Mastering Supervised Learning
At a Glance
This is an immersive course designed to provide participants with practical experience in supervised learning algorithms and techniques. Participants will gain hands-on experience in building, training, and evaluating supervised machine learning models using popular Python libraries such as scikit-learn.
Objectives
By the end of the course, participants will be equipped to:
- Understand the principles of supervised learning and its applications in real-world problems
- Implement various supervised learning algorithms
- Preprocess and prepare data for training supervised learning models
- Evaluate and interpret model performance using appropriate metrics and techniques
- Apply best practices for model selection, tuning, and validation in supervised learning projects
Prerequisite
Participants should have a basic understanding of Python programming and familiarity with fundamental concepts in machine learning. Prior exposure to data preprocessing, model evaluation, and basic statistical concepts is recommended but not required.
Curriculum
- 8 Sections
- 0m Duration
Introduction to Supervised Learning
Regression Algorithms
Classification Algorithms
Preprocessing and Feature Engineering
Model Evaluation and Validation
Model Selection and Tuning
Advanced Topics in Supervised Learning
Real-world Applications and Case Studies
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- Unsupervised learning techniques
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