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Dimensional Reduction| Principal Component Analysis

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Here is a detailed explanation of the Dimesnioanlity Reduction using Principal Component Analysis.
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{'title': 'Dimensional Reduction| Principal Component Analysis', 'heatmap': [{'end': 391.574, 'start': 364.199, 'weight': 0.712}, {'end': 691.753, 'start': 657.041, 'weight': 0.725}, {'end': 1014.911, 'start': 969.781, 'weight': 0.896}, {'end': 1054.413, 'start': 1038.574, 'weight': 0.866}], 'summary': 'Tutorial on principal component analysis explains the intuition behind pca, covers the process of selecting principal components for dimension reduction, and demonstrates the implementation using python and sql with the breast cancer dataset containing 569 records and 30 attributes, achieving a reduction from 30 dimensions to two for improved categorization and machine learning efficiency.', 'chapters': [{'end': 271.984, 'segs': [{'end': 46.535, 'src': 'embed', 'start': 1.462, 'weight': 0, 'content': [{'end': 2.062, 'text': 'hello all.', 'start': 1.462, 'duration': 0.6}, {'end': 8.064, 'text': 'today we will be discussing about a dimension reduction technique, which is called as principal component analysis.', 'start': 2.062, 'duration': 6.002}, {'end': 13.967, 'text': 'one thing that you should always remember is that principal component analysis is not a machine learning technique,', 'start': 8.064, 'duration': 5.903}, {'end': 16.568, 'text': 'but it is an unsupervised machine learning algorithm,', 'start': 13.967, 'duration': 2.601}, {'end': 23.231, 'text': 'such that which will actually helps you to reduce the number of dimensions or the number of features into some other dimensions.', 'start': 16.568, 'duration': 6.663}, {'end': 24.732, 'text': 'basically, lower number of dimensions.', 'start': 23.231, 'duration': 1.501}, {'end': 30.689, 'text': 'suppose, if you have a problem statement where you have a data set, where you have thousand features, thousand different columns,', 'start': 25.507, 'duration': 5.182}, {'end': 38.552, 'text': 'what you can do is that you can reduce those number of dimension into a smaller number of dimensions and then you can apply your machine learning algorithms.', 'start': 30.689, 'duration': 7.863}, {'end': 46.535, 'text': 'always remember that it is always said in machine learning that as the number of dimension increases, so there is always a curse.', 'start': 38.552, 'duration': 7.983}], 'summary': 'Principal component analysis reduces dimensions for machine learning, addressing curse of dimensionality.', 'duration': 45.073, 'max_score': 1.462, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM1462.jpg'}, {'end': 119.805, 'src': 'embed', 'start': 93.391, 'weight': 3, 'content': [{'end': 97.572, 'text': "let's let us just understand the first first part, that is, the intuition part.", 'start': 93.391, 'duration': 4.181}, {'end': 108.563, 'text': 'so, to begin with, suppose, i consider that i have a two dimension dimension that is basically two features, that is, this is feature f1 and f2,', 'start': 97.572, 'duration': 10.991}, {'end': 114.464, 'text': 'and some of the points are populated somewhere like this.', 'start': 108.563, 'duration': 5.901}, {'end': 119.805, 'text': 'right now, when i see this kind of features right, i want to convert this into one dimension.', 'start': 114.464, 'duration': 5.341}], 'summary': 'Explaining the process of converting two features into one dimension.', 'duration': 26.414, 'max_score': 93.391, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM93391.jpg'}, {'end': 208.309, 'src': 'embed', 'start': 183.635, 'weight': 5, 'content': [{'end': 190.682, 'text': 'So usually what happens is that, first of all, when I create this line, this line is basically called as principal component one.', 'start': 183.635, 'duration': 7.047}, {'end': 195.263, 'text': 'And you can create any number of components.', 'start': 193.422, 'duration': 1.841}, {'end': 202.326, 'text': 'But one thing that you have to make sure is that suppose the next component that will be created, it will be created perpendicular to the first line.', 'start': 195.463, 'duration': 6.863}, {'end': 208.309, 'text': 'Perpendicular basically means it will be on 90 degree or it will be orthogonal to the first line.', 'start': 202.927, 'duration': 5.382}], 'summary': 'Creating principal components, ensuring orthogonality.', 'duration': 24.674, 'max_score': 183.635, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM183635.jpg'}, {'end': 276.986, 'src': 'embed', 'start': 247.779, 'weight': 4, 'content': [{'end': 253.357, 'text': "after this, what i do is that I'll create my another principal component.", 'start': 247.779, 'duration': 