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Tutorial 4 - Numpy and Inbuilt Functions Tutorial

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Welcome to the Python Crash Course. In this video we will see about numpy library
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{'title': 'Tutorial 4 - Numpy and Inbuilt Functions Tutorial', 'heatmap': [{'end': 997.642, 'start': 940.423, 'weight': 0.735}, {'end': 1042.609, 'start': 1024.056, 'weight': 0.727}, {'end': 1330.875, 'start': 1313.334, 'weight': 0.85}], 'summary': 'Learn about the importance of numpy library in machine learning and data analysis, creating one-dimensional and multi-dimensional arrays, reshaping arrays, indexing, inbuilt functions, and array operations including multiplication, division, and modulus, with practical examples and use cases.', 'chapters': [{'end': 175.605, 'segs': [{'end': 50.47, 'src': 'embed', 'start': 20.891, 'weight': 0, 'content': [{'end': 25.814, 'text': 'So NumPy library is basically one of the most important library which you will be frequently using.', 'start': 20.891, 'duration': 4.923}, {'end': 29.796, 'text': 'NumPy library is basically used for creating multi-dimensional arrays,', 'start': 26.314, 'duration': 3.482}, {'end': 36.019, 'text': 'and it also has a lot of inbuilt functions which will help you to perform different kinds of array operations.', 'start': 29.796, 'duration': 6.223}, {'end': 40.142, 'text': 'so first of all, let us just understand what exactly is a numpy array?', 'start': 36.659, 'duration': 3.483}, {'end': 41.543, 'text': 'so here you can see it.', 'start': 40.142, 'duration': 1.401}, {'end': 44.285, 'text': 'numpy is a general purpose array processing package.', 'start': 41.543, 'duration': 2.742}, {'end': 50.47, 'text': 'it provides a high performance multi-dimensional array, objects and tools for working with this particular arrays.', 'start': 44.285, 'duration': 6.185}], 'summary': 'Numpy is essential for creating and working with multi-dimensional arrays, offering high performance and a variety of built-in functions.', 'duration': 29.579, 'max_score': 20.891, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C020891.jpg'}, {'end': 82.029, 'src': 'embed', 'start': 59.497, 'weight': 1, 'content': [{'end': 69.082, 'text': 'So whenever you have such kind of bindings of C and C++ libraries, usually the operations usually takes place very, very quickly.', 'start': 59.497, 'duration': 9.585}, {'end': 75.906, 'text': 'Let it be the inbuilt function, suppose you are trying to multiply two arrays, you are trying to do different types of operations within the arrays.', 'start': 69.702, 'duration': 6.204}, {'end': 82.029, 'text': 'And remember guys, most of the machine learning algorithms are all different different mathematical calculations.', 'start': 76.466, 'duration': 5.563}], 'summary': 'C and c++ library operations are very fast, crucial for machine learning algorithms.', 'duration': 22.532, 'max_score': 59.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C059497.jpg'}, {'end': 138.044, 'src': 'embed', 'start': 103.291, 'weight': 3, 'content': [{'end': 109.532, 'text': 'In my previous video, I have already discussed about list and we saw that in list, you can have many items of different data types.', 'start': 103.291, 'duration': 6.241}, {'end': 115.253, 'text': 'But in array, whenever you are storing anything, you should make sure that all the elements are of the same data type.', 'start': 110.012, 'duration': 5.241}, {'end': 119.914, 'text': 'So let us begin and try to understand how we can use NumPy library to create arrays.', 'start': 115.753, 'duration': 4.161}, {'end': 124.415, 'text': 'First of all, to begin with guys, I have to import this NumPy library.', 'start': 120.494, 'duration': 3.921}, {'end': 125.936, 'text': 'Now, before importing.', 'start': 124.975, 'duration': 0.961}, {'end': 128.096, 'text': 'suppose you have installed Python manually right?', 'start': 125.936, 'duration': 2.16}, {'end': 131.999, 'text': 'so for that, if you want to install the numpy library separately,', 'start': 128.476, 'duration': 3.523}, {'end': 138.044, 'text': 'so you just have to use this command that is called as pip install numpy and open your command prompt or,', 'start': 131.999, 'duration': 6.045}], 'summary': 'Numpy library allows creation of arrays with elements of the same data type.', 'duration': 34.753, 'max_score': 103.291, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0103291.jpg'}, {'end': 