602.509, 'title': 'Machine learning process and limitations', 'summary': 'Explains the machine learning process, with an accuracy score of approximately 84% to 83% from classification models, and highlights the limitations of machine learning including handling high dimensional data and the manual process of feature extraction. it then introduces deep learning as a solution to these problems, explaining its capability to handle high dimensional data and perform automated feature extraction, and provides real-world use cases of deep learning in paypal and facebook.', 'duration': 569.272, 'highlights': ['The machine learning process involves data import, pre-processing, exploratory data analysis, model building, and evaluation with an accuracy score of approximately 84% to 83% from classification models.', 'Machine learning limitations include handling high dimensional data, manual feature extraction, and inability to be used in image recognition and object detection.', 'Deep learning overcomes machine learning limitations by automatically handling high dimensional data and performing feature extraction, and is used in real-world cases like fraud detection in PayPal and face verification in Facebook.']}, {'end': 4493.857, 'start': 4172.562, 'title': 'Understanding perceptrons and neural networks', 'summary': 'Explains the structure and functioning of a perceptron, its role in neural networks, the need for weights and biases, and the transition to multi-layer perceptrons for solving complex, non-linear problems.', 'duration': 321.295, 'highlights': ['The activation function maps input to output, such as the signum, sigmoid, and tanh functions, and plays a crucial role in neural networks, ensuring the correct threshold for neuron activation.', 'Multi-layer perceptrons with hidden layers are essential for solving complex, non-linear problems and high-dimensional data, as a single layer perceptron is limited to solving linear problems.', 'The importance of assigning weights to inputs lies in representing the strength and significance of each input for predicting the output, and the need to update weights to reduce errors and improve precision in neural networks.', 'A perceptron comprises inputs, weights, summation, and activation functions, and serves as the fundamental building block for understanding the functioning of neural networks.']}], 'duration': 976.068, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng3517789.jpg', 'highlights': ['Deep learning overcomes machine learning limitations by automatically handling high dimensional data and performing feature extraction, and is used in real-world cases like fraud detection in PayPal and face verification in Facebook.', 'The accuracy using random forest is again approximately 84%, which is a really good number.', 'Again, we have an accuracy of around 83 to 84% when using the decision tree classifier.', 'The machine learning process involves data import, pre-processing, exploratory data analysis, model building, and evaluation with an accuracy score of approximately 84% to 83% from classification models.', 'The activation function maps input to output, such as the signum, sigmoid, and tanh functions, and plays a crucial role in neural networks, ensuring the correct threshold for neuron activation.', 'Multi-layer perceptrons with hidden layers are essential for solving complex, non-linear problems and high-dimensional data, as a single layer perceptron is limited to solving linear problems.']}, {'end': 5212.284, 'segs': [{'end': 4785.804, 'src': 'embed', 'start': 4701.193, 'weight': 0, 'content': [{'end': 4707.055, 'text': "I'm not going to go into depth of what these features stand for because this demo is all about understanding deep learning.", 'start': 4701.193, 'duration': 5.862}, {'end': 4713.098, 'text': 'Now these V1, V2, V3, these are all predictor variables which will help us predict our class.', 'start': 4707.656, 'duration': 5.442}, {'end': 4716.113, 'text': "So guys don't worry about what these features are.", 'start': 4713.911, 'duration': 2.202}, {'end': 4724.883, 'text': 'These features are just information and details about your transaction such as the amount you spend or the time of transaction and so on.', 'start': 4716.574, 'duration': 8.309}, {'end': 4729.007, 'text': 'So here we have the amount variable which denotes the amount spent.', 'start': 4725.764, 'duration': 3.243}, {'end': 4731.63, 'text': 'After that we have the class variable.', 'start': 4729.708, 'duration': 1.922}, {'end': 4735.875, 'text': 'Now this class variable is your output variable or your target variable.', 'start': 4732.211, 'duration': 3.664}, {'end': 4738.879, 'text': 'So your class is basically your output variable.', 'start': 4736.497, 'duration': 2.382}, {'end': 4745.024, 'text': 'Value zero denotes that there has been no fraudulent activity, but if you get a class of one,', 'start': 4739.579, 'duration': 5.445}, {'end': 4748.506, 'text': 'it means