Machine Learning

Course Description

Machine Learning (ML) is the backbone of modern AI, enabling systems to learn from data and make intelligent decisions without explicit programming. This course provides a comprehensive introduction to ML concepts, algorithms, and practical applications.

You’ll gain hands-on experience in building predictive models, evaluating their performance, and deploying solutions that solve real-world problems. Whether you aim to become a data scientist, AI engineer, or tech innovator, this course equips you with the skills to harness the power of machine learning.

What You’ll Learn?

• Understand the fundamentals of Machine Learning and its real-world applications
• Explore different types of ML: Supervised, Unsupervised, and Reinforcement Learning
• Preprocess and clean data for ML models
• Build regression and classification models using Python
• Implement clustering and dimensionality reduction techniques
• Evaluate model performance using metrics like accuracy, precision, recall, and F1-score
• Work with decision trees, random forests, and ensemble methods
• Introduction to deep learning and neural networks
• Deploy ML models for practical applications and projects