12-16-2024, 08:20 PM
![[Image: 537661809_oip.jpg]](https://img100.pixhost.to/images/428/537661809_oip.jpg)
1.95 GB | 00:06:47 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
1 Course Overview.mp4 (3.35 MB)
01 Version Check.mp4 (621.59 KB)
02 Module Overview.mp4 (1.72 MB)
03 Prerequisites and Course Outline.mp4 (1.61 MB)
04 Features and Labels.mp4 (9.15 MB)
05 The Machine Learning Workflow.mp4 (6.33 MB)
06 Components of Feature Engineering.mp4 (3.57 MB)
08 Feature Combination and Dimensionality Reduction.mp4 (5.78 MB)
09 Training, Validation, and Test Data.mp4 (7.94 MB)
10 K-fold Cross Validation.mp4 (6.9 MB)
11 Module Summary.mp4 (1.68 MB)
1 Module Overview.mp4 (2.29 MB)
2 Problems with Data.mp4 (6.6 MB)
3 Dealing with Missing Values.mp4 (6.39 MB)
4 Dealing with Outliers.mp4 (8.16 MB)
5 Applying Different Techniques to Handle Missing Values.mp4 (13.89 MB)
6 Detecting and Handling Outliers.mp4 (12.97 MB)
7 Reading and Exploring the Dataset.mp4 (16.31 MB)
8 Perform Simple and Multiple Linear Regression.mp4 (8.71 MB)
9 Module Summary.mp4 (1.46 MB)
01 Module Overview.mp4 (2.02 MB)
02 Types of Data.mp4 (6.66 MB)
03 Measuring Correlations.mp4 (6.26 MB)
05 Feature Selection Using Missing Value Ratio.mp4 (10.75 MB)
06 Calculating and Visualizing Correlations Using Pandas.mp4 (14.42 MB)
08 Feature Selection Using Filter Methods.mp4 (13.32 MB)
09 Feature Selection Using Wrapper Methods.mp4 (12.8 MB)
10 Feature Selection Using Embedded Methods.mp4 (10.19 MB)
11 Module Summary.mp4 (1.96 MB)
1 Module Overview.mp4 (2.02 MB)
3 Feature Detection and Extraction from Images.mp4 (7.71 MB)
4 Feature Extraction from Text.mp4 (8.01 MB)
5 Module Summary.mp4 (1.69 MB)
01 Module Overview.mp4 (1.69 MB)
02 Tokenization and Visualizing Frequency Distributions.mp4 (8.82 MB)
03 Performing Normalization Using Different Techniques.mp4 (11.52 MB)
04 Creating Feature Vectors from Text Data.mp4 (13.47 MB)
05 Loading and Transforming Images.mp4 (12.23 MB)
06 Extracting Features from Images.mp4 (8.03 MB)
07 Detecting Keypoints and Descriptors to Perform Image Matching.mp4 (14.53 MB)
08 Extracting Text from Images Using OCR.mp4 (14.84 MB)
09 Extracting Features from Dates.mp4 (10.44 MB)
10 Working with Geospatial Features.mp4 (17.9 MB)
11 Summary and Further Study.mp4 (2.36 MB)
1 Course Overview.mp4 (3.82 MB)
01 Version Check.mp4 (553.51 KB)
02 Module Overview.mp4 (1.24 MB)
03 Prerequisites and Course Outline.mp4 (2.42 MB)
04 Scaling and Standardization.mp4 (4.87 MB)
05 Mean, Variance, and Standard Deviation.mp4 (4.53 MB)
06 Understanding Variance.mp4 (4.76 MB)
07 Demo - Calculating Mean, Variance, and Standard Deviation.mp4 (11.95 MB)
08 Demo - Box Plot Visualization and Data Standardization.mp4 (10.07 MB)
09 Standard Scaler.mp4 (5.05 MB)
10 Demo - Standardize Data Using the Scale Function.mp4 (8.4 MB)
12 Robust Scaler.mp4 (4.6 MB)
13 Demo - Scaling Data Using the Robust Scaler.mp4 (11.28 MB)
14 Summary.mp4 (1.76 MB)
1 Module Overview.mp4 (989.65 KB)
2 What Is Normalization.mp4 (1.95 MB)
3 Normalization and Cosine Similarity.mp4 (10.13 MB)
4 Demo - Cosine Similarity and the L2 Norm.mp4 (11.43 MB)
5 Demo - Normalizing Data to Simplify Cosine Similarity Calculations.mp4 (7.48 MB)
6 Demo - K-means Clustering with Cosine Similarity.mp4 (9.26 MB)
7 L1, L2 and Max Norms.mp4 (2.67 MB)