5.578}, {'end': 255.618, 'text': 'So this is basically called as principal component one.', 'start': 253.377, 'duration': 2.241}, {'end': 261.16, 'text': "I'll create the another principal component and the best technique to create is basically I have to create a diagonal.", 'start': 255.978, 'duration': 5.182}, {'end': 263.121, 'text': 'I have to get the orthogonal line.', 'start': 261.18, 'duration': 1.941}, {'end': 268.843, 'text': 'This orthogonal line is my another principal component that is PC2.', 'start': 264.061, 'duration': 4.782}, {'end': 271.984, 'text': "I'll tell you why I'm creating it.", 'start': 270.443, 'duration': 1.541}, {'end': 276.986, 'text': 'So there may be a question like why should we just select this particular line instead of the other lines?', 'start': 272.264, 'duration': 4.722}], 'summary': 'Creating principal components using orthogonal lines for dimensionality reduction.', 'duration': 29.207, 'max_score': 247.779, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM247779.jpg'}], 'start': 1.462, 'title': 'Principal component analysis', 'summary': 'Introduces principal component analysis, an unsupervised machine learning algorithm that reduces dimensions, crucial for accuracy, and explains the intuition behind pca, detailing the process of converting features and creating orthogonal principal components.', 'chapters': [{'end': 72.459, 'start': 1.462, 'title': 'Principal component analysis', 'summary': 'Introduces principal component analysis as an unsupervised machine learning algorithm that reduces the number of dimensions or features, which is crucial as increased dimensions can negatively impact accuracy.', 'duration': 70.997, 'highlights': ['Principal Component Analysis is an unsupervised machine learning algorithm that reduces the number of dimensions or features, which is crucial as increased dimensions can negatively impact accuracy.', 'It helps in reducing the number of dimensions or features into a smaller number, which is beneficial when dealing with a dataset with a large number of features, such as a thousand columns.', 'The curse of dimensionality states that as the number of dimensions increases, the accuracy of machine learning algorithms gets impacted negatively, emphasizing the importance of dimension reduction.']}, {'end': 271.984, 'start': 72.459, 'title': 'Intuition behind principal component analysis', 'summary': 'Explains the intuition behind principal component analysis (pca), detailing the process of converting a two-dimensional feature into one dimension by finding the best fit line and creating orthogonal principal components, with each component being perpendicular to the previous one.', 'duration': 199.525, 'highlights': ['Explaining the process of converting a two-dimensional feature into one dimension by finding the best fit line and projecting the points onto it. Two dimension feature converted into one dimension', 'Detailing the creation of orthogonal principal components, with each component being perpendicular to the previous one. Creation of orthogonal principal components', 'Clarifying the concept of principal component one and the creation of additional principal components, with each being orthogonal to the previous one. Explanation of principal component one and creation of additional principal components']}], 'duration': 270.522, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM1462.jpg', 'highlights': ['Principal Component Analysis reduces dimensions, crucial for accuracy.', 'PCA helps in reducing features into a smaller number, beneficial for large datasets.', 'Curse of dimensionality emphasizes the importance of dimension reduction.', 'Explains converting a two-dimensional feature into one dimension by finding the best fit line.', 'Details the creation of orthogonal principal components.', 'Clarifies the concept of principal component one and the creation of additional principal components.']}, {'end': 580.43, 'segs': [{'end': 323.468, 'src': 'embed', 'start': 302.392, 'weight': 0, 'content': [{'end': 312.18, 'text': "when i'm projecting to the vector space or this principal component that time you should remember that there should be less number of variants or information lost.", 'start': 302.392, 'duration': 9.788}, {'end': 317.185, 'text': 'if there is more information lost, i should never select that particular in this particular space.', 'start': 312.18, 'duration': 5.005}, {'end': 323.468, 'text': "when i'm converting this into two dimension, from from two dimension to one dimension, i'm considering this line.", 'start': 317.185, 'duration': 6.283}], 'summary': 'When projecting to the vector space, minimize information loss.', 'duration': 21.076, 'max_score': 302.392, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM302392.jpg'}, {'end': 399.347, 'src': 'heatmap', 'start': 363.138, 