183.453, 'src': 'embed', 'start': 157.491, 'weight': 2, 'content': [{'end': 163.295, 'text': 'So always remember the first statement of NumPy is that you need to import this NumPy as a library.', 'start': 157.491, 'duration': 5.804}, {'end': 168.579, 'text': "And once I'm importing it, I'm using an alias name, which is called as NP.", 'start': 163.736, 'duration': 4.843}, {'end': 175.605, 'text': 'Remember any operations that I need to, any input functions that I need to do within the NumPy, I can basically use this particular alias itself.', 'start': 169.24, 'duration': 6.365}, {'end': 183.453, 'text': 'So once I execute this, so this has got executed, you can see over here, now I have basically created a list.', 'start': 178.11, 'duration': 5.343}], 'summary': 'Import numpy as a library using alias np for efficient operations.', 'duration': 25.962, 'max_score': 157.491, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0157491.jpg'}], 'start': 0.269, 'title': 'Introduction to numpy library', 'summary': 'Introduces the importance of numpy library in machine learning and data analysis, highlighting its functionality in creating multi-dimensional arrays, its high-performance capabilities, and the significance of using numpy for efficient mathematical calculations.', 'chapters': [{'end': 175.605, 'start': 0.269, 'title': 'Introduction to numpy library', 'summary': 'Introduces the importance of numpy library in machine learning and data analysis, highlighting its functionality in creating multi-dimensional arrays, its high-performance capabilities, and the significance of using numpy for efficient mathematical calculations, and emphasizes the need to understand and practice numpy for machine learning algorithms and data analysis.', 'duration': 175.336, 'highlights': ['NumPy library is crucial in machine learning, as it is used for creating multi-dimensional arrays and performing array operations, which is essential for extensive exploratory data analysis in different data sets.', 'NumPy has bindings of C and C++ libraries, leading to faster operations, especially for mathematical calculations, including array multiplication and other array operations.', "Importing NumPy as a library and using an alias name 'NP' is the initial step to work with NumPy for array operations, which is essential for efficient data analysis and machine learning algorithms.", 'Array is a data structure that stores values of the same data type, distinguishing it from a list where items can be of different data types, emphasizing the importance of using NumPy for creating arrays for data analysis and machine learning algorithms.', "The process of installing NumPy library is explained, including using 'pip install numpy' for Python installation and 'conda install numpy' for Anaconda environment, ensuring that users can easily access and use the library for data analysis and machine learning purposes."]}], 'duration': 175.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0269.jpg', 'highlights': ['NumPy library is crucial in machine learning, as it is used for creating multi-dimensional arrays and performing array operations, which is essential for extensive exploratory data analysis in different data sets.', 'NumPy has bindings of C and C++ libraries, leading to faster operations, especially for mathematical calculations, including array multiplication and other array operations.', "Importing NumPy as a library and using an alias name 'NP' is the initial step to work with NumPy for array operations, which is essential for efficient data analysis and machine learning algorithms.", 'Array is a data structure that stores values of the same data type, distinguishing it from a list where items can be of different data types, emphasizing the importance of using NumPy for creating arrays for data analysis and machine learning algorithms.', "The process of installing NumPy library is explained, including using 'pip install numpy' for Python installation and 'conda install numpy' for Anaconda environment, ensuring that users can easily access and use the library for data analysis and machine learning purposes."]