8 Demo - Normalization Using L1, L2 and Max Norms.mp4 (8.38 MB)
9 Summary.mp4 (1.32 MB)
01 Module Overview.mp4 (1.51 MB)
02 Converting Continuous Data to Categorical.mp4 (3.2 MB)
03 Demo - Convert Numeric Data to Binary Categories Using a Binarizer.mp4 (8.13 MB)
04 Demo - Using the KBinsDiscretizer to Categorize Numeric Values.mp4 (8.65 MB)
05 Demo - Using Bin Values to Flag Outliers.mp4 (4.79 MB)
06 Scaling Data.mp4 (1.78 MB)
07 Demo - Scaling with the MaxAbsScaler.mp4 (3.25 MB)
08 Demo - Scaling with the MinMaxScaler.mp4 (4.78 MB)
09 Custom Transformations.mp4 (654.56 KB)
10 Demo - Performing Custom Transforms Using the FunctionTransformer.mp4 (4.59 MB)
11 Generating Polynomial Features.mp4 (2.61 MB)
12 Demo - Using Polynomial Features to Transform Data.mp4 (10.21 MB)
14 Demo - Working with Chi Squared Distributed Input Features.mp4 (8.25 MB)
15 Demo - Applying Power Transformers to Get Normal Distributions.mp4 (7.04 MB)
17 Demo - Tranforming to a Normal Distribution Using the QuantileTransformer.mp4 (6.31 MB)
18 Summary and Further Study.mp4 (2.52 MB)
1 Course Overview.mp4 (2.9 MB)
01 Version Check.mp4 (562.75 KB)
02 Module Overview.mp4 (1.94 MB)
03 Prerequisites and Course Outline.mp4 (2.05 MB)
04 Continuous and Categorical Data.mp4 (6.61 MB)
05 Numeric Data.mp4 (7.23 MB)
06 Categorical Data.mp4 (5.12 MB)
07 Label Encoding and One-hot Encoding.mp4 (5.22 MB)
08 Choosing between Label Encoding and One-hot Encoding.mp4 (6.81 MB)
09 Types of Classification Tasks.mp4 (7.24 MB)
10 One-hot Encoding with Known and Unknown Categories.mp4 (10.75 MB)
11 One-hot Encoding on a Pandas Data Frame Column.mp4 (5.26 MB)
12 One-hot Encoding Using pd get dummies().mp4 (2.1 MB)
13 Label Encoding to Convert Categorical Data to Ordinal.mp4 (12.67 MB)
14 Label Binarizer to Perform One vs Rest Encoding of Targets.mp4 (8.29 MB)
15 Multilabel Binarizer for Encoding Multilabel Targets.mp4 (4.76 MB)
16 Module Summary.mp4 (1.79 MB)
1 Module Overview.mp4 (2.45 MB)
2 The Dummy Trap.mp4 (7.81 MB)
3 Avoiding the Dummy Trap.mp4 (7.4 MB)
4 Dummy Coding to Overcome Limitations of One-hot Encoding.mp4 (11.15 MB)
5 Regression Analysis with Dummy or Treatment Coding.mp4 (12 MB)
6 Dummy Coding Using Patsy.mp4 (12.98 MB)
7 Perform Regression Analysis Using Machine Learning on Dummy Coded Categories.mp4 (7.74 MB)
8 Performing Linear Regression Using Machine Learning with One-hot Encoded Categories.mp4 (5.69 MB)
9 Module Summary.mp4 (1.69 MB)
01 Module Overview.mp4 (1.66 MB)
02 Dummy Coding vs Contrast Coding.mp4 (6.52 MB)
03 Exploring Contrast Coding Techniques.mp4 (7.04 MB)
04 Regression Analysis Using Simple Effect Coding.mp4 (11.87 MB)
05 Performing Linear Regression Using Machine Learning with Simple Effect Coding.mp4 (12.55 MB)
06 Regression Using Backward Difference Encoding.mp4 (13.68 MB)
07 Regression Using Helmert Encoding.mp4 (15.38 MB)
08 Generating Equally Spaced Categories to Perform Orthogonal Polynomial Encoding.mp4 (10.49 MB)
09 Performing Regression Analysis Using Orthogonal Polynomial Encoding.mp4 (5.46 MB)
10 Module Summary.mp4 (1.91 MB)
1 Module Overview.mp4 (2.37 MB)
2 Bucketing Continuous Data.mp4 (4.66 MB)
3 Bucketing Continuous Data Using Pandas.mp4 (4.89 MB)
4 Categorizing Continuous Data Using the KBinsDiscretizer.mp4 (13.13 MB)
5 Hashing.mp4 (4.93 MB)
