Welcome to the Machine Learning for Absolute Beginners course!
This course is designed for those who want to get started with Machine Learning using simple explanations, real-world examples, hands-on code, and curated video links.
- Level: Beginner
- Pre-requisites: Basic Python, high school math (algebra & basic statistics)
- Tools: Python, Jupyter Notebooks, scikit-learn, pandas, NumPy, matplotlib
- Learning Outcomes:
- Understand ML concepts clearly
- Learn key ML algorithms
- Work on real-world mini-projects
- Build confidence to explore intermediate ML topics
| Module | Title | Description |
|---|---|---|
| 1 | What is Machine Learning? | Introduction, real-world examples, types of ML |
| 2 | Tools of the Trade | Set up Python, Jupyter, and essential libraries |
| 3 | Understanding Data | Learn about features, labels, data types |
| 4 | Preprocessing Data | Clean, normalize, and prepare datasets |
| 5 | Linear Regression | Build your first ML model |
| 6 | Classification (Logistic, KNN) | Learn to classify emails, flowers, and more |
| 7 | Evaluation Techniques | Accuracy, Confusion Matrix, Cross Validation |
| 8 | Decision Trees & Random Forests | Learn advanced models with visual examples |
| 9 | Neural Networks Basics | A beginner-friendly intro to deep learning |
| 10 | Real-world Mini Projects | Stock predictor, recommender system, etc. |
- Clone the repo:
git clone https://github.com/yourusername/ml-for-beginners.git cd ml-for-beginners