Machine Learning

Module 1: Introduction to Machine Learning

• Overview of Machine Learning

  • What is Machine Learning?
  • History and Evolution of Machine Learning
  • AI vs. Machine Learning vs. Deep Learning
  • Importance and Applications of Machine Learning in Industry
  • Categories of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

• Case Studies and Applications

  • Machine Learning in Healthcare, Finance, E-commerce, and Autonomous Systems

Module 2: Mathematical Foundations

• Linear Algebra for Machine Learning

  • Vectors, Matrices, and Tensors
  • Eigenvalues, Eigenvectors, and Matrix Decomposition

• Probability and Statistics

  • Probability Theory, Random Variables, and Distributions
  • Bayes Theorem, Maximum Likelihood Estimation (MLE)
  • Hypothesis Testing, Confidence Intervals

• Optimization Techniques

  • Gradient Descent and Variants
  • Convex vs. Non-Convex Optimization
  • Stochastic Gradient Descent (SGD)

Module 3: Supervised Learning

• Linear Models for Regression and Classification

  • Linear Regression, Polynomial Regression
  • Logistic Regression
  • Performance Metrics: Mean Squared Error (MSE), R-squared, Accuracy, Precision, Recall

• Decision Trees and Ensemble Methods

  • Decision Trees, Random Forests, and Gradient Boosting
  • Bagging vs. Boosting
  • Hyperparameter Tuning: Grid Search, Random Search

• Support Vector Machines (SVM)

  • SVM for Classification and Regression
  • Kernel Functions and the Kernel Trick

Module 4: Unsupervised Learning

• Clustering Algorithms

  • K-Means, Hierarchical Clustering, DBSCAN
  • Evaluation Metrics: Silhouette Score, Elbow Method

• Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)
  • t-SNE, UMAP

• Association Rule Learning

  • Apriori Algorithm
  • Market Basket Analysis

Module 5: Neural Networks and Deep Learning

• Introduction to Neural Networks

  • Perceptrons and Multilayer Perceptrons (MLP)
  • Backpropagation and Gradient Descent in Neural Networks

• Deep Learning Fundamentals

  • Feedforward Neural Networks
  • Activation Functions: Sigmoid, ReLU, Softmax
  • Dropout, Batch Normalization, and Regularization Techniques

• Convolutional Neural Networks (CNNs)

  • CNN Architecture and Layers
  • Applications in Image Recognition

• Recurrent Neural Networks (RNNs) and LSTMs

  • Sequence Models and Time Series Forecasting
  • Long Short-Term Memory (LSTM) Networks
  • Applications in Natural Language Processing (NLP)

Module 6: Reinforcement Learning

• Introduction to Reinforcement Learning

  • Markov Decision Processes (MDP)
  • Q-Learning and Deep Q-Networks (DQN)
  • Policy Gradients

• Applications of Reinforcement Learning

  • Autonomous Systems, Game AI, Robotics

Module 7: Machine Learning Tools and Libraries

• Programming with Python

  • Overview of Python for Machine Learning: Numpy, Pandas, Matplotlib
  • Machine Learning Libraries: Scikit-learn, TensorFlow, Keras, PyTorch

• Model Development and Deployment

  • Model Selection, Training, and Evaluation
  • Saving and Loading Machine Learning Models
  • Deployment of Machine Learning Models: Flask, Docker, Cloud Platforms (AWS, Google Cloud, Azure)

Module 8: Model Evaluation and Validation

• Model Evaluation Metrics

  • Confusion Matrix, ROC Curve, AUC, F1 Score
  • Cross-Validation and Overfitting Prevention
  • Bias-Variance Tradeoff

• Regularization Techniques

  • L1 (Lasso) and L2 (Ridge) Regularization
  • Early Stopping, Dropout for Neural Networks

Module 9: Machine Learning in Practice

• Feature Engineering

  • Feature Selection and Feature Extraction
  • Handling Missing Data and Outliers
  • One-Hot Encoding, Normalization, and Scaling

• Handling Large Datasets

  • Mini-Batch Gradient Descent
  • Parallel and Distributed Machine Learning
  • Handling Imbalanced Datasets

Module 10: Advanced Topics in Machine Learning

• Natural Language Processing (NLP)

  • Tokenization, Lemmatization, Word Embeddings (Word2Vec, GloVe)
  • Sentiment Analysis, Text Classification

• Generative Models

  • Generative Adversarial Networks (GANs)
  • Variational Autoencoders (VAEs)

• Transfer Learning

  • Pretrained Models and Fine-Tuning
  • Applications in Computer Vision and NLP

Module 11: Machine Learning Ethics and Interpretability

• Ethical Considerations in Machine Learning

  • Bias and Fairness in Algorithms
  • Privacy Concerns and Data Protection (GDPR, CCPA)

• Explainability in Machine Learning

  • Interpretable Models vs. Black-Box Models
  • SHAP, LIME for Model Interpretability

Module 12: Capstone Project

• End-to-End Machine Learning Project
  • Problem Definition and Dataset Selection
  • Data Preprocessing and Feature Engineering
  • Model Selection, Training, and Evaluation
  • Model Deployment and Presentation

Assessment:

• Quizzes: Periodic assessments of conceptual understanding
• Assignments: Hands-on coding tasks related to algorithm implementation
• Labs: Practical exercises involving popular libraries like Scikit-learn, TensorFlow, PyTorch
• Final Capstone Project: Solving a real-world problem with a complete ML pipeline from data preprocessing to model deployment