that this transaction is a fraudulent transaction.', 'start': 4745.024, 'duration': 3.482}, {'end': 4751.669, 'text': 'For example, this transaction is not fraudulent.', 'start': 4749.027, 'duration': 2.642}, {'end': 4753.63, 'text': "That's why we have a value of zero over here.", 'start': 4751.769, 'duration': 1.861}, {'end': 4756.012, 'text': 'All right, so this is our data set.', 'start': 4754.191, 'duration': 1.821}, {'end': 4761.751, 'text': "Next. what we're doing is we're counting the number of samples for each class right?", 'start': 4757.99, 'duration': 3.761}, {'end': 4769.414, 'text': 'We have class 0 and class 1, where in class 0 denotes the normal transaction which is non fraudulent transaction,', 'start': 4761.791, 'duration': 7.623}, {'end': 4772.916, 'text': 'and class 1 will denote the fraudulent transactions right?', 'start': 4769.414, 'duration': 3.502}, {'end': 4781.299, 'text': 'So we have around 492 fraudulent transactions and around two hundred and eighty four thousand three hundred and fifteen non fraudulent transactions.', 'start': 4772.936, 'duration': 8.363}, {'end': 4785.804, 'text': 'So when you see this, you know that our data set is highly unbalanced.', 'start': 4782.102, 'duration': 3.702}], 'summary': 'Demo about deep learning for fraud detection with unbalanced dataset: 492 fraudulent transactions, 284,315 non-fraudulent transactions.', 'duration': 84.611, 'max_score': 4701.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng4701193.jpg'}, {'end': 4901.608, 'src': 'embed', 'start': 4874.689, 'weight': 2, 'content': [{'end': 4880.192, 'text': "We'll just randomly shuffle our data set right in order to remove any sort of biasness in the data.", 'start': 4874.689, 'duration': 5.503}, {'end': 4883.938, 'text': "After that, we'll split our data set into two parts.", 'start': 4881.016, 'duration': 2.922}, {'end': 4888.46, 'text': 'One is for training and your other data set is for testing.', 'start': 4884.218, 'duration': 4.242}, {'end': 4890.421, 'text': 'This is also known as data splicing.', 'start': 4888.56, 'duration': 1.861}, {'end': 4897.466, 'text': "Then we'd be splitting each data frame into feature and label, meaning that your input and your output.", 'start': 4891.642, 'duration': 5.824}, {'end': 4901.608, 'text': "You'll be doing this for your training data and for your testing data.", 'start': 4898.366, 'duration': 3.242}], 'summary': 'Data set shuffled to remove bias, split into training and testing sets, spliced into feature and label for both sets.', 'duration': 26.919, 'max_score': 4874.689, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng4874689.jpg'}], 'start': 4494.477, 'title': 'Neural network back propagation', 'summary': 'Discusses the importance of adjusting weights in model training to minimize error using back propagation, and provides a practical implementation example in python for detecting credit card fraud from an unbalanced dataset with 492 frauds out of approximately 285,000 transactions.', 'chapters': [{'end': 4533.705, 'start': 4494.477, 'title': 'Weight importance in model training', 'summary': 'Discusses the importance of changing weights in model training to minimize error, introducing the concept of back propagation to adjust weights based on error values.', 'duration': 39.228, 'highlights': ['Back propagation is a method used to train a model by adjusting weights based on error values.', 'Weight signifies the importance of a variable in model training.']}, {'end': 5212.284, 'start': 4533.705, 'title': 'Understanding back propagation and practical implementation', 'summary': 'Explains the concept of back propagation in neural networks, emphasizing the relationship between weight and error, and then details a practical implementation of deep learning using python, focusing on creating a high-performance model to detect credit card fraud from an unbalanced dataset with 492 frauds out of approximately 285,000 transactions.', 'duration': 678.579, 'highlights': ["The back propagation process involves continuously updating the weights to minimize the error, enhancing the precision of the neural network's output.", 'The dataset for credit card fraud detection contains 492 frauds out of approximately 285,000 transactions, resulting in an imbalance with the fraudulent class accounting for 0.172% of the total transactions.', 'Stratified sampling is employed to tackle the unbalanced dataset, ensuring a more balanced distribution of the classes for model training.', 'The practical implementation involves data preprocessing, including the use of the dropout method and normalization through min-max scaling to avoid bias in predictions.', 'The model creation process includes the utilization of three fully connected layers with the dropout technique to prevent overfitting, employing the ReLU activation function and the Adam optimizer, and fitting the model with 200 epochs and a batch size of 500 for training.']