Supervised Learning: Classification & Regression
Go deep on supervised learning. Master linear & logistic regression, decision trees, SVMs, neural networks, and advanced ensembles — with real datasets and production-grade code.
What You'll Learn
Implement and tune linear and logistic regression from first principles
Build and prune decision trees to avoid overfitting
Train and optimise Random Forest and Gradient Boosting models
Use Support Vector Machines for both classification and regression
Apply the correct model for a given business problem
Interpret model predictions with SHAP values and feature importance
Curriculum Breakdown
Learning Format
Video Lessons
High-quality recorded lessons you can watch at your own pace.
Hands-on Projects
Real-world projects that reinforce every concept you learn.
Certificate
Earn a verifiable certificate upon successful completion.
Certification Details
Supervised Learning Certificate
Issued by Tech101
Prove your mastery of supervised learning algorithms and their real-world application.
Certificate Requirements
- Shareable on LinkedIn
- PDF download
- Unique verification ID
Completion Certificate
Awarded upon finishing all course content and submitting projects. Shows dedication and completion.
Graded Certificate
Earned by passing the final assessment with 70%+ score. Demonstrates verified skill proficiency.
Your Instructor
Sarah Johnson
Data Scientist & ML Engineer
Sarah has 10 years of experience in data science and machine learning, previously at Google AI and DeepMind.
Requirements & Prerequisites
Technical Requirements
- Machine Learning Fundamentals (or equivalent scikit-learn experience)
- Comfortable with Python, Pandas, and NumPy
Who This Course Is For
- ML students ready to go beyond introductory algorithms
- Data scientists who want to master the supervised learning toolkit
- Data Science & AI track students
Student Reviews
Frequently Asked Questions
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