'weight': 2, 'content': [{'end': 364.199, 'text': "i'll try to create this.", 'start': 363.138, 'duration': 1.061}, {'end': 370.081, 'text': 'components many times know, and all will be orthogonal to each other with respect to the dimensions.', 'start': 364.199, 'duration': 5.882}, {'end': 377.926, 'text': "and finally, i'll try to convert this into, suppose, if i'm selecting 200 dimension, this this lines that i see over when i'm projecting,", 'start': 370.081, 'duration': 7.845}, {'end': 382.869, 'text': 'the best 100 lines will be chosen where the variance lost is very, very less.', 'start': 377.926, 'duration': 4.943}, {'end': 391.574, 'text': 'yeah. so after i apply this particular principal component analysis and if i go and see when i see my two dimension getting converted into one dimension,', 'start': 382.869, 'duration': 8.705}, {'end': 399.347, 'text': 'it will look something like this Yeah, now let us just go and understand how we can implement by using Python and SQL.', 'start': 391.574, 'duration': 7.773}], 'summary': 'Implementing principal component analysis to select best 100 lines with least variance lost, using python and sql.', 'duration': 36.209, 'max_score': 363.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM363138.jpg'}, {'end': 554.207, 'src': 'embed', 'start': 524.767, 'weight': 1, 'content': [{'end': 528.709, 'text': 'so i have two classes one is malignant and one is benign.', 'start': 524.767, 'duration': 3.942}, {'end': 532.311, 'text': 'so these are my two outputs, or two outputs with respect to the data set.', 'start': 528.709, 'duration': 3.602}, {'end': 537.113, 'text': 'with respect to all these features, i may either have malignant or benign cancer.', 'start': 532.311, 'duration': 4.802}, {'end': 544.257, 'text': 'okay, now, if i go down, okay, so only two types of cancer may occur from this particular data set.', 'start': 537.113, 'duration': 7.144}, {'end': 554.207, 'text': "now, if i go, and what i'm going to do is that, first of all, i'm going to create a data frame where all my data i'm taking as all my data.", 'start': 545.222, 'duration': 8.985}], 'summary': 'Two classes: malignant and benign cancer, with only two types of cancer in the dataset.', 'duration': 29.44, 'max_score': 524.767, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM524767.jpg'}], 'start': 272.264, 'title': 'Principal component analysis', 'summary': 'Covers the importance of selecting principal components for dimension reduction, highlighting the process of projecting points onto vector spaces and the significance of minimizing information loss. it also discusses the implementation of principal component analysis using python and sql, analyzing the breast cancer dataset from sklearn containing 569 records and 30 attributes, with two target classes: malignant and benign.', 'chapters': [{'end': 382.869, 'start': 272.264, 'title': 'Selecting principal components for dimension reduction', 'summary': 'Discusses the importance of selecting principal components with minimal variance loss for dimension reduction, highlighting the process of projecting points onto vector spaces and the significance of minimizing information loss.', 'duration': 110.605, 'highlights': ['Projecting points onto vector spaces and selecting principal components with minimal variance loss is crucial for effective dimension reduction, as it ensures minimal information loss from the original data.', "When converting from higher dimensions to lower dimensions, it's essential to choose lines or components that result in minimal variance loss, as demonstrated by the comparison between different projection lines in the given examples.", 'In the context of high-dimensional data with numerous features, the process involves selecting orthogonal components that minimize variance loss, ultimately choosing the best lines with minimal information loss when reducing the dimensions.']}, {'end': 580.43, 'start': 382.869, 'title': 'Principal component analysis in python', 'summary': 'Discusses the implementation of principal component analysis using python and sql, utilizing libraries such as pandas, numpy, seaborn, and matplotlib to analyze the breast cancer dataset from sklearn, containing 569 records and 30 attributes, with two target classes: malignant and benign.', 'duration': 197.561, 'highlights': ['The breast cancer dataset from sklearn contains 569 records and 30 attributes, with two target classes: malignant and benign.', 'Libraries like pandas, numpy, seaborn, and matplotlib are utilized for data analysis and visualization, with pandas used for reading the dataset and numpy for creating multi-dimensional arrays.', 'The dataset features include various tumor sizes responsible for causing cancer, and the target types consist of two classes: malignant and benign.']