}], 'duration': 717.807, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng4494477.jpg', 'highlights': ['Back propagation involves continuously updating weights to minimize error', 'Stratified sampling is employed to tackle unbalanced dataset', 'Model creation process includes utilization of three fully connected layers', 'Dataset for credit card fraud detection contains 492 frauds out of approximately 285,000 transactions', 'Practical implementation involves data preprocessing and normalization through min-max scaling', 'Weight signifies the importance of a variable in model training', 'Back propagation is a method used to train a model by adjusting weights based on error values', 'The fraudulent class accounts for 0.172% of the total transactions', 'Utilization of the dropout technique to prevent overfitting in model creation process']}, {'end': 6131.632, 'segs': [{'end': 5275.999, 'src': 'embed', 'start': 5231.552, 'weight': 5, 'content': [{'end': 5235.014, 'text': "Here we'll be testing our model by using our testing data set.", 'start': 5231.552, 'duration': 3.462}, {'end': 5239.377, 'text': "Then we're finally printing the accuracy on our testing data set.", 'start': 5236.016, 'duration': 3.361}, {'end': 5243.898, 'text': "After that, we're just going to plot a heat map, which I'll be showing you all.", 'start': 5240.117, 'duration': 3.781}, {'end': 5245.479, 'text': 'Let me just show you the output.', 'start': 5244.098, 'duration': 1.381}, {'end': 5250.9, 'text': "So guys, in this entire line of code, all we're doing is we're printing an accuracy plot.", 'start': 5246.659, 'duration': 4.241}, {'end': 5253.201, 'text': "Basically, we're printing a heat map.", 'start': 5251.56, 'duration': 1.641}, {'end': 5255.461, 'text': "I'll show you what the heat map looks like.", 'start': 5253.761, 'duration': 1.7}, {'end': 5258.102, 'text': 'This is just to check the accuracy.', 'start': 5256.482, 'duration': 1.62}, {'end': 5264.176, 'text': "We're comparing all the correctly predicted values to our incorrectly predicted values.", 'start': 5259.011, 'duration': 5.165}, {'end': 5266.218, 'text': 'So this is our training history.', 'start': 5264.717, 'duration': 1.501}, {'end': 5271.203, 'text': 'Here blue stands for our training phase and this is our validation or our prediction stage.', 'start': 5266.638, 'duration': 4.565}, {'end': 5275.999, 'text': 'That was our training curve and this is our loss curve.', 'start': 5273.038, 'duration': 2.961}], 'summary': 'Testing model accuracy with heat map and training curve.', 'duration': 44.447, 'max_score': 5231.552, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng5231552.jpg'}, {'end': 5479.926, 'src': 'embed', 'start': 5440.126, 'weight': 0, 'content': [{'end': 5447.45, 'text': "We're generating data while we're watching YouTube videos, when we're sending emails, when we are chatting, and all of that right.", 'start': 5440.126, 'duration': 7.324}, {'end': 5450.212, 'text': 'even the IOT devices at our house, right?', 'start': 5447.45, 'duration': 2.762}, {'end': 5451.113, 'text': 'We have Alexa.', 'start': 5450.272, 'duration': 0.841}, {'end': 5453.054, 'text': 'all of this is generating a lot of data.', 'start': 5451.113, 'duration': 1.941}, {'end': 5456.276, 'text': 'a single click on your phone is generating a lot of data.', 'start': 5453.054, 'duration': 3.222}, {'end': 5458.038, 'text': 'Now, not only that.', 'start': 5456.756, 'duration': 1.282}, {'end': 5464.464, 'text': 'out of all the data that we generate, only 21% of the data is structured and well formatted, right.', 'start': 5458.038, 'duration': 6.426}, {'end': 5472.872, 'text': 'the remaining of the data is unstructured, and the major sources of unstructured data include text messages from WhatsApp, Facebook likes,', 'start': 5464.464, 'duration': 8.408}, {'end': 5475.895, 'text': 'comments on Instagram, the bulk emails and all of this.', 'start': 5472.872, 'duration': 3.023}, {'end': 5479.926, 'text': 'All of this accounts for the unstructured data that we have today.', 'start': 5476.703, 'duration': 3.223}], 'summary': 'Various activities generate large volumes of unstructured data, with only 21% being structured.', 'duration': 39.8, 'max_score': 5440.126, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng5440126.jpg'}, {'end': 5550.701, 'src': 'embed', 'start': 5525.938, 'weight': 8, 'content': [{'end': 5534.084, 'text': "and we're going to use text mining and natural language processing to draw useful insights or patterns from such data in order to grow a business.", 'start': 5525.938, 'duration': 8.146}, {'end': 5539.348, 'text': "Now, let's understand where exactly do we make use of natural language processing and text mining.", 'start': 5534.905, 'duration': 4.443}, {'end': 5547.699, 'text': 'Now, have you ever noticed that if you start typing a word on Google you immediately get suggestions, right? This feature is known as autocomplete.', 'start': 5539.992, 'duration': 7.707}, {'end': 5550.701, 