Pluralsight Path. Feature Engineering (2019)

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Pluralsight Path. Feature Engineering (2019)

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最近下载：2024-12-01

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文件列表

  • C2. Building Features from Image Data (Janani Ravi, 2019)/exercise.7z 214.0 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/exercise.7z 42.8 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/exercise.7z 28.0 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/exercise.7z 22.2 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/10. Working with Geospatial Features.mp4 18.8 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/5. Feature Detection Using Convolution Kernels.mp4 18.1 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/03. Classification Using the Hashing Vectorizer.mp4 17.4 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/06. Feature Detection and Extraction Using SIFT.mp4 17.3 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/7. Reading and Exploring the Dataset.mp4 17.1 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/6. Similar Documents Using Jaccard Index and Locality-sensitive Hashing.mp4 16.8 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/04. Applying Keypoint Preserving Transformations.mp4 16.7 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/07. Regression Using Helmert Encoding.mp4 16.1 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/5. Bag-of-n-grams Using the Count Vectorizer.mp4 15.8 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/08. Extracting Text from Images Using OCR.mp4 15.6 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/3. Stopword Removal Using NLTK and scikit-learn.mp4 15.4 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/07. Detecting Keypoints and Descriptors to Perform Image Matching.mp4 15.2 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/06. Calculating and Visualizing Correlations Using Pandas.mp4 15.1 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/08. Feature Detection Using Histogram of Oriented Gradients.mp4 15.0 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/3. Bag-of-words Using the Count Vectorizer.mp4 14.8 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/10. Sentence and Word Tokenization.mp4 14.6 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/5. Applying Different Techniques to Handle Missing Values.mp4 14.6 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/06. Regression Using Backward Difference Encoding.mp4 14.3 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/7. Demo - Performing Kernel PCA to Reduce Complexity in Nonlinear Data.mp4 14.3 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/7. Parts-of-speech Tagging.mp4 14.3 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/04. Creating Feature Vectors from Text Data.mp4 14.1 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/08. Feature Selection Using Filter Methods.mp4 14.0 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/4. Reducing Dimensions at Scale Using the Hashing Vectorizer.mp4 13.9 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/4. Categorizing Continuous Data Using the KBinsDiscretizer.mp4 13.8 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/6. Dummy Coding Using Patsy.mp4 13.6 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/6. Detecting and Handling Outliers.mp4 13.6 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/04. Demo - Selecting Features Using a Variance Threshold.mp4 13.5 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/09. Feature Selection Using Wrapper Methods.mp4 13.4 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/13. Label Encoding to Convert Categorical Data to Ordinal.mp4 13.3 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/05. Performing Linear Regression Using Machine Learning with Simple Effect Coding.mp4 13.2 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/05. Loading and Transforming Images.mp4 12.8 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/12. Normalization and ZCA Whitening.mp4 12.7 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/06. Working with Images as Arrays.mp4 12.7 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/5. Regression Analysis with Dummy or Treatment Coding.mp4 12.6 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/09. Demo - The Diabetes Dataset - Exploration.mp4 12.6 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/07. Demo - Calculating Mean, Variance, and Standard Deviation.mp4 12.5 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/04. Regression Analysis Using Simple Effect Coding.mp4 12.4 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/11. Plotting Word Frequency Distributions.mp4 12.3 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/03. Performing Normalization Using Different Techniques.mp4 12.1 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/5. Autoencoding.mp4 12.1 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/5. Stemming.mp4 12.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/4. Demo - Cosine Similarity and the L2 Norm.mp4 12.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/13. Demo - Scaling Data Using the Robust Scaler.mp4 11.8 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/4. Dummy Coding to Overcome Limitations of One-hot Encoding.mp4 11.7 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/09. Resizing, Rescaling, Rotating, and Flipping Images.mp4 11.7 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/11. Denoising Images.mp4 11.6 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/3. Sparse Representations Using Dictionary Learning.mp4 11.6 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/10. One-hot Encoding with Known and Unknown Categories.mp4 11.3 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/05. Feature Selection Using Missing Value Ratio.mp4 11.3 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/10. Demo - Dictionary Learning on Handwritten Digits.mp4 11.2 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/08. Working with Color and Color Spaces.mp4 11.2 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/7. Reading and Preprocessing Images.mp4 11.1 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/08. Generating Equally