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AI & Machine Learning
From AI fundamentals to your first end-to-end ML project
25 lessons8h 20m0 enrolledBeginner
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
About this course
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.
Curriculum · 4 sections
AI & ML Foundations
- 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 Learning
- 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 Preprocessing
- 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 Learning
- Supervised Learning Algorithms — Overview—
- Introduction to Supervised Learning—
- Regression Algorithms—
Requirements
- Basic computer literacy
- No prior programming experience required
Earn a certificate of completion when you finish this course.