Machine Learning Topics and Subtopics

A structured overview of fundamental and advanced concepts in Machine Learning (ML), including supervised and unsupervised learning, reinforcement learning, model evaluation, and more.

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Introduction & Basic Concepts

• What is Machine Learning?
• Difference between AI, ML, and DL
• Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning
• Common Terminology
  • Features, Labels
  • Training vs Testing Data
  • Overfitting vs Underfitting
  • Bias-Variance Tradeoff

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Supervised Learning

Regression

• Linear Regression
• Polynomial Regression
• Ridge and Lasso Regression

Classification

• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Support Vector Machines (SVM)
• Decision Trees
• Random Forests
• Naive Bayes
• Gradient Boosting (XGBoost, LightGBM, CatBoost)

Evaluation Metrics

• Accuracy, Precision, Recall, F1-Score
• Confusion Matrix
• ROC Curve & AUC

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Unsupervised Learning

Clustering

• k-Means
• DBSCAN
• Hierarchical Clustering

Dimensionality Reduction

• PCA (Principal Component Analysis)
• t-SNE
• LDA (Linear Discriminant Analysis)

Association Rule Learning

• Apriori Algorithm
• Eclat

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Reinforcement Learning

Key Concepts

• Agent, Environment, Reward, Policy
• Exploration vs Exploitation

Algorithms

• Q-Learning
• Deep Q-Networks (DQN)
• Policy Gradient Methods
• Actor-Critic Methods

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Model Evaluation & Selection

• Cross-Validation
• Grid Search & Random Search
• Hyperparameter Tuning
• Learning Curves
• Model Deployment Concepts

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Neural Networks & Deep Learning (Basic ML Connection)

• Perceptron
• Multilayer Perceptron (MLP)
• Backpropagation
• Activation Functions (ReLU, Sigmoid, Tanh)
• Loss Functions (MSE, Cross-Entropy)

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Feature Engineering & Preprocessing

• Data Cleaning
• Normalization / Standardization
• Encoding Categorical Data
• Handling Missing Data
• Feature Selection and Extraction

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Tools & Frameworks

Languages

• Python
• R
• Julia

Libraries

• Scikit-learn
• Pandas, NumPy
• TensorFlow, PyTorch (for Deep Learning)
• XGBoost, LightGBM

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Practical Applications

• Computer Vision
• Natural Language Processing
• Recommendation Systems
• Anomaly Detection
• Time Series Forecasting

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