Spaced Categories to Perform Orthogonal Polynomial Encoding.mp4 11.0 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/09. Optical Character Recognition Using Tesseract.mp4 11.0 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/8. Demo - Performing Linear Discriminant Analysis to Reorient Data.mp4 11.0 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/09. Extracting Features from Dates.mp4 11.0 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/12. Demo - Kitchen Sink Regression to Establish a Baseline Model.mp4 10.9 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/2. Understanding Principal Components Analysis.mp4 10.9 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/07. Feature Selection, Feature Learning, and Feature Extraction.mp4 10.9 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/6. Demo - Applying Factor Analysis to Reduce Dimensionality.mp4 10.8 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/12. Demo - Using Polynomial Features to Transform Data.mp4 10.7 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/3. Demo - Generate Manifold and Set up Helper Functions.mp4 10.7 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/10. Feature Selection Using Embedded Methods.mp4 10.7 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/3. Normalization and Cosine Similarity.mp4 10.6 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/08. Demo - Box Plot Visualization and Data Standardization.mp4 10.6 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/7. Demo - Using Autoencoders to Learn Efficient Representations of Data.mp4 10.4 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/05. Overfitting and the Bias-variance Trade-off.mp4 10.2 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/7. Bag-of-words Using the Tf-Idf Vectorizer.mp4 10.0 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/8. Designing and Training an Autoencoder.mp4 10.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/6. Demo - K-means Clustering with Cosine Similarity.mp4 9.7 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/04. Features and Labels.mp4 9.6 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/02. Tokenization and Visualizing Frequency Distributions.mp4 9.2 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/8. Perform Simple and Multiple Linear Regression.mp4 9.1 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/04. Demo - Using the KBinsDiscretizer to Categorize Numeric Values.mp4 9.1 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/04. Representing Images for Machine Learning.mp4 8.9 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/07. Pre-processing with Stopword Removal, Frequency Filtering, Building Features U.mp4 8.9 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/04. The Curse of Dimensionality.mp4 8.8 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/10. Demo - Standardize Data Using the Scale Function.mp4 8.8 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/6. Demo - Prepare Image Data to Feed an Autoencoder.mp4 8.8 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/8. Demo - Normalization Using L1, L2 and Max Norms.mp4 8.8 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/03. Prerequisites and Course Outline.mp4 8.8 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/03. Key Points and Descriptors.mp4 8.7 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/14. Label Binarizer to Perform One vs. Rest Encoding of Targets.mp4 8.7 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/14. Demo - Working with Chi Squared Distributed Input Features.mp4 8.7 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/05. Image Preprocessing to Build Robust Models.mp4 8.6 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/2. Understanding Manifold Learning.mp4 8.6 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/4. Dealing with Outliers.mp4 8.6 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/03. Demo - Convert Numeric Data to Binary Categories Using a Binarizer.mp4 8.5 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/2. Natural Language Processing Operations.mp4 8.5 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/04. Understanding Feature Selection Using Filter, Embedded, and Wrappe.mp4 8.5 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/6. Lemmatization.mp4 8.4 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/06. Extracting Features from Images.mp4 8.4 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/5. Exploring Feature Extraction Techniques/4. Feature Extraction from Text.mp4 8.4 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/09. Training, Validation, and Test Data.mp4 8.3 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/4. Demo - Manifold Learning Using Multidimensional Scaling and Spectral Embedding.mp4 8.3 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/11. Demo - The Boston Housing Prices Dataset - Exploration.mp4 8.3 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/5. Locality-sensitive Hashing.mp4 8.2 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/04. Pre-process Text Using a Stemmer, Build Features Using the Hashing Vectorizer.mp4 8.2 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/2. The Dummy Trap.mp4 8.2 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/6. Autoencoders.mp4 8.2 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/06. Demo - Setting up Helper Functions for Feature Selection.mp4 8.1 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/7. Perform Regression Analysis Using Machine Learning on Dummy Coded Categories.mp4 8.1 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/5. Exploring Feature Extraction Techniques/3. Feature Detection and Extraction from Images.mp4 8.1 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/3. Reducing Dimensions Using the Feature Hasher.mp4 8.0 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/08. Word Embeddings.mp4 8.