Machine Learning
00- ML Course Outline
📘 Machine Learning Course Outline
Step-by-step journey from fundamentals to advanced ML applications
Module 1: Foundations
- Introduction to Machine Learning
- Python for Data Science
- Math Essentials (Linear Algebra, Probability, Statistics)
Module 2: Core ML Algorithms
- Supervised Learning (Regression, Classification)
- Unsupervised Learning (Clustering, Dimensionality Reduction)
- Model Evaluation and Validation
Module 3: Advanced Machine Learning
- Ensemble Methods (Bagging, Boosting, Random Forests)
- Support Vector Machines
- Feature Engineering & Feature Selection
Module 4: Neural Networks & Deep Learning
- Introduction to Neural Networks
- Deep Learning with TensorFlow / PyTorch
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs, LSTMs, GRUs)
Module 5: Practical ML Applications
- Natural Language Processing (NLP)
- Computer Vision
- Recommender Systems
- Time Series Forecasting
Module 6: Deployment & MLOps
- Model Deployment with Flask / FastAPI / Streamlit
- Cloud ML Platforms (AWS, GCP, Azure)
- Monitoring & Retraining Models
Module 7: Capstone Project
- End-to-End ML Pipeline
- Choose a Domain (Finance, Healthcare, NLP, CV)
- Final Presentation & Documentation