}, {'end': 489.46, 'segs': [{'end': 231.701, 'src': 'embed', 'start': 197.222, 'weight': 1, 'content': [{'end': 200.865, 'text': 'and then we will also understand what is the difference between one dimensional and two dimensional arrays.', 'start': 197.222, 'duration': 3.643}, {'end': 203.347, 'text': 'We will also try to see different kind of properties.', 'start': 201.245, 'duration': 2.102}, {'end': 207.471, 'text': 'To begin with guys, let me consider that I have defined a list like this.', 'start': 204.028, 'duration': 3.443}, {'end': 209.392, 'text': 'I have used square brackets.', 'start': 207.731, 'duration': 1.661}, {'end': 212.614, 'text': 'I have basically defined all my elements 1, 2, 3, 4, 5.', 'start': 209.472, 'duration': 3.142}, {'end': 218.017, 'text': "And then I'm basically using np.array and array is an inbuilt function over here.", 'start': 212.615, 'duration': 5.402}, {'end': 221.278, 'text': 'You can see that if I press shift tab before the brackets,', 'start': 218.037, 'duration': 3.241}, {'end': 229.42, 'text': "you can basically see that it creates an array and it creates an array based on the data type or based on the element that I'm basically giving over here.", 'start': 221.278, 'duration': 8.142}, {'end': 231.701, 'text': "So over here, I'm basically giving a list.", 'start': 229.8, 'duration': 1.901}], 'summary': 'Exploring differences between 1d and 2d arrays and creating arrays from lists using np.array.', 'duration': 34.479, 'max_score': 197.222, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0197222.jpg'}, {'end': 333.068, 'src': 'embed', 'start': 303.644, 'weight': 3, 'content': [{'end': 306.246, 'text': 'This is how a one-dimensional array is basically represented.', 'start': 303.644, 'duration': 2.602}, {'end': 313.152, 'text': 'Now, you may be also thinking, can I convert this into a two-dimensional array? Yes, you can basically convert that into a two-dimensional array.', 'start': 306.727, 'duration': 6.425}, {'end': 317.155, 'text': 'So, for this, what I will do is that I am going to use an inbuilt function called as reshape.', 'start': 313.532, 'duration': 3.623}, {'end': 328.503, 'text': 'inside reshape, you basically have to provide some values, okay, like how many number of rows or how many number of columns are there before you know?', 'start': 318.752, 'duration': 9.751}, {'end': 330.125, 'text': 'reshaping into a two-dimensional array.', 'start': 328.503, 'duration': 1.622}, {'end': 333.068, 'text': 'first of all, let us create a two-dimensional array manually.', 'start': 330.125, 'duration': 2.943}], 'summary': 'Explains how to convert a one-dimensional array to a two-dimensional array using the reshape function.', 'duration': 29.424, 'max_score': 303.644, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0303644.jpg'}, {'end': 479.534, 'src': 'embed', 'start': 452.885, 'weight': 0, 'content': [{'end': 458.969, 'text': "you'll be having a lot of rows that can basically be converted finally into an array and then applied to a machine learning algorithm.", 'start': 452.885, 'duration': 6.084}, {'end': 464.691, 'text': 'the reason we are basically using arrays is that, guys, arrays operation are very, very fast.', 'start': 458.969, 'duration': 5.722}, {'end': 472.573, 'text': 'so whenever i convert my whole data set right into an array, okay, after doing all the feature engineering, feature selection and many process,', 'start': 464.691, 'duration': 7.882}, {'end': 479.534, 'text': 'what will happen is that i can take that data, apply it to my machine learning algorithm, such that my training usually takes up quickly,', 'start': 472.573, 'duration': 6.961}], 'summary': 'Using arrays for fast machine learning algorithm application.', 'duration': 26.649, 'max_score': 452.885, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0452885.jpg'}], 'start': 178.11, 'title': 'Arrays in python and numpy', 'summary': 'Demonstrates how to create one-dimensional and multi-dimensional arrays in python, and the advantages of using numpy arrays in machine learning, with a focus on properties and differences between the two, and includes examples of creating and manipulating arrays.', 'chapters': [{'end': 212.614, 'start': 178.11, 'title': 'Creating and understanding arrays in python', 'summary': 'Demonstrates how to create one-dimensional and multi-dimensional arrays in python, with a focus on properties and differences between the two. the example includes creating a list with elements 1, 2, 3, 4, 5.', 'duration': 34.504, 'highlights': ['The chapter covers creating one-dimensional and multi-dimensional arrays in Python.', 'It focuses on understanding the properties and differences between one-dimensional and two-dimensional arrays.', 'An example is provided where a list with elements 1, 2, 3, 4, 5 is created using square brackets.']