Machine Learning with Python and Scikit-Learn

Stem & Robotics Machine Learning is the backbone of modern AI, predictive analytics, and data-driven decision-making.

This course teaches you machine learning with Python using Scikit-Learn, one of the most widely used ML libraries. You’ll cover supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, and real-world applications.

With live mentor-led coding labs and pre-recorded tutorials, you’ll gain practical experience by applying ML to real datasets. By the end, you’ll build and deploy a capstone ML project.

Target Audience

• Students and professionals starting in AI/ML
• Data analysts moving into machine learning
• Developers integrating ML into applications
• Learners preparing for ML/AI careers

Prerequisites

• Python programming (NumPy, Pandas basics)
• Basic math & statistics (linear algebra, probability helpful)

Learning Outcomes

By completing this course, learners will:

• Understand ML fundamentals & workflows
• Implement supervised and unsupervised ML algorithms
• Use Scikit-Learn for ML pipelines
• Evaluate models using accuracy, precision, recall, F1-score
• Perform hyperparameter tuning with GridSearchCV
• Build ML applications for business & real-world domains

Live Components

• Weekly mentor-led ML problem-solving labs
• Group coding sessions for ML projects
• Capstone project showcase

Pre-recorded Access

• Tutorials on Scikit-Learn, regression, classification, clustering
• Pre-cleaned datasets for practice
• Recorded project walkthroughs