6 Feature Hashing with Dictionaries, Tuples, and Text Data.mp4 (6.41 MB)
7 Building a Simple Regression Model Using Hashed Categorical Values.mp4 (7.41 MB)
8 Summary and Further Study.mp4 (1.99 MB)
1 Course Overview.mp4 (3.76 MB)
01 Version Check.mp4 (553.1 KB)
02 Module Overview.mp4 (1.64 MB)
03 Prerequisites and Course Outline.mp4 (8.38 MB)
04 The Curse of Dimensionality.mp4 (8.43 MB)
05 Overfitting and the Bias-variance Trade-off.mp4 (9.7 MB)
06 Techniques to Reduce Complexity.mp4 (5 MB)
07 Choosing the Right Technique.mp4 (6.75 MB)
08 Drawbacks of Reducing Complexity.mp4 (1.78 MB)
09 Demo - The Diabetes Dataset - Exploration.mp4 (11.97 MB)
10 Demo - Establishing a Baseline Model.mp4 (3.93 MB)
11 Demo - The Boston Housing Prices Dataset - Exploration.mp4 (7.89 MB)
12 Demo - Kitchen Sink Regression to Establish a Baseline Model.mp4 (10.44 MB)
13 Summary.mp4 (2.28 MB)
01 Module Overview.mp4 (1.56 MB)
02 Statistical Techniques for Feature Selection.mp4 (4.79 MB)
03 Conceptual Overview of Different Feature Selection Techniques.mp4 (6.98 MB)
04 Demo - Selecting Features Using a Variance Threshold.mp4 (12.92 MB)
05 Demo - Selecting K Best Features Using Chi2 Analysis.mp4 (7 MB)
06 Demo - Setting up Helper Functions for Feature Selection.mp4 (7.77 MB)
07 Demo - Find the Right Value for K Using Chi2 Analysis.mp4 (5.09 MB)
08 Demo - Find the Right Value for K Using ANOVA.mp4 (5.31 MB)
09 Demo - Select Features Using Percentiles and Mutual Information Analysis.mp4 (6.21 MB)
10 Demo - Dictionary Learning on Handwritten Digits.mp4 (10.69 MB)
11 Summary.mp4 (2.04 MB)
1 Module Overview.mp4 (2.76 MB)
2 Understanding Principal Components Analysis.mp4 (10.44 MB)
3 Demo - Performing PCA to Reduce Dimensionality.mp4 (6.16 MB)
4 Demo - Building Linear Models Using Principal Components.mp4 (7.13 MB)
5 Understanding Factor Analysis.mp4 (3.61 MB)
6 Demo - Applying Factor Analysis to Reduce Dimensionality.mp4 (10.27 MB)
7 Understanding Linear Discriminant Analysis.mp4 (3.35 MB)
8 Demo - Performing Linear Discriminant Analysis to Reorient Data.mp4 (10.48 MB)
9 Summary.mp4 (1.38 MB)
1 Module Overview.mp4 (3.69 MB)
2 Understanding Manifold Learning.mp4 (8.2 MB)
3 Demo - Generate Manifold and Set up Helper Functions.mp4 (10.2 MB)
4 Demo - Manifold Learning Using Multidimensional Scaling and Spectral Embedding.mp4 (7.92 MB)
5 Demo - Manifold Learning Using t-SNE and Isomap.mp4 (5.51 MB)
6 Demo - Manifold Learning Using Locally Linear Embedding.mp4 (6.8 MB)
7 Demo - Performing Kernel PCA to Reduce Complexity in Nonlinear Data.mp4 (13.68 MB)
8 Summary.mp4 (1.66 MB)
1 Module Overview.mp4 (1.96 MB)
2 K-means Model Stacking.mp4 (4.38 MB)
3 Demo - Classifying Image with Original Features.mp4 (7.21 MB)
4 Demo - Transforming Data Using K-means Cluster Centers.mp4 (6.56 MB)
5 Autoencoding.mp4 (11.51 MB)
6 Demo - Prepare Image Data to Feed an Autoencoder.mp4 (8.39 MB)
8 Summary and Further Study.mp4 (2.84 MB)
1 Course Overview.mp4 (3.44 MB)
01 Version Check.mp4 (546.32 KB)
02 Module Overview.mp4 (2.07 MB)
03 Prerequisites and Course Outline.mp4 (1.79 MB)
04 One-hot Encoding.mp4 (6.57 MB)
05 Count Vectors.mp4 (4.37 MB)
06 Tf-Idf Vectors.mp4 (3.86 MB)
07 Co-occurence Vectors.mp4 (7.33 MB)
08 Word Embeddings.mp4 (7.61 MB)
09 Installing Packages and Setting Up the Environment.mp4 (6.18 MB)
