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Introduction
Notations
Mathematical Notations
Machine Learning Notations
Deep Learning Notations
Optimization
Gradient Descent
Gradient Descent Concept
Gradient Descent Construction
Application: Gradient Descent
Machine Learning
The Machine Learning Framework
Fundamentals
Loss
Concept
Cross Entropy Loss
Focal Loss
Bias and Variance Tradeoff
Concept
Decision Boundary
Concept
Linear Models
Logistic Regression
Concept
Implementation
Application: Sentiment Analysis with Logistic Regression
Generalized Linear Models
Model Selection and Evaluation
Metrics and Scoring Rules
Classification Metrics
Accuracy
Precision, Recall and F1 Score
Brier Score
Regression Metrics
Mean Absolute Error
(Root) Mean Squared Error
Mean Absolute Percentage Error
Learning Curve Theory
Concept
Implementation
Application: Overfitting and Underfitting
Plotting Learning Curves and Checking Models’ Scalability
Trees, Forests, Bagging and Boosting
Decision Trees
Braindump
Ensemble Learning
Bagging
Random Forests
Boosting
Dimensionality Reduction
Principal Component Analysis
Concept
PCA
Eigenface
Neighbourhood
K-Nearest Neighbours
Concept
K-Nearest Neighbours Decision Boundary
Curse of Dimensionality
The Importance of Feature Scaling in KNN
Deep Learning
Numerical Stability and Initialization
Computer Vision
Modern Convolutional Neural Networks
Residual Networks
Concept
Natural Language Processing Specialization
Natural Language Processing with Classification and Vector Spaces
Sentiment Analysis with Logistic Regression
Preprocessing
Building and Visualizing word frequencies
Visualizing tweets and the Logistic Regression model
Natural Language Processing (NLP)
Makemore
Vector Semantics and Embeddings
Words and Vectors
Concept
Cosine Similarity and Notion of Closeness
Concept
Implementation
Application: Word Similarity
Term Frequency-Inverse Document Frequency (TF-IDF)
Concept
Implementation
Movie Recommender System
References, Resources and Roadmap
Bibliography
Resources
Repository
Open issue
Index