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/5. Demo - Normalizing Data to Simplify Cosine Similarity Calculations.mp4 7.8 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/7. Building a Simple Regression Model Using Hashed Categorical Values.mp4 7.8 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/3. Avoiding the Dummy Trap.mp4 7.8 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/07. Co-occurence Vectors.mp4 7.7 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/4. Inverse Transform Using the Count Vectorizer.mp4 7.7 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/6. Generating N-grams Using NLTK.mp4 7.6 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/09. Types of Classification Tasks.mp4 7.6 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/05. Numeric Data.mp4 7.6 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/3. Demo - Classifying Image with Original Features.mp4 7.6 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/4. Demo - Building Linear Models Using Principal Components.mp4 7.5 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/07. Representing Pixels in Images.mp4 7.4 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/03. Exploring Contrast Coding Techniques.mp4 7.4 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/15. Demo - Applying Power Transformers to Get Normal Distributions.mp4 7.4 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/10. Block Views and Pooling.mp4 7.4 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/05. Demo - Selecting K Best Features Using Chi2 Analysis.mp4 7.3 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/03. Conceptual Overview of Different Feature Selection Techniques.mp4 7.3 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/10. K-fold Cross Validation.mp4 7.2 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/4. Frequency Filtering Using scikit-learn.mp4 7.2 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/07. Calculating and Visualizing Correlations Using Yellowbrick.mp4 7.1 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/08. Choosing between Label Encoding and One-hot Encoding.mp4 7.1 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/6. Demo - Manifold Learning Using Locally Linear Embedding.mp4 7.1 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/07. Choosing the Right Technique.mp4 7.1 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/4. Convolution Kernels.mp4 7.0 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/05. Scale Invariant Feature Transform (SIFT), DAISY, and Histogram of Oriented Gradients (HOG).mp4 7.0 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/02. Types of Data.mp4 7.0 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/5. Exploring Feature Extraction Techniques/2. Representing Images as Matrices and Image Preprocessing Techniques.mp4 7.0 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/04. Continuous and Categorical Data.mp4 6.9 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/2. Problems with Data.mp4 6.9 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/04. One-hot Encoding.mp4 6.9 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/4. Demo - Transforming Data Using K-means Cluster Centers.mp4 6.9 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/02. Dummy Coding vs. Contrast Coding.mp4 6.8 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/6. Feature Hashing with Dictionaries, Tuples, and Text Data.mp4 6.7 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/3. Dealing with Missing Values.mp4 6.7 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/05. The Machine Learning Workflow.mp4 6.6 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/11. Demo - Standardize Data Using the Standard Scalar Estimator and Apply Bessels Correction.mp4 6.6 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/17. Demo - Tranforming to a Normal Distribution Using the QuantileTransformer.mp4 6.6 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/03. Measuring Correlations.mp4 6.6 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/13. Image Augmentation Using Weather Transforms.mp4 6.5 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/09. Demo - Select Features Using Percentiles and Mutual Information Analysis.mp4 6.5 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/09. Installing Packages and Setting Up the Environment.mp4 6.5 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/3. Demo - Performing PCA to Reduce Dimensionality.mp4 6.5 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/05. Building Features Using the Count Vectorizer.mp4 6.3 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/08. Feature Combination and Dimensionality Reduction.mp4 6.1 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/8. Performing Linear Regression Using Machine Learning with One-hot Encoded Categories.mp4 6.0 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/02. Feature Detection and Its Importance.mp4 6.0 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/09. Building Features Using Bag-of-n-grams Model.mp4 5.9 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/5. Demo - Manifold Learning Using t-SNE and Isomap.mp4 5.8 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/09. Performing Regression Analysis Using Orthogonal Polynomial Encoding.mp4 5.7 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/08. Demo - Find the Right Value for K Using ANOVA.mp4 5.6 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/11. One-hot Encoding on a Pandas Data Frame Column.mp4 5.5 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/07. Label Encoding and One-hot Encoding.mp4 5.5 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/06. Pre-processing with Stopword Removal, Building Features Using Count Vectorizer.mp4 5.4 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/06. Categorical Data.mp4 5.4 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/07. Demo - Find the Right Value for K Using Chi2 Analysis.mp4 5.3 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/09. Standard Scaler.mp4 5.3 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/06. Techniques to Reduce Complexity.mp4 5.2 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/07. Feature Detection Using DAISY Descriptors.mp4 5.2 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/5. Hashing.mp4 5.2 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/3. Bucketing Continuous Data Using Pandas.mp4 5.1 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/04. Scaling and Standardization.mp4 5.1 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/02. Statistical Techniques for Feature Selection.mp4 5.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/05. Demo - Using Bin Values to Flag Outliers.mp4 5.