10 Sentence and Word Tokenization.mp4 (13.89 MB)
11 Plotting Word Frequency Distributions.mp4 (11.73 MB)
12 Module Summary.mp4 (1.88 MB)
1 Module Overview.mp4 (1.98 MB)
2 Bag-of-words and Bag-of-n-grams.mp4 (4.16 MB)
3 Bag-of-words Using the Count Vectorizer.mp4 (14.09 MB)
4 Inverse Transform Using the Count Vectorizer.mp4 (7.3 MB)
5 Bag-of-n-grams Using the Count Vectorizer.mp4 (15.06 MB)
6 Generating N-grams Using NLTK.mp4 (7.27 MB)
7 Bag-of-words Using the Tf-Idf Vectorizer.mp4 (9.54 MB)
8 Module Summary.mp4 (1.84 MB)
1 Module Overview.mp4 (1.72 MB)
2 Natural Language Processing Operations.mp4 (8.1 MB)
3 Stopword Removal Using NLTK and scikit-learn.mp4 (14.71 MB)
4 Frequency Filtering Using scikit-learn.mp4 (6.89 MB)
5 Stemming.mp4 (11.45 MB)
6 Lemmatization.mp4 (8.04 MB)
7 Parts-of-speech Tagging.mp4 (13.64 MB)
8 Module Summary.mp4 (1.74 MB)
1 Module Overview.mp4 (1.62 MB)
2 Feature Hashing.mp4 (3.6 MB)
3 Reducing Dimensions Using the Feature Hasher.mp4 (7.65 MB)
4 Reducing Dimensions at Scale Using the Hashing Vectorizer.mp4 (13.23 MB)
5 Locality-sensitive Hashing.mp4 (7.84 MB)
6 Similar Documents Using Jaccard Index and Locality-sensitive Hashing.mp4 (16.01 MB)
7 Module Summary.mp4 (1.85 MB)
01 Module Overview.mp4 (1.58 MB)
02 Naive Bayes for Classification.mp4 (3.68 MB)
03 Classification Using the Hashing Vectorizer.mp4 (16.61 MB)
05 Building Features Using the Count Vectorizer.mp4 (6.01 MB)
08 Building Features Using the Tf-Idf Vectorizer.mp4 (4.64 MB)
09 Building Features Using Bag-of-n-grams Model.mp4 (5.59 MB)
10 Summary and Further Study.mp4 (2.15 MB)
1 Course Overview.mp4 (3.42 MB)
01 Version Check.mp4 (563.39 KB)
02 Module Overview.mp4 (1.77 MB)
03 Prerequisites and Course Outline.mp4 (1.43 MB)
04 Representing Images for Machine Learning.mp4 (8.5 MB)
05 Image Preprocessing to Build Robust Models.mp4 (8.25 MB)
06 Working with Images as Arrays.mp4 (12.12 MB)
07 Representing Pixels in Images.mp4 (7.07 MB)
08 Working with Color and Color Spaces.mp4 (10.64 MB)
09 Resizing, Rescaling, Rotating, and Flipping Images.mp4 (11.13 MB)
10 Block Views and Pooling.mp4 (7.02 MB)
11 Denoising Images.mp4 (11.06 MB)
12 Normalization and ZCA Whitening.mp4 (12.14 MB)
13 Image Augmentation Using Weather Transforms.mp4 (6.23 MB)
14 Module Summary.mp4 (1.54 MB)
01 Module Overview.mp4 (1.48 MB)
02 Feature Detection and Its Importance.mp4 (5.68 MB)
03 Key Points and Descriptors.mp4 (8.34 MB)
04 Applying Keypoint Preserving Transformations.mp4 (15.91 MB)
05 Scale Invariant Feature Transform (SIFT), DAISY, and Histogram of Oriented Gradients (HOG).mp4 (6.66 MB)
06 Feature Detection and Extraction Using SIFT.mp4 (16.49 MB)
07 Feature Detection Using DAISY Descriptors.mp4 (4.99 MB)
08 Feature Detection Using Histogram of Oriented Gradients.mp4 (14.34 MB)
09 Optical Character Recognition Using Tesseract.mp4 (10.49 MB)
10 Module Summary.mp4 (1.71 MB)
1 Module Overview.mp4 (1.63 MB)
2 Dictionary Learning.mp4 (3.45 MB)
3 Sparse Representations Using Dictionary Learning.mp4 (11.06 MB)
4 Convolution Kernels.mp4 (6.67 MB)
5 Feature Detection Using Convolution Kernels.mp4 (17.26 MB)
6 Autoencoders.mp4 (7.8 MB)
7 Reading and Preprocessing Images.mp4 (10.62 MB)
8 Designing and Training an Autoencoder.mp4 (9.53 MB)
9 Summary and Further Study.mp4 (1.91 MB)]
