AI & Machine Learning Mastery
Master artificial intelligence and machine learning with Python, TensorFlow, and real-world applications.
Learning Outcomes
By course completion, learners will:
Course Orientation (Module 0)
Purpose: Establish AI/ML engineering mindset.
Contents:
- • AI/ML landscape and applications
- • Mathematical foundations review
- • Python environment setup
- • Model development lifecycle
- • Ethics in AI/ML
- • Research paper reading strategies
Deliverable:
- • Development environment setup
- • GitHub repository with project structure
- • Learning goals documentation
Learning Module Template
Each module follows this fixed internal structure:
1. Concept Overview
Algorithm intuition, use cases, real-world applications
2. Theory (Minimal but Precise)
Mathematical foundations, model architecture, training process
3. Engineering Perspective
Model selection, hyperparameter tuning, production considerations
4. Hands-On Tasks
Data preprocessing, model training, evaluation metrics
5. Mini Assignment
Improve model performance, experiment with architectures
6. Review Checklist
Model validation, performance benchmarks, code review
Course Phases
Phase 1
(Week 1-3)Python & ML Basics
Python fundamentals, NumPy, Pandas, Data preprocessing
Phase 2
(Week 4-6)Machine Learning
Supervised/Unsupervised learning, Model training
Phase 3
(Week 7-9)Deep Learning
Neural Networks, TensorFlow, Keras
Phase 4
(Week 10-12)Advanced Topics
NLP, Computer Vision, Model deployment
Phase 5
(Week 13-14)Capstone Project
End-to-end ML project, Evaluation
Evaluation & Certification
Mandatory components: