📚 Course Overview
This comprehensive course introduces you to the fundamentals and advanced concepts of machine learning, from mathematical foundations to practical implementations using Python and popular ML libraries.
- Mathematical Foundations
- Supervised Learning Algorithms
- Unsupervised Learning
- Neural Networks & Deep Learning
- Model Evaluation & Validation
- Real-world Applications
🎯 Learning Objectives
By the end of this course, you will be able to:
- Understand ML algorithms and their applications
- Implement ML models using Python libraries
- Evaluate and compare different models
- Apply feature engineering techniques
- Deploy ML models in production
- Solve real-world problems with ML
🔬 Laboratory Work
Hands-on programming exercises and projects:
- Python & NumPy Fundamentals
- Data Preprocessing & Exploration
- Linear & Logistic Regression
- Decision Trees & Random Forest
- Clustering & Dimensionality Reduction
- Neural Networks with TensorFlow
- Model Deployment & Production
📊 Assessment Structure
Your performance will be evaluated through:
- Programming Assignments (30%)
- Laboratory Work (20%)
- Mid-term Examination (20%)
- Final Project (20%)
- Class Participation (10%)
📖 Prerequisites
To succeed in this course, you should have:
- Basic programming knowledge (Python preferred)
- Linear Algebra fundamentals
- Statistics and Probability basics
- Calculus (derivatives and optimization)
- Understanding of algorithms and data structures
📚 Recommended Books
- Hands-On Machine Learning by Aurélien Géron
- Pattern Recognition and ML by Bishop
- Elements of Statistical Learning by Hastie
- Introduction to Statistical Learning by James
- Python Machine Learning by Raschka