}], 'duration': 308.166, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM272264.jpg', 'highlights': ['Projecting points onto vector spaces and selecting principal components with minimal variance loss is crucial for effective dimension reduction.', 'The breast cancer dataset from sklearn contains 569 records and 30 attributes, with two target classes: malignant and benign.', 'In the context of high-dimensional data with numerous features, the process involves selecting orthogonal components that minimize variance loss.']}, {'end': 1144.41, 'segs': [{'end': 627.864, 'src': 'embed', 'start': 601.265, 'weight': 0, 'content': [{'end': 605.827, 'text': 'before i apply the principal component analysis to reduce the dimension over here.', 'start': 601.265, 'duration': 4.562}, {'end': 609.128, 'text': 'the total number of dimensions are 30..', 'start': 605.827, 'duration': 3.301}, {'end': 618.591, 'text': "from this total number of dimensions from 30 i'm going to convert into two dimensions and then i'm going to plot that particular graph and see whether my output can be easily categorized.", 'start': 609.128, 'duration': 9.463}, {'end': 627.864, 'text': 'first of all, always remember, the first step in demonstrated reduction is basically to do stands standard normalization.', 'start': 619.858, 'duration': 8.006}], 'summary': 'Applying pca to reduce 30 dimensions to 2 for easy categorization.', 'duration': 26.599, 'max_score': 601.265, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM601265.jpg'}, {'end': 691.753, 'src': 'heatmap', 'start': 657.041, 'weight': 0.725, 'content': [{'end': 665.084, 'text': "with respect to this particular uh information, we'll try to convert into another distribution wherein wherein, suppose,", 'start': 657.041, 'duration': 8.043}, {'end': 671.206, 'text': "if i consider mean mean radius, i'll convert this into another distribution, d dash,", 'start': 665.084, 'duration': 6.122}, {'end': 679.649, 'text': 'such that you know this will belong to a standard normal distribution where your mean will be zero and standard deviation equal to one.', 'start': 671.206, 'duration': 8.443}, {'end': 681.209, 'text': 'so this all parameters.', 'start': 679.649, 'duration': 1.56}, {'end': 684.25, 'text': 'this when, when i consider this particular feature right,', 'start': 681.209, 'duration': 3.041}, {'end': 691.753, 'text': 'this has to go through one equation which is like x of i minus mean of mean of this particular column divided by standard deviation.', 'start': 684.25, 'duration': 7.503}], 'summary': 'Converting mean radius to standard normal distribution with mean=0, std dev=1', 'duration': 34.712, 'max_score': 657.041, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM657041.jpg'}, {'end': 731.524, 'src': 'embed', 'start': 699.084, 'weight': 1, 'content': [{'end': 702.708, 'text': 'so i have to pass all these things and that is basically in in a scalar.', 'start': 699.084, 'duration': 3.624}, {'end': 707.032, 'text': 'it is called a standard scalar, standard scalar.', 'start': 702.708, 'duration': 4.324}, {'end': 710.855, 'text': 'so the main purpose why we are doing is because this value may be in different,', 'start': 707.032, 'duration': 3.823}, {'end': 715.139, 'text': 'different units and we need to rescale all these values in the same units.', 'start': 710.855, 'duration': 4.284}, {'end': 724.219, 'text': 'We need to rescale the value rescale very important term rescale all this value in the same unit.', 'start': 716.394, 'duration': 7.825}, {'end': 731.524, 'text': 'Once we rescale, then my distribution, when I see over here, everything will be very, very nearer and compact to each other.', 'start': 724.759, 'duration': 6.765}], 'summary': 'Rescaling values to same units for compact distribution.', 'duration': 32.44, 'max_score': 699.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM699084.jpg'}, {'end': 1014.911, 'src': 'heatmap', 'start': 969.781, 'weight': 0.896, 'content': [{'end': 976.086, 'text': "So XPCA, colon, comma zero basically means I'm actually retrieving all the details from the zero index.", 'start': 969.781, 'duration': 6.305}, {'end': 979.109, 'text': "Then from the first feature, I'm retrieving all the details.", 'start': 976.567, 'duration': 2.542}, {'end': 983.793, 'text': "And my target is basically I'm giving it as cancer of target variable.", 'start': 979.549, 'duration': 4.244}, {'end': 987.876, 'text': 'Based on this target only, my points will be getting colored.', 'start': 984.413, 'duration': 3.463}, {'end': 991.099, 'text': "And here I'm going to apply CMAP just for some style.", 'start': 988.376, 'duration': 2.723}, {'end': 996.263, 'text': "And in the X label, you can see that I've given some title and the Y label I've given some title.", 'start': 992.059, 'duration': 4.204}, {'end': 1003.088, 'text': 'finally, when we try to represent