'text': 'It will basically suggest the rest of the word to you.', 'start': 5548.199, 'duration': 2.502}], 'summary': "Using text mining and natural language processing to grow a business, including google's autocomplete feature.", 'duration': 24.763, 'max_score': 5525.938, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng5525938.jpg'}, {'end': 5621.648, 'src': 'embed', 'start': 5592.696, 'weight': 7, 'content': [{'end': 5600.259, 'text': 'It basically studies the reviews that customer gives for a particular movie and it tries to figure out if that movie is good or bad,', 'start': 5592.696, 'duration': 7.563}, {'end': 5601.139, 'text': 'depending on the review.', 'start': 5600.259, 'duration': 0.88}, {'end': 5604.8, 'text': 'So Netflix actually uses NLP in a very interesting manner.', 'start': 5601.619, 'duration': 3.181}, {'end': 5614.163, 'text': 'It tries to understand the type of movies that a person likes by the way a person has rated the movie or by the way the person has reviewed a movie.', 'start': 5605.2, 'duration': 8.963}, {'end': 5621.648, 'text': 'So by understanding what type of review a person is giving to a movie Netflix will recommend more movies that you like.', 'start': 5614.643, 'duration': 7.005}], 'summary': 'Netflix uses nlp to analyze movie reviews and recommend similar movies based on user preferences.', 'duration': 28.952, 'max_score': 5592.696, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng5592696.jpg'}, {'end': 5698.654, 'src': 'embed', 'start': 5656.844, 'weight': 1, 'content': [{'end': 5663.746, 'text': 'but the overall goal is to turn the text into data for analysis by using natural language processing.', 'start': 5656.844, 'duration': 6.902}, {'end': 5668.868, 'text': 'So basically text mining is implemented by using natural language processing techniques.', 'start': 5664.246, 'duration': 4.622}, {'end': 5674.042, 'text': 'There are various techniques in natural language processing that can help us perform text mining.', 'start': 5669.58, 'duration': 4.462}, {'end': 5677.924, 'text': "That's how text mining and natural language processing are related.", 'start': 5674.502, 'duration': 3.422}, {'end': 5685.067, 'text': 'natural language processing is the techniques that are used to solve the problem of text mining, text analysis and all of that.', 'start': 5677.924, 'duration': 7.143}, {'end': 5688.089, 'text': "Let's look at a couple more applications.", 'start': 5685.648, 'duration': 2.441}, {'end': 5692.671, 'text': 'sentimental analysis is one of the major applications of natural language processing.', 'start': 5688.089, 'duration': 4.582}, {'end': 5695.292, 'text': 'you see, Twitter performs sentimental analysis.', 'start': 5692.671, 'duration': 2.621}, {'end': 5698.654, 'text': 'Facebook, Google all of these perform sentimental analysis.', 'start': 5695.292, 'duration': 3.362}], 'summary': 'Text mining and nlp are used for data analysis. sentiment analysis is a major application with twitter, facebook, and google utilizing it.', 'duration': 41.81, 'max_score': 5656.844, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng5656844.jpg'}, {'end': 6023.961, 'src': 'embed', 'start': 5990.157, 'weight': 3, 'content': [{'end': 5993.7, 'text': 'They are not very helpful when we are analyzing important documents,', 'start': 5990.157, 'duration': 3.543}, {'end': 5999.964, 'text': 'that we need to focus on the important keywords in the documents instead of all of these commonly used words.', 'start': 5993.7, 'duration': 6.264}, {'end': 6004.547, 'text': 'example of stop words include the how, when, why not??', 'start': 5999.964, 'duration': 4.583}, {'end': 6005.108, 'text': 'Yes,', 'start': 6004.807, 'duration': 0.301}, {'end': 6007.109, 'text': 'No, all of these are stop words.', 'start': 6005.288, 'duration': 1.821}, {'end': 6011.838, 'text': 'So in order to better analyze our data, we need to get rid of stop words.', 'start': 6007.837, 'duration': 4.001}, {'end': 6016.179, 'text': "Now the last terminology I'm going to discuss is document term matrix.", 'start': 6012.458, 'duration': 3.721}, {'end': 6023.961, 'text': 'It is important to create something known as the document or matrix in natural language processing or DTM,', 'start': 6016.699, 'duration': 7.262}], 'summary': 'Stop words hinder document analysis, use dtm for nlp.', 'duration': 33.804, 'max_score': 5990.157, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng5990157.jpg'}], 'start': 5212.905, 'title': 'Deep learning, nlp basics & applications', 'summary': 'Covers deep learning model evaluation, achieving 96% accuracy after 200 epochs, the need for natural language processing due to 2.5 quintillion bytes of unstructured data daily, and basics of nlp including stemming, lemmatization, stop words, and document term matrix.', 'chapters': [{'end': 5379.224, 'start': 5212.905, 'title': 'Deep learning model evaluation', 'summary': 'Covers the process of training a deep learning model, evaluating its performance, and analyzing the accuracy and loss curves, reaching an accuracy of 96% after 200 epochs, and finally assessing false positive and false negative rates.', 'duration': 166.319, 'highlights': ['The accuracy of the model increased from 83% to 96% as the number of epochs increased, demonstrating the improvement in model performance.', "The demonstration involved plotting and analyzing accuracy and loss curves, along with a heat map to visualize the model's predictions, providing a comprehensive evaluation of the model's performance.", "The false positive and false negative rates were discussed to evaluate the model's ability to predict fraudulent data points."]