Machine Learning

Chapter 1: Introduction to Machine Learning

 

Chapter 2: Supervised Learning

  • 2.1) Overview of Supervised Learning

  • 2.2) Linear Regression

    • Simple Linear Regression

    • Multiple Linear Regression

    • Cost Function and Gradient Descent

  • 2.3) Logistic Regression

    • Sigmoid Function

    • Decision Boundary

  • 2.4) Support Vector Machines (SVM)

    • Margin, Hyperplane

    • Kernel Trick

  • 2.5) Decision Trees

    • ID3, C4.5

    • Gini Index, Information Gain

  • 2.6) K-Nearest Neighbors (KNN)

  • 2.7) Evaluation Metrics

    • Confusion Matrix

    • Accuracy, Precision, Recall, F1-Score

    • ROC, AUC

 

Chapter 3: Unsupervised Learning

  • 3.1) Clustering

    • K-Means Clustering

    • Hierarchical Clustering

    • DBSCAN

  • 3.2) Dimensionality Reduction

    • PCA (Principal Component Analysis)

    • LDA (Linear Discriminant Analysis)

  • 3.3) Association Rule Mining

    • Apriori Algorithm

    • FP-Growth Algorithm

    • Support, Confidence, Lift

 

Chapter 4: Ensemble Learning

  • 4.1) Bagging

    • Bootstrap Aggregating

    • Random Forest

  • 4.2) Boosting

    • AdaBoost

    • Gradient Boosting

    • XGBoost

  • 4.3) Stacking

 

Chapter 5: Neural Networks and Deep Learning

  • 5.1) Perceptron and Multilayer Perceptron (MLP)

  • 5.2) Activation Functions

    • Sigmoid, Tanh, ReLU

  • 5.3) Backpropagation Algorithm

  • 5.4) Introduction to Deep Learning

  • 5.5) Convolutional Neural Networks (CNNs) – Basics

  • 5.6) Recurrent Neural Networks (RNNs) – Basics

 

Chapter 6: Reinforcement Learning

  • 6.1) Introduction to RL

  • 6.2) Markov Decision Processes (MDP)

  • 6.3) Q-Learning

  • 6.4) Exploration vs Exploitation

  • 6.5) Applications of RL

 

Chapter 7: Model Selection and Evaluation

  • 7.1) Bias-Variance Tradeoff

  • 7.2) Cross-Validation Techniques

  • 7.3) Hyperparameter Tuning

  • 7.4) Grid Search vs Random Search

  • 7.5) Overfitting and Underfitting

 

Chapter 8: Recent Trends and Applications

  • 8.1) Transfer Learning

  • 8.2) AutoML

  • 8.3) Federated Learning

  • 8.4) ML in Industry Applications

    • Healthcare

    • Finance

    • Robotics

    • Natural Language Processing (NLP)

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