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/08. Demo - Scaling with the MinMaxScaler.mp4 5.0 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/15. Multilabel Binarizer for Encoding Multilabel Targets.mp4 5.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/06. Understanding Variance.mp4 5.0 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/2. Bucketing Continuous Data.mp4 4.9 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/08. Building Features Using the Tf-Idf Vectorizer.mp4 4.9 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/12. Robust Scaler.mp4 4.8 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/10. Demo - Performing Custom Transforms Using the FunctionTransformer.mp4 4.8 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/05. Mean, Variance, and Standard Deviation.mp4 4.8 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/2. K-means Model Stacking.mp4 4.6 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/05. Count Vectors.mp4 4.6 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/2. Bag-of-words and Bag-of-n-grams.mp4 4.4 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/10. Demo - Establishing a Baseline Model.mp4 4.1 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/06. Tf-Idf Vectors.mp4 4.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 4.0 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.9 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/1. Module Overview.mp4 3.9 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/02. Naive Bayes for Classification.mp4 3.9 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/5. Understanding Factor Analysis.mp4 3.8 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/2. Feature Hashing.mp4 3.8 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/06. Components of Feature Engineering.mp4 3.7 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/2. Dictionary Learning.mp4 3.6 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.6 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.6 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/7. Understanding Linear Discriminant Analysis.mp4 3.5 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.5 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/07. Demo - Scaling with the MaxAbsScaler.mp4 3.4 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/02. Converting Continuous Data to Categorical.mp4 3.4 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.mp4 3.0 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/8. Summary and Further Study.mp4 3.0 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/1. Module Overview.mp4 2.9 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/7. L1, L2 and Max Norms.mp4 2.8 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/11. Generating Polynomial Features.mp4 2.7 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/18. Summary and Further Study.mp4 2.6 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/1. Module Overview.mp4 2.6 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/03. Prerequisites and Course Outline.mp4 2.5 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/1. Module Overview.mp4 2.5 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/11. Summary and Further Study.mp4 2.5 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/1. Module Overview.mp4 2.4 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/13. Summary.mp4 2.4 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/10. Summary and Further Study.mp4 2.3 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/12. One-hot Encoding Using pd.get_dummies().mp4 2.2 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/02. Module Overview.mp4 2.2 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/03. Prerequisites and Course Outline.mp4 2.1 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/11. Summary.mp4 2.1 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/01. Module Overview.mp4 2.1 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/5. Exploring Feature Extraction Techniques/1. Module Overview.mp4 2.1 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/8. Summary and Further Study.mp4 2.1 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/1. Module Overview.mp4 2.1 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/1. Module Overview.mp4 2.1 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/11. Module Summary.mp4 2.1 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/2. What Is Normalization.mp4 2.0 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/02. Module Overview.mp4 2.0 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/10. Module Summary.mp4 2.0 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/9. Summary and Further Study.mp4 2.0 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/12. Module Summary.mp4 2.0 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/exercise.7z 2.0 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/7. Module Summary.mp4 1.9 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/8. Module Summary.mp4 1.9 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/16. Module Summary.mp4 1.9 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/03. Prerequisites and Course Outline.mp4 1.9 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/08. Drawbacks of Reducing Complexity.mp4 1.9 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/06. Scaling Data.mp4 1.9 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/02. Module Overview.mp4 1.9 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/14. Summary.mp4 1.8 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/13. Transforming Features to Gaussian-like Distributions Using Power Transformers.mp4 1.8 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/8. Module Summary.mp4 1.8 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/1. Module Overview.mp4 1.8 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/02. Module Overview.mp4 1.8 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/10. Module Summary.mp4 1.8 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/01. Module Overview.mp4 1.8 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/5. Exploring Feature Extraction Techniques/5. Module Summary.mp4 1.8 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/9. Module