Week 1
Python, data and notebooks
Set up notebooks, load data and perform basic analysis.
PythonJupyterPandas
Detailed learning path
Machine Learning Foundations
A 10-week project-based ML path covering Python workflows, classical ML, model evaluation and applied prediction systems.
Weekly roadmap
What you will learn week by week
Inspired by portfolio-first learning platforms: each module has a clear outcome, practice activity and project artifact.
Week 2
Data cleaning and visualization
Prepare real datasets and communicate patterns.
PandasMatplotlibEDA
Week 3
Linear regression from scratch
Build regression models and interpret coefficients.
RegressionNumPy
Week 4
Classification fundamentals
Logistic regression, confusion matrix and classification metrics.
ClassificationMetrics
Week 5
Decision trees and ensembles
Understand decision trees, random forests and feature importance.
TreesRandom Forest
Week 6
Unsupervised learning
Clustering, dimensionality reduction and pattern discovery.
KMeansPCA
Week 7
Model evaluation and tuning
Cross-validation, overfitting, hyperparameter tuning and pipelines.
CVTuning
Week 8
R for data science overview
Use R for statistical summaries and visualization.
RStatistics
Week 9
MATLAB automated ML overview
Explore MATLAB-style automated ML workflows and comparison.
MATLABAutoML
Week 10
Capstone build week
Build, evaluate and present an ML prediction system.
CapstonePortfolio
Capstone project
Prediction System Capstone
Build a real prediction system such as crop yield, rainfall, student performance or customer behavior prediction.
Pythonscikit-learnR/MATLAB exposure