Hands-On Machine Learning Syllabus 🧠

A structured guide to mastering machine learning through practical projects.

📘 Introduction

Welcome to the Hands-On Machine Learning tutorial! This syllabus is designed for learners who want to dive into ML by building real-world models.

🧩 Core Concepts

1. Basics of ML: Supervised vs. unsupervised learning, overfitting, and evaluation metrics.
2. Python Libraries: Use of NumPy, Pandas, and Scikit-learn for data manipulation.
3. Neural Networks: Introduction to deep learning and TensorFlow/PyTorch.

🛠 Hands-On Projects

• Project 1: Predict housing prices using linear regression.
• Project 2: Classify images with convolutional neural networks.
• Project 3: Build a recommendation system with collaborative filtering.

🚀 Advanced Topics

• Optimization Techniques: Gradient descent and stochastic optimization.
• Model Tuning: Hyperparameter tuning with GridSearchCV.
• Ethics in AI: Bias mitigation and responsible ML practices.

📚 Resources

• Machine Learning Fundamentals for beginners.
• Advanced Deep Learning to expand your knowledge.
• Python for Data Science for coding essentials.

Explore more tutorials and dive deeper into your ML journey! 🌟