📚 Topic / Module Names for the Course

Module 1: Introduction to Machine Learning

1. What is Machine Learning?
2. ML vs AI vs Deep Learning
3. Real‑world applications of ML

Module 2: Understanding Data

1. What is data? Types of data
2. Features and labels
3. Data preprocessing basics

Module 3: Types of Machine Learning

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
4. Semi‑supervised & Self‑supervised (optional)

Module 4: Core ML Algorithms

1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. K‑Nearest Neighbors (KNN)
5. Clustering (K‑Means)
6. Naïve Bayes

Module 5: Model Training Workflow

1. Train–Test split
2. Overfitting vs Underfitting
3. Bias–Variance trade-off
4. Hyperparameters explained simply

Module 6: Model Evaluation

1. Accuracy, Precision, Recall, F1‑Score
2. Confusion Matrix
3. Regression metrics (MSE, RMSE, MAE)

Module 7: Practical ML Pipeline

1. Data collection
2. Data cleaning basics
3. Feature engineering overview
4. How to choose the right algorithm

Module 8: Real‑World ML Applications

1. Business cases & industry workflows
2. ML in finance, manufacturing, healthcare, marketing
3. ML project life cycle (CRISP‑DM)

Module 9: Tools You Should Know

1. Python & Jupyter
2. sklearn introduction
3. How ML is implemented in companies

Module 10: Your ML Learning Roadmap

1. What to learn next (Deep Learning, NLP, LLMs)
2. Career paths in ML
3. Projects to start with