Summary.mp4 1.8 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/11. Module Summary.mp4 1.8 MB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/01. Module Overview.mp4 1.7 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/8. Summary.mp4 1.7 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/02. Module Overview.mp4 1.7 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/1. Module Overview.mp4 1.7 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/1. Module Overview.mp4 1.7 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/03. Prerequisites and Course Outline.mp4 1.7 MB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/01. Module Overview.mp4 1.7 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/01. Module Overview.mp4 1.6 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/14. Module Summary.mp4 1.6 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/01. Module Overview.mp4 1.6 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/01. Module Overview.mp4 1.6 MB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/9. Module Summary.mp4 1.5 MB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/03. Prerequisites and Course Outline.mp4 1.5 MB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/9. Summary.mp4 1.4 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/9. Summary.mp4 1.4 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/16. Transforming Data to Normal or Uniform Distributions Using Quantile Transformers.mp4 1.4 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/exercise.7z 1.4 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/02. Module Overview.mp4 1.3 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/1. Module Overview.mp4 1.0 MB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/09. Custom Transformations.mp4 670.3 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/01. Version Check.mp4 636.5 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/01. Version Check.mp4 576.9 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/01. Version Check.mp4 576.3 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/01. Version Check.mp4 566.8 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/01. Version Check.mp4 566.4 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/01. Version Check.mp4 559.4 kB
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  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/7. Reading and Exploring the Dataset.vtt 12.3 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/07. Regression Using Helmert Encoding.vtt 11.6 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/5. Applying Different Techniques to Handle Missing Values.vtt 11.6 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/5. Feature Detection Using Convolution Kernels.vtt 11.5 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/03. Classification Using the Hashing Vectorizer.vtt 11.5 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/04. Demo - Selecting Features Using a Variance Threshold.vtt 11.4 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/3. Normalization and Cosine Similarity.vtt 11.3 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/07. Feature Selection, Feature Learning, and Feature Extraction.vtt 11.1 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/05. Overfitting and the Bias-variance Trade-off.vtt 11.0 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/07. Demo - Calculating Mean, Variance, and Standard Deviation.vtt 11.0 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/2. Understanding Principal Components Analysis.vtt 11.0 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/13. Demo - Scaling Data Using the Robust Scaler.vtt 10.9 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/6. Detecting and Handling Outliers.vtt 10.7 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/4. Dummy Coding to Overcome Limitations of One-hot Encoding.vtt 10.6 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/4. Demo - Cosine Similarity and the L2 Norm.vtt 10.4 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/5. Autoencoding.vtt 10.3 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/6. Similar Documents Using Jaccard Index and Locality-sensitive Hashing.vtt 10.3 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/09. Demo - The Diabetes Dataset - Exploration.vtt 10.3 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/7. Demo - Performing Kernel PCA to Reduce Complexity in Nonlinear Data.vtt 10.3 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/06. Feature Detection and Extraction Using SIFT.vtt 10.3 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/3. Stopword Removal Using NLTK and scikit-learn.vtt 10.2 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/10. Working with Geospatial Features.vtt 10.2 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/04. Features and Labels.vtt 10.1 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/08. Demo - Box Plot Visualization and Data Standardization.vtt 10.0 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/3. Bag-of-words Using the Count Vectorizer.vtt 10.0 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/06. Regression Using Backward Difference Encoding.vtt 9.7 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/06. Calculating and Visualizing Correlations Using Pandas.vtt 9.6 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/10. Demo - Dictionary Learning on Handwritten Digits.vtt 9.6 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/03. Prerequisites and Course Outline.vtt 9.6 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/5. Regression Analysis with Dummy or Treatment Coding.vtt 9.5 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/13. Label Encoding to Convert Categorical Data to Ordinal.vtt 9.5 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/08. Feature Selection Using Filter Methods.vtt 9.5 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/04. Creating Feature Vectors from Text Data.vtt 9.4 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/04. The Curse of Dimensionality.vtt 9.4 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/4. Reducing Dimensions at Scale Using the Hashing Vectorizer.vtt 9.4 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/5. Exploring Feature Extraction Techniques/4. Feature