مباحث Feature Engineering for Machine Learning:

• 1. The Machine Learning Pipeline Data Tasks Models Features Model Evaluation
• 2. Fancy Tricks with Simple Numbers Scalars, Vectors, and Spaces Dealing with Counts Binarization Quantization or Binning Log Transformation Log Transform in Action Power Transforms: Generalization of the Log Transform Feature Scaling or Normalization Min-Max Scaling Standardization (Variance Scaling) ℓ2 Normalization Interaction Features Feature Selection Summary Bibliography
• 3. Text Data: Flattening, Filtering, and Chunking Bag-of-X: Turning Natural Text into Flat Vectors Bag-of-Words Bag-of-n-Grams Filtering for Cleaner Features Stopwords Frequency-Based Filtering Stemming Atoms of Meaning: From Words to n-Grams to Phrases Parsing and Tokenization Collocation Extraction for Phrase Detection Summary Bibliography
• 4. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf Tf-Idf : A Simple Twist on Bag-of-Words Putting It to the Test Creating a Classification Dataset Scaling Bag-of-Words with Tf-Idf Transformation Classification with Logistic Regression Tuning Logistic Regression with Regularization Deep Dive: What Is Happening? Summary Bibliography
• 5. Categorical Variables: Counting Eggs in the Age of Robotic Chickens Encoding Categorical Variables One-Hot Encoding Dummy Coding Effect Coding Pros and Cons of Categorical Variable Encodings Dealing with Large Categorical Variables Feature Hashing Bin Counting Summary Bibliography
• 6. Dimensionality Reduction: Squashing the Data Pancake with PCA Intuition Derivation Linear Projection Variance and Empirical Variance Principal Components: First Formulation Principal Components: Matrix-Vector Formulation General Solution of the Principal Components Transforming Features Implementing PCA PCA in Action Whitening and ZCA Considerations and Limitations of PCA Use Cases Summary Bibliography
• 7. Nonlinear Featurization via K-Means Model Stacking k-Means Clustering Clustering as Surface Tiling k-Means Featurization for Classification Alternative Dense Featurization Pros, Cons, and Gotchas Summary Bibliography
• 8. Automating the Featurizer: Image Feature Extraction and Deep Learning The Simplest Image Features (and Why They Don’t Work) Manual Feature Extraction: SIFT and HOG Image Gradients Gradient Orientation Histograms SIFT Architecture Learning Image Features with Deep Neural Networks Fully Connected Layers Convolutional Layers Rectified Linear Unit (ReLU) Transformation Response Normalization Layers Pooling Layers Structure of AlexNet Summary Bibliography
• 9. Back to the Feature: Building an Academic Paper Recommender Item-Based Collaborative Filtering First Pass: Data Import, Cleaning, and Feature Parsing Academic Paper Recommender: Naive Approach Second Pass: More Engineering and a Smarter Model Academic Paper Recommender: Take 2 Third Pass: More Features = More Information Academic Paper Recommender: Take 3 Summary Bibliography
• A. Linear Modeling and Linear Algebra Basics Overview of Linear Classification The Anatomy of a Matrix From Vectors to Subspaces Singular Value Decomposition (SVD) The Four Fundamental Subspaces of the Data Matrix Solving a Linear System Bibliography