}, {'end': 5822.045, 'start': 5379.264, 'title': 'Need for natural language processing', 'summary': 'Discusses the need for natural language processing and text mining due to the overwhelming amount of unstructured data, such as 2.5 quintillion bytes generated daily, and its applications including sentiment analysis, chatbots, machine translation, and advertisement matching.', 'duration': 442.781, 'highlights': ['2.5 quintillion bytes of data generated daily, with only 21% being structured.', 'Applications of NLP include sentimental analysis, chatbots, machine translation, and advertisement matching.', 'Netflix uses NLP to analyze customer reviews and recommend movies accordingly.']}, {'end': 6131.632, 'start': 5822.486, 'title': 'Nlp basics: stemming, lemmatization, stop words & document term matrix', 'summary': 'Covers the basics of natural language processing, including stemming, lemmatization, stop words, and document term matrix, emphasizing the importance of each technique and their impact on text analysis.', 'duration': 309.146, 'highlights': ['The chapter explains the difference between stemming and lemmatization, highlighting how lemmatization considers the morphological analysis of words and produces proper words as output.', 'It emphasizes the significance of removing stop words in natural language processing to focus on important keywords, thus enhancing the efficiency of document analysis.', 'The concept of document term matrix is introduced, illustrating its role in displaying the frequency of words in a document and its importance in text analysis.']}], 'duration': 918.727, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/7O60HOZRLng/pics/7O60HOZRLng5212905.jpg', 'highlights': ['The need for natural language processing due to 2.5 quintillion bytes of unstructured data daily', 'Achieving 96% accuracy after 200 epochs, demonstrating the improvement in model performance', "The false positive and false negative rates were discussed to evaluate the model's ability to predict fraudulent data points", 'Netflix uses NLP to analyze customer reviews and recommend movies accordingly', "The demonstration involved plotting and analyzing accuracy and loss curves, providing a comprehensive evaluation of the model's performance", 'Applications of NLP include sentimental analysis, chatbots, machine translation, and advertisement matching', 'The accuracy of the model increased from 83% to 96% as the number of epochs increased', 'The chapter explains the difference between stemming and lemmatization, highlighting how lemmatization considers the morphological analysis of words and produces proper words as output', 'It emphasizes the significance of removing stop words in natural language processing to focus on important keywords, thus enhancing the efficiency of document analysis', 'The concept of document term matrix is introduced, illustrating its role in displaying the frequency of words in a document and its importance in text analysis', '2.5 quintillion bytes of data generated daily, with only 21% being structured']}], 'highlights': ['Deep learning overcomes machine learning limitations by automatically handling high dimensional data and performing feature extraction, used in real-world cases like fraud detection in PayPal and face verification in Facebook.', 'Achieving 96% accuracy after 200 epochs, demonstrating the improvement in model performance.', "Python's pre-built libraries save significant coding time for AI developers.", "Python's simplicity and readability make it the easiest language for AI.", 'The need for natural language processing due to 2.5 quintillion bytes of unstructured data daily.', 'Tech giants like Tesla, Amazon, and Netflix implement AI techniques for deriving useful insights from data, showcasing practical applications and significance of AI in business growth.', 'The process includes data modeling using logistic regression, random forest classifier, decision tree classifier, and support vector machine to achieve an accuracy of approximately 84% using logistic regression.', 'Back propagation involves continuously updating weights to minimize error.', 'The accuracy using random forest is approximately 84%, which is a really good number.', 'Stratified sampling is employed to tackle unbalanced dataset.']}