ML: Syllabus

Mastering Machine Learning: From Fundamentals to Advanced Applications with Python

Machine learning is revolutionizing industries by enabling data-driven decision-making and automation. This book takes a practical approach to machine learning, covering data preparation, model training, evaluation, and deployment with Python. By the end of this book, readers will have a deep understanding of ML algorithms, optimization techniques, and real-world applications, with 90% hands-on implementation.

Module 1: Introduction to Machine Learning#

• What is machine learning? Key concepts and history
• Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning
• Overview of the ML pipeline: Data Collection → Preprocessing → Model Training → Evaluation → Deployment
• Setting up a Python environment for ML (NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch)

Module 2: Data Preprocessing and Feature Engineering#

• Handling missing data and outliers
• Feature scaling and normalization
• Encoding categorical variables
• Feature selection and dimensionality reduction (PCA, LDA, t-SNE)

Module 3: Supervised Learning - Regression Models#

• Simple and Multiple Linear Regression
• Polynomial Regression
• Ridge and Lasso Regression for regularization
• Model evaluation metrics (MSE, RMSE, R², Adjusted R²)

Module 4: Supervised Learning - Classification Models#

• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Model evaluation (Confusion Matrix, Precision-Recall, ROC-AUC)

Module 5: Unsupervised Learning#

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA) for dimensionality reduction
• Anomaly Detection with One-Class SVM

Module 6: Neural Networks and Deep Learning#

• Introduction to Neural Networks
• Building Deep Neural Networks with TensorFlow & PyTorch
• Optimizers: SGD, Adam, RMSprop
• Activation Functions: ReLU, Sigmoid, Softmax

Module 7: Convolutional Neural Networks (CNNs) for Image Processing#

• Understanding convolution, pooling, and feature extraction
• Building a CNN model with TensorFlow & PyTorch
• Transfer learning using pre-trained models (VGG16, ResNet, EfficientNet)

Module 8: Natural Language Processing (NLP) and Transformers#

• Tokenization, Stopword Removal, and Stemming/Lemmatization
• Word Embeddings: Word2Vec, GloVe, FastText
• Sentiment Analysis using LSTMs and GRUs
• Transformer-based models (BERT, GPT, T5) for text generation and question-answering

Module 9: Reinforcement Learning#

• Basics of Reinforcement Learning (RL)
• Q-learning and Deep Q Networks (DQN)
• Policy Gradient Methods
• Real-world applications of RL in gaming and robotics

Module 10: Model Optimization and Hyperparameter Tuning#

• Grid Search vs. Random Search
• Bayesian Optimization
• Hyperparameter tuning with Optuna and Scikit-Optimize
• Avoiding overfitting with dropout, regularization, and batch normalization

Module 11: Model Deployment and MLOps#

• Deploying ML models with Flask and FastAPI
• Using Docker and Kubernetes for scalable deployments
• CI/CD pipelines for ML model updates
• Monitoring model drift and retraining pipelines

Module 12: Time Series Forecasting#

• Handling time series data in Pandas
• Moving averages and exponential smoothing
• ARIMA, SARIMA, and LSTMs for forecasting
• Real-world applications: Stock Market, Weather Prediction, Demand Forecasting

Module 13: Ethical AI and Responsible Machine Learning#

• Bias and Fairness in AI models
• Explainable AI (XAI) techniques
• Model interpretability with SHAP and LIME
• Legal and ethical considerations in AI deployments

Hands-On Projects

Project 1: Customer Churn Prediction#

• Data preprocessing and feature engineering
• Training and evaluating a classification model
• Deploying the model as an API with Flask/FastAPI

Project 2: Image Classification with CNNs#

• Building a CNN for classifying images
• Using data augmentation techniques
• Fine-tuning a pre-trained deep learning model

Project 3: Sentiment Analysis with NLP#

• Preprocessing text data for sentiment classification
• Training LSTMs and Transformer-based models
• Deploying the NLP model as a REST API

Project 4: Stock Market Prediction with Time Series Analysis#

• Using ARIMA, LSTMs, and XGBoost for financial forecasting
• Comparing different models based on RMSE and MAE
• Visualizing results with Matplotlib and Seaborn

Project 5: Real-Time ML Pipeline with MLOps#

• Implementing an automated model training pipeline
• Deploying models using Docker & Kubernetes
• Monitoring model performance in real-time

References#

• Scikit-Learn Documentation
• TensorFlow Documentation
• PyTorch Documentation
• MLOps Best Practices