Screenshot
![[Image: DFN4aLbX_o.jpg]](https://images2.imgbox.com/d2/78/DFN4aLbX_o.jpg)
1 Course Overview.mp4 (3.35 MB)
01 Version Check.mp4 (621.59 KB)
02 Module Overview.mp4 (1.72 MB)
03 Prerequisites and Course Outline.mp4 (1.61 MB)
04 Features and Labels.mp4 (9.15 MB)
05 The Machine Learning Workflow.mp4 (6.33 MB)
06 Components of Feature Engineering.mp4 (3.57 MB)
08 Feature Combination and Dimensionality Reduction.mp4 (5.78 MB)
09 Training, Validation, and Test Data.mp4 (7.94 MB)
10 K-fold Cross Validation.mp4 (6.9 MB)
11 Module Summary.mp4 (1.68 MB)
1 Module Overview.mp4 (2.29 MB)
2 Problems with Data.mp4 (6.6 MB)
3 Dealing with Missing Values.mp4 (6.39 MB)
4 Dealing with Outliers.mp4 (8.16 MB)
5 Applying Different Techniques to Handle Missing Values.mp4 (13.89 MB)
6 Detecting and Handling Outliers.mp4 (12.97 MB)
7 Reading and Exploring the Dataset.mp4 (16.31 MB)
8 Perform Simple and Multiple Linear Regression.mp4 (8.71 MB)
9 Module Summary.mp4 (1.46 MB)
01 Module Overview.mp4 (2.02 MB)
02 Types of Data.mp4 (6.66 MB)
03 Measuring Correlations.mp4 (6.26 MB)
05 Feature Selection Using Missing Value Ratio.mp4 (10.75 MB)
06 Calculating and Visualizing Correlations Using Pandas.mp4 (14.42 MB)
08 Feature Selection Using Filter Methods.mp4 (13.32 MB)
09 Feature Selection Using Wrapper Methods.mp4 (12.8 MB)
10 Feature Selection Using Embedded Methods.mp4 (10.19 MB)
11 Module Summary.mp4 (1.96 MB)
1 Module Overview.mp4 (2.02 MB)
3 Feature Detection and Extraction from Images.mp4 (7.71 MB)
4 Feature Extraction from Text.mp4 (8.01 MB)
5 Module Summary.mp4 (1.69 MB)
01 Module Overview.mp4 (1.69 MB)
02 Tokenization and Visualizing Frequency Distributions.mp4 (8.82 MB)
03 Performing Normalization Using Different Techniques.mp4 (11.52 MB)
04 Creating Feature Vectors from Text Data.mp4 (13.47 MB)
05 Loading and Transforming Images.mp4 (12.23 MB)
06 Extracting Features from Images.mp4 (8.03 MB)
07 Detecting Keypoints and Descriptors to Perform Image Matching.mp4 (14.53 MB)
08 Extracting Text from Images Using OCR.mp4 (14.84 MB)
09 Extracting Features from Dates.mp4 (10.44 MB)
10 Working with Geospatial Features.mp4 (17.9 MB)
11 Summary and Further Study.mp4 (2.36 MB)
1 Course Overview.mp4 (3.82 MB)
01 Version Check.mp4 (553.51 KB)
02 Module Overview.mp4 (1.24 MB)
03 Prerequisites and Course Outline.mp4 (2.42 MB)
04 Scaling and Standardization.mp4 (4.87 MB)
05 Mean, Variance, and Standard Deviation.mp4 (4.53 MB)
06 Understanding Variance.mp4 (4.76 MB)
07 Demo - Calculating Mean, Variance, and Standard Deviation.mp4 (11.95 MB)
08 Demo - Box Plot Visualization and Data Standardization.mp4 (10.07 MB)
09 Standard Scaler.mp4 (5.05 MB)
10 Demo - Standardize Data Using the Scale Function.mp4 (8.4 MB)
12 Robust Scaler.mp4 (4.6 MB)
13 Demo - Scaling Data Using the Robust Scaler.mp4 (11.28 MB)
14 Summary.mp4 (1.76 MB)
1 Module Overview.mp4 (989.65 KB)
2 What Is Normalization.mp4 (1.95 MB)
3 Normalization and Cosine Similarity.mp4 (10.13 MB)
4 Demo - Cosine Similarity and the L2 Norm.mp4 (11.43 MB)
5 Demo - Normalizing Data to Simplify Cosine Similarity Calculations.mp4 (7.48 MB)
6 Demo - K-means Clustering with Cosine Similarity.mp4 (9.26 MB)
7 L1, L2 and Max Norms.mp4 (2.67 MB)
8 Demo - Normalization Using L1, L2 and Max Norms.mp4 (8.38 MB)