it now, you can see that our data still looks much better, right?', 'start': 997.247, 'duration': 5.841}, {'end': 1007.989, 'text': 'initially just understand, guys, we had 30 dimensions, but now we are just having two dimensions.', 'start': 1003.088, 'duration': 4.901}, {'end': 1012.631, 'text': 'all these points have got converted into new dimension or scaled into new dimension.', 'start': 1007.989, 'duration': 4.642}, {'end': 1014.911, 'text': 'always remember, the first step was what.', 'start': 1012.631, 'duration': 2.28}], 'summary': 'Using xpca to reduce dimensions from 30 to 2 for better data representation.', 'duration': 45.13, 'max_score': 969.781, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM969781.jpg'}, {'end': 1023.268, 'src': 'embed', 'start': 992.059, 'weight': 3, 'content': [{'end': 996.263, 'text': "And in the X label, you can see that I've given some title and the Y label I've given some title.", 'start': 992.059, 'duration': 4.204}, {'end': 1003.088, 'text': 'finally, when we try to represent it now, you can see that our data still looks much better, right?', 'start': 997.247, 'duration': 5.841}, {'end': 1007.989, 'text': 'initially just understand, guys, we had 30 dimensions, but now we are just having two dimensions.', 'start': 1003.088, 'duration': 4.901}, {'end': 1012.631, 'text': 'all these points have got converted into new dimension or scaled into new dimension.', 'start': 1007.989, 'duration': 4.642}, {'end': 1014.911, 'text': 'always remember, the first step was what.', 'start': 1012.631, 'duration': 2.28}, {'end': 1016.771, 'text': 'what was the first step after reading the data set?', 'start': 1014.911, 'duration': 1.86}, {'end': 1023.268, 'text': 'the first step was standard scaling, scaling down right.', 'start': 1016.771, 'duration': 6.497}], 'summary': 'Data reduced from 30 to 2 dimensions using standard scaling.', 'duration': 31.209, 'max_score': 992.059, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM992059.jpg'}, {'end': 1062.517, 'src': 'heatmap', 'start': 1038.574, 'weight': 0.866, 'content': [{'end': 1045.436, 'text': 'now you can apply any of the machine learning algorithm, like, if you apply logistic regression, that it is going to create a straight line.', 'start': 1038.574, 'duration': 6.862}, {'end': 1054.413, 'text': 'the accuracy rate will be very high in this, okay, but if you use some other algorithms, like k nearest neighbor, nearest neighbor at that time,', 'start': 1045.436, 'duration': 8.977}, {'end': 1060.135, 'text': 'a nearest neighbor at that time, this will also give you a very good accuracy.', 'start': 1054.413, 'duration': 5.722}, {'end': 1062.517, 'text': 'otherwise, you can also use decision tree.', 'start': 1060.135, 'duration': 2.382}], 'summary': 'Machine learning algorithms like logistic regression and k nearest neighbor can achieve high accuracy rates in creating models.', 'duration': 23.943, 'max_score': 1038.574, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM1038574.jpg'}, {'end': 1110.328, 'src': 'embed', 'start': 1077.867, 'weight': 4, 'content': [{'end': 1080.088, 'text': 'okay, now it is up to you.', 'start': 1077.867, 'duration': 2.221}, {'end': 1086.472, 'text': 'since I have reduced my 30 dimension into two dimension, I can understand how my data is distributed now.', 'start': 1080.088, 'duration': 6.384}, {'end': 1093.277, 'text': "now, what is my next step is that I'll take this scale data, I'll take this scale data and apply any machine learning algorithm that I want.", 'start': 1086.472, 'duration': 6.805}, {'end': 1094.818, 'text': 'do the train test plate, you know.', 'start': 1093.277, 'duration': 1.541}, {'end': 1103.083, 'text': 'do the train test plate, so this X underscore PCA will become my independent feature and I know my output feature is nothing but my cancer of target.', 'start': 1094.818, 'duration': 8.265}, {'end': 1110.328, 'text': "and then i'll do a train test plate and i'll try start, you know, applying any machine learning algorithm, which will be very,", 'start': 1103.763, 'duration': 6.565}], 'summary': 'Reduced 30 dimensions to 2, will apply ml algorithm for cancer target prediction.', 'duration': 32.461, 'max_score': 1077.867, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM1077867.jpg'}], 'start': 580.43, 'title': 'Principal component analysis for dimensionality reduction', 'summary': 'Explains using pca to reduce 30 dimensions to two for easy categorization, visualizing the transformed data and its application in machine learning algorithms, aiming to improve accuracy and efficiency in model training and validation.', 'chapters': [{'end': 786.425, 'start': 580.43, 'title': 'Principal component analysis', 'summary': 'Explains the process of standard normalization and principal component analysis to reduce 30 dimensions to two for easy