}, {'end': 489.46, 'start': 212.615, 'title': 'Working with numpy arrays', 'summary': 'Introduces the creation and manipulation of numpy arrays, showcasing the process of converting a list to a one-dimensional array, identifying the shape of arrays, and converting a list of lists to a two-dimensional array, emphasizing the advantages of using arrays in machine learning.', 'duration': 276.845, 'highlights': ['The chapter introduces the creation and manipulation of numpy arrays, showcasing the process of converting a list to a one-dimensional array, identifying the shape of arrays, and converting a list of lists to a two-dimensional array, emphasizing the advantages of using arrays in machine learning.', "Numpy array is created by using the inbuilt function np.array, and by providing a list as an input, a one-dimensional array is obtained, which can be confirmed using the 'shape' function, displaying the number of elements in the array.", "The process of reshaping a one-dimensional array into a two-dimensional array using the 'reshape' function is demonstrated, and the advantages of using arrays in machine learning, due to their fast operation, are highlighted for efficient training processes."]}], 'duration': 311.35, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0178110.jpg', 'highlights': ['The advantages of using arrays in machine learning are highlighted, emphasizing their fast operation for efficient training processes.', 'The chapter covers creating one-dimensional and multi-dimensional arrays in Python, focusing on understanding their properties and differences.', 'An example is provided where a list with elements 1, 2, 3, 4, 5 is created using square brackets.', "The process of reshaping a one-dimensional array into a two-dimensional array using the 'reshape' function is demonstrated.", 'Numpy array is created by using the inbuilt function np.array, and by providing a list as an input, a one-dimensional array is obtained.']}, {'end': 664.46, 'segs': [{'end': 576.215, 'src': 'embed', 'start': 530.714, 'weight': 0, 'content': [{'end': 534.278, 'text': 'This 5 comma 3 basically indicates now I will have 5 rows and 3 columns.', 'start': 530.714, 'duration': 3.564}, {'end': 541.065, 'text': 'So when I am reshaping also, you have to make sure that the total count of this element should be 15.', 'start': 535.299, 'duration': 5.766}, {'end': 543.607, 'text': 'Over here you can see that it is 15.', 'start': 541.065, 'duration': 2.542}, {'end': 551.173, 'text': 'I can reshape these elements and actually create a new array with a different shape, but make sure always the number of elements should be equal.', 'start': 543.607, 'duration': 7.566}, {'end': 559.399, 'text': 'So, if I am executing this array.reshape 5, 3, you can see that all I am basically getting 5 rows and 3 columns.', 'start': 551.693, 'duration': 7.706}, {'end': 561.26, 'text': 'What if I make 5, 4?', 'start': 559.819, 'duration': 1.441}, {'end': 565.843, 'text': 'Now understand, guys, if I make 5, 4, the total number of elements will be 20, right?', 'start': 561.26, 'duration': 4.583}, {'end': 572.134, 'text': 'If I execute this, cannot reshape array of size 15 into shape 5, 4..', 'start': 566.184, 'duration': 5.95}, {'end': 576.215, 'text': 'is the array error that we are basically getting.', 'start': 572.134, 'duration': 4.081}], 'summary': 'Reshaping array from 5x3 to 5x4 results in error due to element count mismatch.', 'duration': 45.501, 'max_score': 530.714, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0530714.jpg'}, {'end': 643.201, 'src': 'embed', 'start': 618.295, 'weight': 2, 'content': [{'end': 623.74, 'text': 'we have also discussed indexing in dictionaries, we have discussed in list, in tuples and sets, right.', 'start': 618.295, 'duration': 5.445}, {'end': 628.184, 'text': 'so here also, it is very, very important to understand how we perform indexing in numpy array.', 'start': 623.74, 'duration': 4.444}, {'end': 630.293, 'text': 'So let us go ahead and try to understand it.', 'start': 628.612, 'duration': 1.681}, {'end': 637.157, 'text': "So first of all, this is my array, right? So I'm just going to reshape this to something else.", 'start': 630.753, 'duration': 6.404}, {'end': 643.201, 'text': 'Or what I can do is that I can basically create one simple array.', 'start': 638.258, 'duration': 4.943}], 'summary': 'Discussed indexing in