Course Outline
- Week 1: Foundations of Machine Learning and Python
- What is Machine Learning?
- Python Essentials for ML
- NumPy and Pandas for Data Manipulation
- Data Preprocessing and Feature Engineering
- Machine Learning Workflow Overview
- Week 2: Supervised and Unsupervised Learning
- Supervised Learning
- Unsupervised Learning
- Linear and Logistic Regression
- Decision Trees and Random Forests
- Clustering Techniques: K-Means and Hierarchical
- Dimensionality Reduction: PCA and t-SNE
- Model Evaluation and Validation Techniques
- Week 3: Deep Learning Fundamentals
- Introduction to Neural Networks
- Backpropagation and Optimization Algorithms
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTMs
- Transfer Learning and Fine Tuning
- Week 4: Natural Language Processing
- Text Preprocessing and Tokenization
- Word Embeddings: Word2Vec and GloVe
- Sentiment Analysis and Text Classification
- Named Entity Recognition (NER)
- Introduction to Transformers
- Week 5: Advanced Machine Learning Techniques
- Ensemble Methods: Bagging and Boosting
- XGBoost and LightGBM
- Time Series Analysis and Forecasting
- Building Recommender Systems
- Anomaly Detection Techniques
- Week 6: Generative Models
- Introduction to Generative Models
- Autoencoders and Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs)
- Neural Style Transfer
- Generative Models for NLP
- Week 7: Reinforcement Learning
- Introduction to Reinforcement Learning
- Markov Decision Processes
- Q-Learning and Deep Q-Networks
- Policy Gradient Methods
- RL Applications and Case Studies
- Week 8: MLOps and Deployment
- Building ML Pipelines
- Model Deployment with Flask and Docker
- Introduction to MLOps
- Model Monitoring and Maintenance
- Capstone Project: End-to-End ML Solution
Autoencoders and Variational Autoencoders (VAEs)
Week 6: Generative Models