Extraction from Text.vtt 9.3 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/12. Normalization and ZCA Whitening.vtt 9.3 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/4. Categorizing Continuous Data Using the KBinsDiscretizer.vtt 9.3 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/6. Dummy Coding Using Patsy.vtt 9.3 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/03. Key Points and Descriptors.vtt 9.3 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/04. Applying Keypoint Preserving Transformations.vtt 9.2 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/04. Regression Analysis Using Simple Effect Coding.vtt 9.1 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/09. Feature Selection Using Wrapper Methods.vtt 9.1 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/05. Performing Linear Regression Using Machine Learning with Simple Effect Coding.vtt 9.0 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/04. Understanding Feature Selection Using Filter, Embedded, and Wrappe.vtt 9.0 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/08. Feature Detection Using Histogram of Oriented Gradients.vtt 9.0 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/7. Parts-of-speech Tagging.vtt 8.9 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/4. Dealing with Outliers.vtt 8.9 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/8. Demo - Performing Linear Discriminant Analysis to Reorient Data.vtt 8.9 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/12. Demo - Using Polynomial Features to Transform Data.vtt 8.8 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/5. Stemming.vtt 8.7 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/2. Natural Language Processing Operations.vtt 8.7 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/09. Training, Validation, and Test Data.vtt 8.7 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/05. Image Preprocessing to Build Robust Models.vtt 8.6 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/09. Optical Character Recognition Using Tesseract.vtt 8.5 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/3. Demo - Generate Manifold and Set up Helper Functions.vtt 8.5 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/6. Demo - Applying Factor Analysis to Reduce Dimensionality.vtt 8.5 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/3. Sparse Representations Using Dictionary Learning.vtt 8.5 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/10. Demo - Standardize Data Using the Scale Function.vtt 8.4 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/12. Demo - Kitchen Sink Regression to Establish a Baseline Model.vtt 8.4 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/04. Demo - Using the KBinsDiscretizer to Categorize Numeric Values.vtt 8.3 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/7. Demo - Using Autoencoders to Learn Efficient Representations of Data.vtt 8.2 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/05. Feature Selection Using Missing Value Ratio.vtt 8.2 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/5. Exploring Feature Extraction Techniques/3. Feature Detection and Extraction from Images.vtt 8.2 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/5. Locality-sensitive Hashing.vtt 8.2 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/03. Demo - Convert Numeric Data to Binary Categories Using a Binarizer.vtt 8.1 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/10. Sentence and Word Tokenization.vtt 8.1 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/8. Demo - Normalization Using L1, L2 and Max Norms.vtt 8.0 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/10. Feature Selection Using Embedded Methods.vtt 8.0 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/5. Bag-of-n-grams Using the Count Vectorizer.vtt 8.0 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/03. Performing Normalization Using Different Techniques.vtt 8.0 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/08. Word Embeddings.vtt 7.9 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/05. Loading and Transforming Images.vtt 7.9 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/10. One-hot Encoding with Known and Unknown Categories.vtt 7.8 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/2. The Dummy Trap.vtt 7.8 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/3. Dealing with Missing Values.vtt 7.6 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/6. Autoencoders.vtt 7.6 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/09. Resizing, Rescaling, Rotating, and Flipping Images.vtt 7.6 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/07. Co-occurence Vectors.vtt 7.6 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/2. Understanding Manifold Learning.vtt 7.5 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/07. Detecting Keypoints and Descriptors to Perform Image Matching.vtt 7.5 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/05. Numeric Data.vtt 7.5 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/08. Generating Equally Spaced Categories to Perform Orthogonal Polynomial Encoding.vtt 7.5 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/05. Scale Invariant Feature Transform (SIFT), DAISY, and Histogram of Oriented Gradients (HOG).vtt 7.3 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/03. Conceptual Overview of Different Feature Selection Techniques.vtt 7.3 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/04. Representing Images for Machine Learning.vtt 7.2 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/5. Exploring Feature Extraction Techniques/2. Representing Images as Matrices and Image Preprocessing Techniques.vtt 7.2 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/06. Demo - Setting up Helper Functions for Feature Selection.vtt 7.1 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/8. Perform Simple and Multiple Linear Regression.vtt 7.1 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/03. Measuring Correlations.vtt 7.1 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/11. Denoising Images.vtt 7.1 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/06. Working with Images as Arrays.vtt 7.1 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/07. Choosing the Right Technique.vtt 7.0 