Machine Learning with Python Online Course

About the course

About the Course

If you're aspiring to become a data scientist and apply machine learning to real-world problems, this course is a great starting point. You'll build a portfolio of 12 hands-on projects, such as predicting house prices, classifying flowers, recognizing handwritten digits, detecting cancer cells, and forecasting employee attrition. Along the way, you'll master essential tools and techniques, including setting up a Python environment, using popular IDEs like Jupyter and Spyder, understanding key ML metrics, applying regression and classification models, using ensemble methods like bagging and boosting, and working with unsupervised learning techniques such as clustering.

By the end of the course, you’ll be equipped with the practical skills and project experience needed to tackle real-world machine learning challenges or secure a job in the field.

Who should take this

• Beginners to machine learning with basic Python experience
• Professionals and graduates aiming to build a project portfolio
• Anyone wanting practical skills in classification, regression, clustering, and ensemble ML
• Excel users looking to scale up to Python‑based analytics
• Aspiring data scientists seeking employment-ready experience

Course outline

1. Setting Up Your Python Environment
   • Installing Anaconda, Jupyter, Spyder
   • Navigating notebook interfaces
2. Exploring ML Algorithms via Projects
   • Flower classification (SVM)
   • House price prediction (regression)
   • Handwriting digit recognition
   • Employee attrition prediction (decision trees)
3. Unsupervised Learning Techniques
   • Hierarchical clustering
   • k‑Means clustering
4. Model Evaluation and Metrics
   • MSE, R‑squared, confusion matrix, precision & recall
   • Train/test split, K‑fold and Stratified cross‑validation
5. Advanced Model Strategies
   • Bagging, boosting, stacking ensembles
6. Visualizing Insights
   • Plotting with Matplotlib and Seaborn
   • Data storytelling
7. Capstone and Portfolio Projects
   • Complete end‑to‑end ML pipelines across 12 real‑world tasks