different data structures and emphasized importance of understanding indexing in numpy array.', 'duration': 24.906, 'max_score': 618.295, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0618295.jpg'}], 'start': 489.46, 'title': 'Reshaping numpy arrays', 'summary': 'Explains the concept of reshaping numpy arrays, emphasizing the significance of element count maintenance and indexing process in numpy arrays.', 'chapters': [{'end': 664.46, 'start': 489.46, 'title': 'Reshaping numpy arrays', 'summary': 'Explains the concept of reshaping numpy arrays, highlighting the importance of maintaining the total count of elements and the process of indexing in numpy arrays.', 'duration': 175, 'highlights': ["The function 'reshape' allows for changing the shape of the array, ensuring the total number of elements remains the same, exemplified by reshaping a 3x5 array to a 5x3 array with 15 elements. The reshape function allows for altering the shape of the array while maintaining the total count of elements, demonstrated by reshaping a 3x5 array to a 5x3 array with 15 elements.", 'Emphasizes the significance of maintaining the consistent count of elements, illustrated by the error encountered when attempting to reshape a 3x5 array to a 5x4 array with 20 elements, highlighting the importance of equal element count. The importance of maintaining a consistent count of elements is emphasized through the error encountered when trying to reshape a 3x5 array to a 5x4 array with 20 elements, highlighting the significance of equal element count.', 'Stresses the importance of understanding indexing in numpy arrays and the relevance of retrieving data within data structures like arrays, data frames, and dictionaries. The significance of understanding indexing in numpy arrays and the relevance of retrieving data within data structures like arrays, data frames, and dictionaries is emphasized.']}], 'duration': 175, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0489460.jpg', 'highlights': ["The function 'reshape' maintains total element count, e.g., reshaping 3x5 array to 5x3 with 15 elements.", 'Importance of consistent element count stressed, e.g., error when reshaping 3x5 array to 5x4 with 20 elements.', 'Significance of understanding indexing in numpy arrays emphasized for data retrieval.']}, {'end': 1223.862, 'segs': [{'end': 748.751, 'src': 'embed', 'start': 727.196, 'weight': 0, 'content': [{'end': 736.082, 'text': "so what i'll do is that first of all, i'll just show you how indexing can be done in, uh, two dimensions, as i said, you guys,", 'start': 727.196, 'duration': 8.886}, {'end': 737.803, 'text': 'first of all just put a comma.', 'start': 736.082, 'duration': 1.721}, {'end': 744.308, 'text': 'okay, the left hand side you are basically providing the information about which row index you want.', 'start': 737.803, 'duration': 6.505}, {'end': 748.751, 'text': 'suppose, if i give colon, that basically means it is going to pick up all the row indexes.', 'start': 744.308, 'duration': 4.443}], 'summary': 'Demonstrating two-dimensional indexing for row selection.', 'duration': 21.555, 'max_score': 727.196, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0727196.jpg'}, {'end': 997.642, 'src': 'heatmap', 'start': 925.204, 'weight': 1, 'content': [{'end': 931.771, 'text': 'here, if i used two, that basically means it is going to take a step gap of.', 'start': 925.204, 'duration': 6.567}, {'end': 940.423, 'text': 'you can see over here once i execute it and once i see my array, see after zero it is picking up two, then four, six, eight and ten.', 'start': 932.839, 'duration': 7.584}, {'end': 947.106, 'text': 'okay, this is our basic functionality of uh, arrange, again, there is an inbuilt function called as linspace.', 'start': 940.423, 'duration': 6.683}, {'end': 949.087, 'text': "i'm going to show you that also.", 'start': 947.106, 'duration': 1.981}, {'end': 951.448, 'text': 'so here you can basically see what.', 'start': 949.087, 'duration': 2.361}, {'end': 953.89, 'text': 'uh, what is exactly linspace?', 'start': 951.448, 'duration': 2.442}, {'end': 957.011, 'text': 'so np dot, linspace.', 'start': 953.89, 'duration': 3.121}, {'end': 959.993, 'text': 'i press shift tab here you can see that.', 'start': 957.011, 'duration': 2.982}, {'end': 962.454, 'text': 'uh, again, the definition is pretty much clear.', 'start': 959.993, 'duration': 2.461}, {'end': 967.718, 'text': "they're saying that between start and stop value, how