categorization, using standard scalar from sklearn.preprocessing to convert data into a standard normal distribution with mean zero and standard deviation one.', 'duration': 205.995, 'highlights': ['The chapter explains the process of standard normalization and principal component analysis to reduce 30 dimensions to two for easy categorization, using standard scalar from sklearn.preprocessing to convert data into a standard normal distribution with mean zero and standard deviation one. 30 dimensions reduced to two', 'Standard normalization involves rescaling values into the same units using a standard scalar to ensure that all features are very near and compact to each other, with mean zero and standard deviation one. Rescaling values into the same units', 'The standard scalar from sklearn.preprocessing is responsible for converting data of any distribution into a standard normal distribution, with mean zero and standard deviation one, using the formula X of I minus mu divided by standard deviation. Conversion of data into standard normal distribution']}, {'end': 1144.41, 'start': 786.965, 'title': 'Pca for dimensionality reduction', 'summary': 'Explains the use of principal component analysis (pca) to reduce 30 features to 2 features, visualizing the transformed data and its application in machine learning algorithms, aiming to improve accuracy and efficiency in model training and validation.', 'duration': 357.445, 'highlights': ['PCA algorithm used to reduce 30 features to 2 features The PCA algorithm is employed to convert the 30-dimensional features into two dimensions, thus reducing the dimensionality of the data.', 'Visualization of the transformed data using matplotlib The transformed data is visualized using matplotlib, effectively demonstrating the distribution of the data in the two-dimensional space.', 'Application of machine learning algorithms on the transformed data After dimensionality reduction, various machine learning algorithms can be applied to the transformed data to improve accuracy and efficiency in model training and validation.']}], 'duration': 563.98, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/OFyyWcw2cyM/pics/OFyyWcw2cyM580430.jpg', 'highlights': ['The chapter explains the process of using PCA to reduce 30 dimensions to two for easy categorization, aiming to improve accuracy and efficiency in model training and validation.', 'Standard normalization involves rescaling values into the same units using a standard scalar to ensure that all features are very near and compact to each other, with mean zero and standard deviation one.', 'The PCA algorithm is employed to convert the 30-dimensional features into two dimensions, thus reducing the dimensionality of the data.', 'Visualization of the transformed data using matplotlib effectively demonstrates the distribution of the data in the two-dimensional space.', 'After dimensionality reduction, various machine learning algorithms can be applied to the transformed data to improve accuracy and efficiency in model training and validation.']}], 'highlights': ['PCA helps in reducing features into a smaller number, beneficial for large datasets.', 'Principal Component Analysis reduces dimensions, crucial for accuracy.', 'Curse of dimensionality emphasizes the importance of dimension reduction.', 'Projecting points onto vector spaces and selecting principal components with minimal variance loss is crucial for effective dimension reduction.', 'The chapter explains the process of using PCA to reduce 30 dimensions to two for easy categorization, aiming to improve accuracy and efficiency in model training and validation.', 'Standard normalization involves rescaling values into the same units using a standard scalar to ensure that all features are very near and compact to each other, with mean zero and standard deviation one.', 'Visualization of the transformed data using matplotlib effectively demonstrates the distribution of the data in the two-dimensional space.', 'After dimensionality reduction, various machine learning algorithms can be applied to the transformed data to improve accuracy and efficiency in model training and validation.', 'The breast cancer dataset from sklearn contains 569 records and 30 attributes, with two target classes: malignant and benign.', 'In the context of high-dimensional data with numerous features, the process involves selecting orthogonal components that minimize variance loss.', 'Explains converting a two-dimensional feature into one dimension by finding the best fit line.', 'Details the creation of orthogonal principal components.', 'Clarifies the concept of principal component one and the creation of additional principal components.', 'The PCA algorithm is employed to convert the 30-dimensional features into two dimensions, thus reducing the dimensionality of the data.']}