Training on Practical Machine Learning with Python

Module 1: End-to-End ML Pipeline in Python

• Understanding the ML pipeline workflow
• Data collection and preparation in Python
• Feature engineering and selection
• Model building and evaluation
• Model deployment and monitoring
• Case Study: Implementing an end-to-end ML pipeline

Module 2: Regression and Classification with Python

• Linear and logistic regression implementation
• Decision trees and random forests
• Gradient boosting with XGBoost and LightGBM
• Model evaluation and performance metrics
• Hyperparameter tuning and cross-validation
• Case Study: Building and tuning a classification model

Module 3: Unsupervised Learning and Dimensionality Reduction

• Clustering with K-means, DBSCAN, and hierarchical
• Dimensionality reduction with PCA, t-SNE, and UMAP
• Anomaly detection and outlier analysis
• Association rule learning and market basket analysis
• Applications of unsupervised learning
• Case Study: Applying clustering to a real-world dataset

Module 4: Advanced ML Techniques and Model Interpretability

• Ensemble methods and model stacking
• Handling imbalanced datasets
• Feature importance and model interpretation
• SHAP and LIME for model explainability
• Managing model drift and performance degradation
• Case Study: Building and interpreting an advanced ML model

Module 5: ML System Design and Deployment

• ML system architecture and design
• Model serving and API development
• Containerization and orchestration
• ML pipeline automation and MLOps
• Monitoring and maintaining ML systems in production
• Case Study: Deploying an ML model as an API service

Module 6: Time Series Analysis and Forecasting

• Understanding time series data and its characteristics
• Time series decomposition and pattern analysis
• ARIMA, SARIMA, and Prophet models
• Machine learning for time series forecasting
• Evaluating and improving forecast accuracy
• Case Study: Building a time series forecasting model

Module 7: Natural Language Processing (NLP) with Python

• Text preprocessing and tokenization
• Text representation: Bag-of-Words, TF-IDF, Word2Vec
• Building text classification and sentiment analysis models
• Topic modeling with LDA and NMF
• Modern NLP with transformers and BERT
• Case Study: Building an NLP model for sentiment analysis

Module 8: Computer Vision with Python

• Image processing and feature extraction
• Building image classifiers with deep learning
• Object detection and segmentation
• Transfer learning and fine-tuning vision models
• Applications of computer vision in industry
• Case Study: Building a computer vision model

Module 9: Deploying ML Models to Production

• Understanding the deployment landscape
• Building ML APIs with Flask and FastAPI
• Containerizing ML models with Docker
• Managing ML models in the cloud
• Monitoring and maintaining deployed models
• Case Study: Deploying a machine learning model to the cloud

Module 10: Advanced Topics and Emerging Trends

• AutoML and hyperparameter optimization
• Feature stores and feature engineering pipelines
• Responsible and ethical ML practices
• Edge AI and model optimization
• Future trends in practical machine learning
• Case Study: Implementing AutoML for model optimization