Machine Learning Mastery

Course Overview

Syllabus

Week 1: Foundations of Machine Learning

1. Introduction to ML and its types (supervised, unsupervised, reinforcement).
2. Setting up the environment: Python, Jupyter Notebook, and essential libraries (NumPy, Pandas, Matplotlib).
3. Data preprocessing: Cleaning, normalization, and handling missing values.
4. Exercise: Load and explore a dataset, perform data cleaning and visualization.

Week 2: Supervised Learning

1. Regression: Linear and polynomial regression.
2. Classification: Logistic regression, decision trees, and random forests.
3. Metrics: Accuracy, precision, recall, and F1-score.
4. Exercises: Implement linear regression on a housing price dataset.

Week 3: Unsupervised Learning

1. Clustering: K-means and hierarchical clustering.
2. Dimensionality reduction: PCA.
3. Real-world applications of clustering.
4. Exercise: Cluster customers in a retail dataset to identify patterns.

Week 4: Advanced Topics and Deployment

1. Neural networks basics and introduction to deep learning.
2. Model evaluation and hyperparameter tuning.
3. Deploying ML models with Flask or FastAPI.
4. Mini Project: Predict customer churn using a given dataset and deploy it as an API.