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/7. Reading and Preprocessing Images.vtt 7.0 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/6. Demo - Prepare Image Data to Feed an Autoencoder.vtt 7.0 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/09. Types of Classification Tasks.vtt 6.9 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/02. Types of Data.vtt 6.9 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/14. Demo - Working with Chi Squared Distributed Input Features.vtt 6.8 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/04. One-hot Encoding.vtt 6.8 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/09. Extracting Features from Dates.vtt 6.8 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/8. Designing and Training an Autoencoder.vtt 6.7 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/08. Working with Color and Color Spaces.vtt 6.6 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/5. Demo - Normalizing Data to Simplify Cosine Similarity Calculations.vtt 6.6 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/3. Demo - Classifying Image with Original Features.vtt 6.6 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/05. The Machine Learning Workflow.vtt 6.6 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/3. Avoiding the Dummy Trap.vtt 6.6 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/08. Choosing between Label Encoding and One-hot Encoding.vtt 6.5 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/05. Demo - Selecting K Best Features Using Chi2 Analysis.vtt 6.5 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/03. Exploring Contrast Coding Techniques.vtt 6.4 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/04. Continuous and Categorical Data.vtt 6.3 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/08. Feature Combination and Dimensionality Reduction.vtt 6.3 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/4. Convolution Kernels.vtt 6.3 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/7. Bag-of-words Using the Tf-Idf Vectorizer.vtt 6.3 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/3. Preparing Data for Machine Learning/2. Problems with Data.vtt 6.3 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/10. K-fold Cross Validation.vtt 6.2 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/02. Statistical Techniques for Feature Selection.vtt 6.2 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/02. Feature Detection and Its Importance.vtt 6.2 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/04. Scaling and Standardization.vtt 6.1 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/4. Demo - Manifold Learning Using Multidimensional Scaling and Spectral Embedding.vtt 6.0 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/02. Tokenization and Visualizing Frequency Distributions.vtt 6.0 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/4. Demo - Building Linear Models Using Principal Components.vtt 6.0 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/05. Mean, Variance, and Standard Deviation.vtt 6.0 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/3. Demo - Performing PCA to Reduce Dimensionality.vtt 6.0 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/14. Label Binarizer to Perform One vs. Rest Encoding of Targets.vtt 5.9 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/02. Dummy Coding vs. Contrast Coding.vtt 5.9 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/11. Plotting Word Frequency Distributions.vtt 5.9 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/17. Demo - Tranforming to a Normal Distribution Using the QuantileTransformer.vtt 5.8 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/08. Extracting Text from Images Using OCR.vtt 5.8 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/11. Demo - Standardize Data Using the Standard Scalar Estimator and Apply Bessels Correction.vtt 5.7 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/09. Standard Scaler.vtt 5.7 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/15. Demo - Applying Power Transformers to Get Normal Distributions.vtt 5.7 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/06. Techniques to Reduce Complexity.vtt 5.7 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/3. Reducing Dimensions Using the Feature Hasher.vtt 5.7 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/6. Demo - K-means Clustering with Cosine Similarity.vtt 5.6 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/12. Robust Scaler.vtt 5.6 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/11. Demo - The Boston Housing Prices Dataset - Exploration.vtt 5.5 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/07. Label Encoding and One-hot Encoding.vtt 5.4 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/06. Categorical Data.vtt 5.4 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/2. Bucketing Continuous Data.vtt 5.3 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/6. Implementing Feature Extraction/06. Extracting Features from Images.vtt 5.3 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/4. Demo - Transforming Data Using K-means Cluster Centers.vtt 5.3 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/7. Perform Regression Analysis Using Machine Learning on Dummy Coded Categories.vtt 5.2 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/07. Pre-processing with Stopword Removal, Frequency Filtering, Building Features U.vtt 5.2 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/6. Lemmatization.vtt 5.1 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/06. Understanding Variance.vtt 5.0 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/7. Building a Simple Regression Model Using Hashed Categorical Values.vtt 5.0 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/10. Block Views and Pooling.vtt 5.0 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/07. Representing Pixels in Images.vtt 5.0 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/09. Demo - Select Features Using Percentiles and Mutual Information Analysis.vtt 4.9 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/playlist.m3u 4.9 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/6. Generating N-grams Using NLTK.vtt 4.9 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/09. Installing Packages and Setting Up the Environment.vtt 