Machine Learning
Build, train, and evaluate predictive models from first principles to deployment.
Week 1
Foundations of Machine Learning
Understand what ML is and set up your working environment.
- Supervised vs. unsupervised vs. reinforcement learning
- The ML workflow: data, model, evaluation, iteration
- Python, NumPy, and pandas for ML
- Setting up notebooks and reproducible experiments
Week 2
Data Preparation & Feature Engineering
Turn raw data into model-ready features.
- Cleaning, handling missing values, and outliers
- Encoding categorical variables and scaling
- Feature selection and dimensionality reduction
- Train/validation/test splitting strategies
Week 3
Supervised Learning
Master core regression and classification algorithms.
- Linear and logistic regression
- Decision trees and random forests
- Support vector machines and k-NN
- Bias–variance tradeoff and regularization
Week 4
Model Evaluation & Tuning
Measure and improve model performance rigorously.
- Accuracy, precision, recall, F1, ROC–AUC
- Cross-validation techniques
- Hyperparameter tuning with grid and random search
- Diagnosing underfitting and overfitting
Week 5
Unsupervised Learning & Neural Networks
Explore clustering and an intro to deep learning.
- K-means and hierarchical clustering
- Principal component analysis (PCA)
- Introduction to neural networks and backpropagation
- Building a first model with a deep learning framework
Week 6
Deployment & Capstone
Ship a model and present an end-to-end project.
- Serializing models and building an inference API
- Monitoring, drift, and responsible AI
- MLOps basics and versioning
- Capstone: end-to-end ML project presentation