9 Summary.mp4 (1.32 MB)
01 Module Overview.mp4 (1.51 MB)
02 Converting Continuous Data to Categorical.mp4 (3.2 MB)
03 Demo - Convert Numeric Data to Binary Categories Using a Binarizer.mp4 (8.13 MB)
04 Demo - Using the KBinsDiscretizer to Categorize Numeric Values.mp4 (8.65 MB)
05 Demo - Using Bin Values to Flag Outliers.mp4 (4.79 MB)
06 Scaling Data.mp4 (1.78 MB)
07 Demo - Scaling with the MaxAbsScaler.mp4 (3.25 MB)
08 Demo - Scaling with the MinMaxScaler.mp4 (4.78 MB)
09 Custom Transformations.mp4 (654.56 KB)
10 Demo - Performing Custom Transforms Using the FunctionTransformer.mp4 (4.59 MB)
11 Generating Polynomial Features.mp4 (2.61 MB)
12 Demo - Using Polynomial Features to Transform Data.mp4 (10.21 MB)
14 Demo - Working with Chi Squared Distributed Input Features.mp4 (8.25 MB)
15 Demo - Applying Power Transformers to Get Normal Distributions.mp4 (7.04 MB)
17 Demo - Tranforming to a Normal Distribution Using the QuantileTransformer.mp4 (6.31 MB)
18 Summary and Further Study.mp4 (2.52 MB)
1 Course Overview.mp4 (2.9 MB)
01 Version Check.mp4 (562.75 KB)
02 Module Overview.mp4 (1.94 MB)
03 Prerequisites and Course Outline.mp4 (2.05 MB)
04 Continuous and Categorical Data.mp4 (6.61 MB)
05 Numeric Data.mp4 (7.23 MB)
06 Categorical Data.mp4 (5.12 MB)
07 Label Encoding and One-hot Encoding.mp4 (5.22 MB)
08 Choosing between Label Encoding and One-hot Encoding.mp4 (6.81 MB)
09 Types of Classification Tasks.mp4 (7.24 MB)
10 One-hot Encoding with Known and Unknown Categories.mp4 (10.75 MB)
11 One-hot Encoding on a Pandas Data Frame Column.mp4 (5.26 MB)
12 One-hot Encoding Using pd get dummies().mp4 (2.1 MB)
13 Label Encoding to Convert Categorical Data to Ordinal.mp4 (12.67 MB)
14 Label Binarizer to Perform One vs Rest Encoding of Targets.mp4 (8.29 MB)
15 Multilabel Binarizer for Encoding Multilabel Targets.mp4 (4.76 MB)
16 Module Summary.mp4 (1.79 MB)
1 Module Overview.mp4 (2.45 MB)
2 The Dummy Trap.mp4 (7.81 MB)
3 Avoiding the Dummy Trap.mp4 (7.4 MB)
4 Dummy Coding to Overcome Limitations of One-hot Encoding.mp4 (11.15 MB)
5 Regression Analysis with Dummy or Treatment Coding.mp4 (12 MB)
6 Dummy Coding Using Patsy.mp4 (12.98 MB)
7 Perform Regression Analysis Using Machine Learning on Dummy Coded Categories.mp4 (7.74 MB)
8 Performing Linear Regression Using Machine Learning with One-hot Encoded Categories.mp4 (5.69 MB)
9 Module Summary.mp4 (1.69 MB)
01 Module Overview.mp4 (1.66 MB)
02 Dummy Coding vs Contrast Coding.mp4 (6.52 MB)
03 Exploring Contrast Coding Techniques.mp4 (7.04 MB)
04 Regression Analysis Using Simple Effect Coding.mp4 (11.87 MB)
05 Performing Linear Regression Using Machine Learning with Simple Effect Coding.mp4 (12.55 MB)
06 Regression Using Backward Difference Encoding.mp4 (13.68 MB)
07 Regression Using Helmert Encoding.mp4 (15.38 MB)
08 Generating Equally Spaced Categories to Perform Orthogonal Polynomial Encoding.mp4 (10.49 MB)
09 Performing Regression Analysis Using Orthogonal Polynomial Encoding.mp4 (5.46 MB)
10 Module Summary.mp4 (1.91 MB)
1 Module Overview.mp4 (2.37 MB)
2 Bucketing Continuous Data.mp4 (4.66 MB)
3 Bucketing Continuous Data Using Pandas.mp4 (4.89 MB)
4 Categorizing Continuous Data Using the KBinsDiscretizer.mp4 (13.13 MB)
5 Hashing.mp4 (4.93 MB)
6 Feature Hashing with Dictionaries, Tuples, and Text Data.mp4 (6.41 MB)