4.9 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/07. Demo - Find the Right Value for K Using Chi2 Analysis.vtt 4.6 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/5. Hashing.vtt 4.6 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/10. Demo - Performing Custom Transforms Using the FunctionTransformer.vtt 4.6 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/05. Count Vectors.vtt 4.6 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/3. Using Statistical Techniques for Feature Selection/08. Demo - Find the Right Value for K Using ANOVA.vtt 4.5 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/6. Feature Hashing with Dictionaries, Tuples, and Text Data.vtt 4.5 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/playlist.m3u 4.4 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/5. Demo - Manifold Learning Using t-SNE and Isomap.vtt 4.4 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/4. Understanding and Implementing Feature Selection/07. Calculating and Visualizing Correlations Using Yellowbrick.vtt 4.3 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/04. Pre-process Text Using a Stemmer, Build Features Using the Hashing Vectorizer.vtt 4.3 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/playlist.m3u 4.3 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/6. Demo - Manifold Learning Using Locally Linear Embedding.vtt 4.3 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/02. Naive Bayes for Classification.vtt 4.3 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/08. Demo - Scaling with the MinMaxScaler.vtt 4.3 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/3. Building Feature Vector Representations of Text/2. Bag-of-words and Bag-of-n-grams.vtt 4.3 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/05. Demo - Using Bin Values to Flag Outliers.vtt 4.3 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/4. Simplifying Text Processing Using Natural Language Processing/4. Frequency Filtering Using scikit-learn.vtt 4.2 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/playlist.m3u 4.2 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/playlist.m3u 4.1 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/2. Understanding the Role of Features in Machine Learning/06. Components of Feature Engineering.vtt 4.1 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/3. Understanding and Implementing Dummy Coding/8. Performing Linear Regression Using Machine Learning with One-hot Encoded Categories.vtt 4.1 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/2. Representing Text as Features for Machine Learning/06. Tf-Idf Vectors.vtt 4.0 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/3. Building Features Using Normalization/7. L1, L2 and Max Norms.vtt 3.9 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/02. Converting Continuous Data to Categorical.vtt 3.9 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/5. Reducing Complexity in Nonlinear Data/1. Module Overview.vtt 3.9 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/5. Implementing Bin Counting and Feature Hashing/3. Bucketing Continuous Data Using Pandas.vtt 3.8 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/2. K-means Model Stacking.vtt 3.7 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/11. One-hot Encoding on a Pandas Data Frame Column.vtt 3.7 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/5. Reducing Dimensions in Text Using Hashing/2. Feature Hashing.vtt 3.7 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/2. Understanding the Need for Dimensionality Reduction/10. Demo - Establishing a Baseline Model.vtt 3.6 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/4. Understanding and Implementing Contrast Coding/09. Performing Regression Analysis Using Orthogonal Polynomial Encoding.vtt 3.6 kB
  • B1. Building Features from Nominal Data (Janani Ravi, 2019)/2. Implementing Approaches to Working with Categorical Data/15. Multilabel Binarizer for Encoding Multilabel Targets.vtt 3.6 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/3. Detecting Features and Text in Images/07. Feature Detection Using DAISY Descriptors.vtt 3.6 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/7. Understanding Linear Discriminant Analysis.vtt 3.5 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/2. Representing Images as Features for Machine Learning/13. Image Augmentation Using Weather Transforms.vtt 3.5 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/4. Simplifying Image Processing Using Dimensionality Reduction/2. Dictionary Learning.vtt 3.5 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/5. Understanding Factor Analysis.vtt 3.4 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/11. Generating Polynomial Features.vtt 3.4 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/05. Building Features Using the Count Vectorizer.vtt 3.3 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/6. Applying Text Feature Extraction Techniques to Machine Learning/09. Building Features Using Bag-of-n-grams Model.vtt 3.3 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/playlist.m3u 3.2 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/18. Summary and Further Study.vtt 3.2 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/4. Building Features Using Scaling and Transformations/07. Demo - Scaling with the MaxAbsScaler.vtt 3.1 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/4. Reducing Complexity in Linear Data/1. Module Overview.vtt 3.1 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 3.0 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 3.0 kB
  • B2. Reducing Complexity in Data (Janani Ravi, 2019)/6. Dimensionality Reduction Using Clustering and Autoencoding Techniques/8. Summary and Further Study.vtt 3.0 kB
  • A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 2.9 kB
  • C2. Building Features from Image Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 2.9 kB
  • C1. Building Features from Text Data (Janani Ravi, 2019)/1. Course Overview/1. Course Overview.vtt 2.8 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/2. Using Numeric Data in Machine Learning Algorithms/03. Prerequisites and Course Outline.vtt 2.8 kB
  • A2. Building Features from Numeric Data (Janani Ravi, 2019)/~i.txt 2.7 kB
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