Artificial Intelligence
AI & Machine Learning
From AI fundamentals to your first end-to-end ML project
About this course
<p>A practical, video-first journey through Artificial Intelligence and Machine Learning: core concepts, the Python toolkit (NumPy, Pandas, Matplotlib), data preprocessing and your first supervised learning models. Advanced modules (deep learning, NLP, deployment) will be added over time.</p>
What you'll learn
- Understand AI vs Machine Learning vs Deep Learning vs Generative AI
- Use Python, NumPy, Pandas and Matplotlib for data work
- Clean, transform and encode real-world datasets
- Build and evaluate your first supervised learning models
Requirements
- Basic computer literacy
- No prior programming experience required
Course Content
AI & ML Foundations10 lessons
- AI, Machine Learning, Deep Learning & Generative AI Explained
- All About Machine Learning & Deep Learning
- Ultimate AI/ML Roadmap for Beginners
- How to Learn AI & Machine Learning — Full Roadmap
- What Is Artificial Intelligence?
- What's the Difference Between AI, ML and Deep Learning?
- Supervised vs Unsupervised vs Reinforcement Learning
- 5 Real-World Applications of Deep Learning
- Machine Learning Full Course (10 Hours)
- Project: End-to-End Python ML Project
Python for Machine Learning6 lessons
- Python Basics: Data Types, Variables & Operators
- Conditional Statements and Loops
- Python Modules Part 1: The math Module
- Python Modules Part 2: The random Module
- NumPy vs Pandas
- Data Visualization with Matplotlib & Seaborn
Data Preprocessing6 lessons
- Data Preprocessing & Data Cleaning
- Handling Missing Values (Imputation Methods)
- Data Transformation: Normalization & Standardization
- Encoding Categorical Variables (One-Hot & Label Encoding)
- Feature Selection Techniques
- Handling Imbalanced Data
Supervised Learning3 lessons
- Supervised Learning Algorithms — Overview
- Introduction to Supervised Learning
- Regression Algorithms