7 Building a Simple Regression Model Using Hashed Categorical Values.mp4 (7.41 MB)
8 Summary and Further Study.mp4 (1.99 MB)
1 Course Overview.mp4 (3.76 MB)
01 Version Check.mp4 (553.1 KB)
02 Module Overview.mp4 (1.64 MB)
03 Prerequisites and Course Outline.mp4 (8.38 MB)
04 The Curse of Dimensionality.mp4 (8.43 MB)
05 Overfitting and the Bias-variance Trade-off.mp4 (9.7 MB)
06 Techniques to Reduce Complexity.mp4 (5 MB)
07 Choosing the Right Technique.mp4 (6.75 MB)
08 Drawbacks of Reducing Complexity.mp4 (1.78 MB)
09 Demo - The Diabetes Dataset - Exploration.mp4 (11.97 MB)
10 Demo - Establishing a Baseline Model.mp4 (3.93 MB)
11 Demo - The Boston Housing Prices Dataset - Exploration.mp4 (7.89 MB)
12 Demo - Kitchen Sink Regression to Establish a Baseline Model.mp4 (10.44 MB)
13 Summary.mp4 (2.28 MB)
01 Module Overview.mp4 (1.56 MB)
02 Statistical Techniques for Feature Selection.mp4 (4.79 MB)
03 Conceptual Overview of Different Feature Selection Techniques.mp4 (6.98 MB)
04 Demo - Selecting Features Using a Variance Threshold.mp4 (12.92 MB)
05 Demo - Selecting K Best Features Using Chi2 Analysis.mp4 (7 MB)
06 Demo - Setting up Helper Functions for Feature Selection.mp4 (7.77 MB)
07 Demo - Find the Right Value for K Using Chi2 Analysis.mp4 (5.09 MB)
08 Demo - Find the Right Value for K Using ANOVA.mp4 (5.31 MB)
09 Demo - Select Features Using Percentiles and Mutual Information Analysis.mp4 (6.21 MB)
10 Demo - Dictionary Learning on Handwritten Digits.mp4 (10.69 MB)
11 Summary.mp4 (2.04 MB)
1 Module Overview.mp4 (2.76 MB)
2 Understanding Principal Components Analysis.mp4 (10.44 MB)
3 Demo - Performing PCA to Reduce Dimensionality.mp4 (6.16 MB)
4 Demo - Building Linear Models Using Principal Components.mp4 (7.13 MB)
5 Understanding Factor Analysis.mp4 (3.61 MB)
6 Demo - Applying Factor Analysis to Reduce Dimensionality.mp4 (10.27 MB)
7 Understanding Linear Discriminant Analysis.mp4 (3.35 MB)
8 Demo - Performing Linear Discriminant Analysis to Reorient Data.mp4 (10.48 MB)
9 Summary.mp4 (1.38 MB)
1 Module Overview.mp4 (3.69 MB)
2 Understanding Manifold Learning.mp4 (8.2 MB)
3 Demo - Generate Manifold and Set up Helper Functions.mp4 (10.2 MB)
4 Demo - Manifold Learning Using Multidimensional Scaling and Spectral Embedding.mp4 (7.92 MB)
5 Demo - Manifold Learning Using t-SNE and Isomap.mp4 (5.51 MB)
6 Demo - Manifold Learning Using Locally Linear Embedding.mp4 (6.8 MB)
7 Demo - Performing Kernel PCA to Reduce Complexity in Nonlinear Data.mp4 (13.68 MB)
8 Summary.mp4 (1.66 MB)
1 Module Overview.mp4 (1.96 MB)
2 K-means Model Stacking.mp4 (4.38 MB)
3 Demo - Classifying Image with Original Features.mp4 (7.21 MB)
4 Demo - Transforming Data Using K-means Cluster Centers.mp4 (6.56 MB)
5 Autoencoding.mp4 (11.51 MB)
6 Demo - Prepare Image Data to Feed an Autoencoder.mp4 (8.39 MB)
8 Summary and Further Study.mp4 (2.84 MB)
1 Course Overview.mp4 (3.44 MB)
01 Version Check.mp4 (546.32 KB)
02 Module Overview.mp4 (2.07 MB)
03 Prerequisites and Course Outline.mp4 (1.79 MB)
04 One-hot Encoding.mp4 (6.57 MB)
05 Count Vectors.mp4 (4.37 MB)
06 Tf-Idf Vectors.mp4 (3.86 MB)
07 Co-occurence Vectors.mp4 (7.33 MB)
08 Word Embeddings.mp4 (7.61 MB)
09 Installing Packages and Setting Up the Environment.mp4 (6.18 MB)
10 Sentence and Word Tokenization.mp4 (13.89 MB)