many number of points you basically want?", 'start': 962.454, 'duration': 5.264}, {'end': 969.799, 'text': 'and this is our equally divided points.', 'start': 967.718, 'duration': 2.081}, {'end': 976.263, 'text': 'okay, so always remember that, guys, suppose i say, okay, i want from 1 to 10, i want 50 points.', 'start': 969.799, 'duration': 6.464}, {'end': 978.805, 'text': "if i execute it, you can see that this is my value that i'm getting.", 'start': 976.263, 'duration': 2.542}, {'end': 982.108, 'text': 'These are equally spaced points.', 'start': 979.626, 'duration': 2.482}, {'end': 984.651, 'text': 'It is pretty much simple again in space.', 'start': 982.329, 'duration': 2.322}, {'end': 986.672, 'text': 'Again, why do we use it??', 'start': 985.451, 'duration': 1.221}, {'end': 991.276, 'text': 'Sometimes, in deep learning and all you know,', 'start': 987.033, 'duration': 4.243}, {'end': 997.642, 'text': 'we need to select some of the points quickly and we need to initialize some of the points quickly so that it can be very, very helpful.', 'start': 991.276, 'duration': 6.366}], 'summary': 'Demonstrating the functionality of np.linspace to create equally spaced points between start and stop values, useful for quick point selection in deep learning.', 'duration': 26.244, 'max_score': 925.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0925204.jpg'}, {'end': 1051.136, 'src': 'heatmap', 'start': 1024.056, 'weight': 0.727, 'content': [{'end': 1027.939, 'text': 'Now, let me just print this array over here.', 'start': 1024.056, 'duration': 3.883}, {'end': 1036.025, 'text': "Now, what I'll do is that in this array element, I'll say that from the third index to all the indexes replaced by 100.", 'start': 1028.519, 'duration': 7.506}, {'end': 1037.086, 'text': "So, I'll execute it.", 'start': 1036.025, 'duration': 1.061}, {'end': 1042.609, 'text': "Now, if you go and see my ARR, you can see that I'm having all the values and all the elements are replaced by 100.", 'start': 1037.126, 'duration': 5.483}, {'end': 1043.51, 'text': 'Perfectly fine.', 'start': 1042.609, 'duration': 0.901}, {'end': 1045.09, 'text': 'This is also called as broadcasting.', 'start': 1043.569, 'duration': 1.521}, {'end': 1049.835, 'text': 'So, copy function and broadcasting.', 'start': 1046.112, 'duration': 3.723}, {'end': 1051.136, 'text': "Let's write it down.", 'start': 1050.435, 'duration': 0.701}], 'summary': 'Array elements from third index onward replaced by 100, demonstrating broadcasting.', 'duration': 27.08, 'max_score': 1024.056, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C01024056.jpg'}, {'end': 1169.115, 'src': 'embed', 'start': 1142.583, 'weight': 2, 'content': [{'end': 1147.004, 'text': 'Any update on one variable will replicate that particular value in that particular memory itself.', 'start': 1142.583, 'duration': 4.421}, {'end': 1150.785, 'text': 'So in order to prevent this, we have something called as copy function.', 'start': 1147.464, 'duration': 3.321}, {'end': 1154.986, 'text': 'So I can use array.copy and assign it to array1.', 'start': 1151.765, 'duration': 3.221}, {'end': 1159.649, 'text': "once i execute it and after this i'll say that print array.", 'start': 1155.746, 'duration': 3.903}, {'end': 1161.21, 'text': 'okay, i want to print array.', 'start': 1159.649, 'duration': 1.561}, {'end': 1166.433, 'text': 'first of all and i know in my array it is 500 elements are present in everything after third index.', 'start': 1161.21, 'duration': 5.223}, {'end': 1169.115, 'text': "then i'll say that okay, array of one from three column.", 'start': 1166.433, 'duration': 2.682}], 'summary': 'Using array.copy to prevent memory replication, printing specific column values', 'duration': 26.532, 'max_score': 1142.583, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C01142583.jpg'}], 'start': 664.46, 'title': 'Indexing and inbuilt functions in numpy', 'summary': 'Delves into indexing in one and two dimensions in numpy arrays, while also discussing inbuilt functions like arrange, linspace, and the copy function, providing examples and use cases.', 'chapters': [{'end': 1223.862, 'start': 664.46, 'title': 'Indexing and inbuilt functions in numpy', 'summary': 'Explains indexing in one and two dimensions in numpy arrays, demonstrating examples and techniques. it also covers inbuilt functions like arrange, linspace, and the copy function, highlighting their parameters and use cases.', 'duration': 559.402, 'highlights': ['The chapter covers indexing in one and two dimensions, demonstrating how to retrieve specific elements using row and column indexes.', "It explains the inbuilt functions 'arrange' and 'linspace', showcasing their parameters and use cases with examples.", "It details the 'copy' function in NumPy, explaining its role in creating a separate memory space for array elements to prevent sharing the same memory location and the impact it has on reference type arrays."]}], 'duration': 559.402, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C0664460.jpg', 'highlights': ['The chapter covers indexing in one and two dimensions, demonstrating how to retrieve specific elements using row and column indexes.', "It explains the inbuilt functions 'arrange' and 'linspace', showcasing their parameters and use cases with examples.", "It details the 'copy' function in NumPy, explaining its role in creating a separate memory space for array elements to prevent sharing the same memory location and the impact it has on reference type arrays."]}, {'end': 1601.107, 'segs': [{'end': 1270.974, 'src': 'embed', 'start': 1244.506, 'weight': 0, 'content': [{'end': 1250.237, 'text': 'Similarly you can also do different kind of operations like multiplied by 2.', 'start': 1244.506, 'duration': 5.731}, {'end': 1251.598, 'text': 'say multiplied by 2.', 'start': 1250.237, 'duration': 1.361}, {'end': 1256.342, 'text': 'here you can see that all the elements has been multiplied by 2, and remember in array all the elements of 500 over here.', 'start': 1251.598, 'duration': 4.744}, {'end': 1263.807, 'text': 'okay, you can also do a division, saying as divided by 2, so how it is happening for each and every element.', 'start': 1256.342, 'duration': 7.465}, {'end': 1265.408, 'text': 'this process is basically taking.', 'start': 1263.807, 'duration': 1.601}, {'end': 1270.974, 'text': 'okay, also using modulus 2, All the different kind of arrays, it will basically happen.', 'start': 1265.408, 'duration': 5.566}], 'summary': 'Operations like multiplication, division, and modulus are applied to array elements.', 'duration': 26.468, 'max_score': 1244.506, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C01244506.jpg'}, {'end': 1341.721, 'src': 'heatmap', 'start': 1313.334, 'weight': 0.85, 'content': [{'end': 1316.378, 'text': 'okay, arrange, you can basically use for quickly creating summaries.', 'start': 1313.334, 'duration': 3.044}, {'end': 1321.306, 'text': 'And okay, there was some more inbuilt functions that I wanted to discuss.', 'start': 1318.283, 'duration': 3.023}, {'end': 1322.847, 'text': 'one is about NP dot ones.', 'start': 1321.306, 'duration': 1.541}, {'end': 1330.875, 'text': 'Okay So, let me just show you so NP dot ones actually creates an array where all the elements are basically replaced by one Okay.', 'start': 1322.867, 'duration': 8.008}, {'end': 1335.939, 'text': 'So here you can see that I have given one value like four So it is basically replacing four ones.', 'start': 1331.495, 'duration': 4.444}, {'end': 1338.742, 'text': 'I mean it is basically creating four ones in that particular array.', 'start': 1336.119, 'duration': 2.623}, {'end': 1341.721, 'text': 'So Similarly, you can also give values like this.', 'start': 1338.762, 'duration': 2.959}], 'summary': "Numpy's np.ones function creates arrays with specified values, such as replacing 4 with 1.", 'duration': 28.387, 'max_score': 1313.334, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C01313334.jpg'}, {'end': 1462.238, 'src': 'embed', 'start': 1437.713, 'weight': 1, 'content': [{'end': 1443.958, 'text': "So, over here, in RANDN, I'm basically saying that, okay, consider a 4x4, but make sure that this is a standard normal distribution.", 'start': 1437.713, 'duration': 6.245}, {'end': 1447.38, 'text': 'Once I execute it, these are my elements that have got selected.', 'start': 1444.758, 'duration': 2.622}, {'end': 1451.063, 'text': "And always remember, guys, this random distribution that I'm selecting.", 'start': 1447.42, 'duration': 3.643}, {'end': 1454.866, 'text': 'if I execute it each and every time, the elements will change because it is randomly selecting it.', 'start': 1451.063, 'duration': 3.803}, {'end': 1458.755, 'text': 'Okay, so here also similarly for this random distribution.', 'start': 1455.452, 'duration': 3.303}, {'end': 1462.238, 'text': 'Now what I do is that since this is of a random distribution.', 'start': 1459.195, 'duration': 