Phase 3 • Machine Learning Foundations

Phase 3 introduces machine learning from first principles. Learners build supervised and unsupervised models, work with neural networks, and deploy ML systems into production with MLOps best practices.

What you will practice

• Training regression and classification models for business predictions.
• Evaluating model performance with appropriate metrics.
• Engineering features and selecting important variables.
• Building neural networks for complex pattern recognition.
• Processing text with natural language processing techniques.
• Deploying models to production with MLOps workflows.
• Tuning hyperparameters and preventing overfitting.

Lesson sprint

• :material-tag: Day 40 – Introduction to Machine Learning & Core Concepts: Frame ML problems and evaluation strategies.
• :material-tag: Day 41 – Supervised Learning – Regression: Predict continuous outcomes with linear models.
• :material-tag: Day 42 – Supervised Learning – Classification (Part 1): Build binary classifiers with logistic regression and trees.
• :material-tag: Day 43 – Supervised Learning – Classification (Part 2): Master ensemble methods and ROC analysis.
• :material-tag: Day 44 – Unsupervised Learning: Discover patterns with clustering and dimensionality reduction.
• :material-tag: Day 45 – Feature Engineering & Model Evaluation: Create features and validate models rigorously.
• :material-tag: Day 46 – Introduction to Neural Networks & Frameworks: Build deep learning models with TensorFlow/Keras.
• :material-tag: Day 47 – Convolutional Neural Networks (CNNs) for Computer Vision: Process images with convolutional architectures.
• :material-tag: Day 48 – Recurrent Neural Networks (RNNs) for Sequence Data: Model sequential data with LSTMs and GRUs.
• :material-tag: Day 49 – Natural Language Processing (NLP): Process and analyze text data.
• :material-tag: Day 50 – MLOps - Model Deployment: Deploy models to production environments.
• :material-tag: Day 51 – Regularised Models: Prevent overfitting with regularization techniques.
• :material-tag: Day 52 – Ensemble Methods: Combine models for better predictions.
• :material-tag: Day 53 – Model Tuning and Feature Selection: Optimize hyperparameters systematically.
• :material-tag: Day 54 – Probabilistic Modeling: Reason about uncertainty with probabilistic approaches.

Learning outcomes

By completing Phase 3, you will be able to:

• Select and train appropriate ML models for business problems.
• Evaluate model performance with cross-validation and holdout sets.
• Engineer features that improve predictive accuracy.
• Build and train neural networks for complex tasks.
• Deploy models to production with proper monitoring.
• Apply MLOps best practices for reliable ML systems.

Ready to continue? Advance to Phase 4 – Advanced ML & MLOps for cutting-edge techniques.