11 Plotting Word Frequency Distributions.mp4 (11.73 MB)
12 Module Summary.mp4 (1.88 MB)
1 Module Overview.mp4 (1.98 MB)
2 Bag-of-words and Bag-of-n-grams.mp4 (4.16 MB)
3 Bag-of-words Using the Count Vectorizer.mp4 (14.09 MB)
4 Inverse Transform Using the Count Vectorizer.mp4 (7.3 MB)
5 Bag-of-n-grams Using the Count Vectorizer.mp4 (15.06 MB)
6 Generating N-grams Using NLTK.mp4 (7.27 MB)
7 Bag-of-words Using the Tf-Idf Vectorizer.mp4 (9.54 MB)
8 Module Summary.mp4 (1.84 MB)
1 Module Overview.mp4 (1.72 MB)
2 Natural Language Processing Operations.mp4 (8.1 MB)
3 Stopword Removal Using NLTK and scikit-learn.mp4 (14.71 MB)
4 Frequency Filtering Using scikit-learn.mp4 (6.89 MB)
5 Stemming.mp4 (11.45 MB)
6 Lemmatization.mp4 (8.04 MB)
7 Parts-of-speech Tagging.mp4 (13.64 MB)
8 Module Summary.mp4 (1.74 MB)
1 Module Overview.mp4 (1.62 MB)
2 Feature Hashing.mp4 (3.6 MB)
3 Reducing Dimensions Using the Feature Hasher.mp4 (7.65 MB)
4 Reducing Dimensions at Scale Using the Hashing Vectorizer.mp4 (13.23 MB)
5 Locality-sensitive Hashing.mp4 (7.84 MB)
6 Similar Documents Using Jaccard Index and Locality-sensitive Hashing.mp4 (16.01 MB)
7 Module Summary.mp4 (1.85 MB)
01 Module Overview.mp4 (1.58 MB)
02 Naive Bayes for Classification.mp4 (3.68 MB)
03 Classification Using the Hashing Vectorizer.mp4 (16.61 MB)
05 Building Features Using the Count Vectorizer.mp4 (6.01 MB)
08 Building Features Using the Tf-Idf Vectorizer.mp4 (4.64 MB)
09 Building Features Using Bag-of-n-grams Model.mp4 (5.59 MB)
10 Summary and Further Study.mp4 (2.15 MB)
1 Course Overview.mp4 (3.42 MB)
01 Version Check.mp4 (563.39 KB)
02 Module Overview.mp4 (1.77 MB)
03 Prerequisites and Course Outline.mp4 (1.43 MB)
04 Representing Images for Machine Learning.mp4 (8.5 MB)
05 Image Preprocessing to Build Robust Models.mp4 (8.25 MB)
06 Working with Images as Arrays.mp4 (12.12 MB)
07 Representing Pixels in Images.mp4 (7.07 MB)
08 Working with Color and Color Spaces.mp4 (10.64 MB)
09 Resizing, Rescaling, Rotating, and Flipping Images.mp4 (11.13 MB)
10 Block Views and Pooling.mp4 (7.02 MB)
11 Denoising Images.mp4 (11.06 MB)
12 Normalization and ZCA Whitening.mp4 (12.14 MB)
13 Image Augmentation Using Weather Transforms.mp4 (6.23 MB)
14 Module Summary.mp4 (1.54 MB)
01 Module Overview.mp4 (1.48 MB)
02 Feature Detection and Its Importance.mp4 (5.68 MB)
03 Key Points and Descriptors.mp4 (8.34 MB)
04 Applying Keypoint Preserving Transformations.mp4 (15.91 MB)
05 Scale Invariant Feature Transform (SIFT), DAISY, and Histogram of Oriented Gradients (HOG).mp4 (6.66 MB)
06 Feature Detection and Extraction Using SIFT.mp4 (16.49 MB)
07 Feature Detection Using DAISY Descriptors.mp4 (4.99 MB)
08 Feature Detection Using Histogram of Oriented Gradients.mp4 (14.34 MB)
09 Optical Character Recognition Using Tesseract.mp4 (10.49 MB)
10 Module Summary.mp4 (1.71 MB)
1 Module Overview.mp4 (1.63 MB)
2 Dictionary Learning.mp4 (3.45 MB)
3 Sparse Representations Using Dictionary Learning.mp4 (11.06 MB)
4 Convolution Kernels.mp4 (6.67 MB)
5 Feature Detection Using Convolution Kernels.mp4 (17.26 MB)
6 Autoencoders.mp4 (7.8 MB)
7 Reading and Preprocessing Images.mp4 (10.62 MB)
8 Designing and Training an Autoencoder.mp4 (9.53 MB)
9 Summary and Further Study.mp4 (1.91 MB)]
Screenshot
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