Machine Learning · Progress
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Whole subject0 of 50 topics done
Unit 0 · Introduction to Machine Learning0/4
- 0.1Introduction to Machine Learning: Definition, AI vs ML vs DL
- 0.2Types of Machine Learning
- 0.3Data Basics: Features, Variable Types, Data Splits & Pre-processing
- 0.4Parametric vs Non-parametric Models
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Unit 1 · Supervised Learning0/13
- 1.1Supervised Learning: Classification vs Regression
- 1.2Distance-Based Methods & Distance Measures
- 1.3K-Nearest Neighbours (KNN)
- 1.4Decision Trees: ID3, Entropy & Information Gain
- 1.5Probability Basics & Bayes Theorem
- 1.6Bayesian Learning & Inference
- 1.7Naive Bayes Classifier
- 1.8Linear Regression
- 1.9Logistic Regression
- 1.10Generalized Linear Models
- 1.11Support Vector Machines: Hyperplane, Margin & Support Vectors
- 1.12Non-linearity & Kernel Methods (Non-linear SVM)
- 1.13Beyond Binary Classification: Multi-class, Structured Outputs & Ranking
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Unit 2 · Unsupervised Learning0/11
- 2.1Clustering: Concepts, Requirements & Types
- 2.2K-means Clustering
- 2.3Kernel K-means
- 2.4Dimensionality Reduction & the Curse of Dimensionality
- 2.5Feature Selection
- 2.6Principal Component Analysis (PCA)
- 2.7Kernel PCA
- 2.8Recommender Systems: Content-Based & Collaborative Filtering
- 2.9Matrix Factorization & Matrix Completion
- 2.10Generative Models: Mixture Models (GMM & EM)
- 2.11Latent Factor Models
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Unit 3 · Model Evaluation and Selection0/9
- 3.1Confusion Matrix & Classification Metrics
- 3.2ROC, AUC & Other Evaluation Metrics
- 3.3Overfitting, Underfitting & the Bias–Variance Trade-off
- 3.4Model Selection & Cross-Validation
- 3.5Introduction to Statistical Learning Theory (PAC, VC Dimension)
- 3.6Ensemble Learning: Principle & Types
- 3.7Bagging & Bootstrapping
- 3.8Boosting & Bagging vs Boosting
- 3.9Random Forest
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Unit 4 · Sparse Modeling, Sequences and Deep Learning0/6
- 4.1Sparse Modeling & Estimation
- 4.2Modeling Sequence / Time-Series Data
- 4.3Neural Network Fundamentals: Perceptron, Activation Functions & Backpropagation
- 4.4Deep Learning: Concepts, ML vs DL & Applications
- 4.5Deep Learning Architectures: CNN, RNN, Autoencoders, DBN
- 4.6Feature Representation Learning
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Unit 5 · Scalable and Advanced ML0/5
- 5.1Scalable ML: Online & Distributed Learning
- 5.2Semi-supervised Learning
- 5.3Active Learning
- 5.4Reinforcement Learning
- 5.5Inference in Graphical Models: Bayesian Networks
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Unit 6 · Recent Trends0/2
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