3.043}], 'summary': 'Using randn to select elements from 4x4 standard normal distribution.', 'duration': 24.525, 'max_score': 1437.713, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C01437713.jpg'}], 'start': 1223.942, 'title': 'Numpy and array operations', 'summary': 'Discusses array operations including multiplication, division, and modulus, and conditions to filter elements, with an example involving an array of 500 elements. it also covers numpy functions such as np.ones, np.random.rand, and np.random.randn, with practical examples on creating arrays and generating random distributions based on standard normal distribution.', 'chapters': [{'end': 1313.334, 'start': 1223.942, 'title': 'Array operations and conditions', 'summary': 'Discusses array operations like multiplication, division, and modulus, and demonstrates how to apply conditions to filter elements based on specific criteria, including displaying values less than a certain number. the example involves an array of 500 elements and displays elements less than 300.', 'duration': 89.392, 'highlights': ['The chapter discusses array operations like multiplication, division, and modulus, and demonstrates how to apply conditions to filter elements based on specific criteria, including displaying values less than a certain number.', 'The example involves an array of 500 elements and displays elements less than 300.', 'The code also demonstrates writing multiple conditions inside square braces to filter elements based on given criteria.']}, {'end': 1601.107, 'start': 1313.334, 'title': 'Numpy functions and random distributions', 'summary': 'Covers numpy functions such as np.ones, np.random.rand, and np.random.randn, explaining how to create arrays with specific values, populate arrays with random samples, and generate random distributions based on standard normal distribution, with examples and practical tips.', 'duration': 287.773, 'highlights': ["NP.random.randn is used to select random variables based on standard normal distribution, demonstrated with a 4x4 array, and it's emphasized that the elements will change each time it's executed.", "NP.random.rand is used to create an array of a given shape and populate it with random samples from a uniform distribution 0, 1, ensuring that all elements are selected between 0 to 1, and it's clarified that the elements will change each time it's executed.", 'NP.ones is demonstrated to create an array where all elements are replaced by one, with the ability to specify the data type and create two-dimensional arrays, providing a clear understanding of its functionality and usage.']}], 'duration': 377.165, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/vh525RjO6C0/pics/vh525RjO6C01223942.jpg', 'highlights': ['The chapter discusses array operations like multiplication, division, and modulus, and demonstrates how to apply conditions to filter elements based on specific criteria, including displaying values less than a certain number.', "NP.random.randn is used to select random variables based on standard normal distribution, demonstrated with a 4x4 array, and it's emphasized that the elements will change each time it's executed."]}], 'highlights': ['NumPy library is crucial in machine learning, as it is used for creating multi-dimensional arrays and performing array operations, which is essential for extensive exploratory data analysis in different data sets.', 'NumPy has bindings of C and C++ libraries, leading to faster operations, especially for mathematical calculations, including array multiplication and other array operations.', 'The advantages of using arrays in machine learning are highlighted, emphasizing their fast operation for efficient training processes.', "The process of installing NumPy library is explained, including using 'pip install numpy' for Python installation and 'conda install numpy' for Anaconda environment, ensuring that users can easily access and use the library for data analysis and machine learning purposes.", "The function 'reshape' maintains total element count, e.g., reshaping 3x5 array to 5x3 with 15 elements.", 'The chapter covers indexing in one and two dimensions, demonstrating how to retrieve specific elements using row and column indexes.', 'The chapter discusses array operations like multiplication, division, and modulus, and demonstrates how to apply conditions to filter elements based on specific criteria, including displaying values less than a certain number.']}