AI & ML 3-Month Learning Roadmap PDF — Complete Beginner to Advanced Guide

Want to become an AI or Machine Learning Engineer but not sure where to start? This AI/ML 3-Month Roadmap PDF provides a clear, structured learning plan to master the fundamentals of Artificial Intelligence and Machine Learning — from Python basics to Deep Learning and project building — all within 90 days.

📘 What’s Inside the 3-Month AI/ML Roadmap

This roadmap is divided into three progressive phases, ensuring you learn every essential concept step by step. Each week includes topics, practice resources, and mini projects to help you apply your skills in real-world tasks.

Month 1: Mathematical Foundations & Python

• Python Programming: Learn syntax, loops, functions, and OOP with small projects.
• Mathematics for ML: Linear algebra, calculus, and probability basics for data modeling.
• Data Science Libraries: Practice NumPy, Pandas, Matplotlib, and Seaborn.
• Intro to ML: Learn supervised vs. unsupervised learning and train your first ML model.

Month 2: Core Machine Learning Algorithms

• Regression: Linear, Ridge, and Logistic Regression with case studies.
• Classification: Decision Trees, Random Forests, and SVMs.
• Unsupervised Learning: K-Means, DBSCAN, and PCA for clustering and feature reduction.
• Model Evaluation: Learn accuracy, precision, recall, F1-score, and hyperparameter tuning.

Month 3: Deep Learning & Advanced Concepts

• Neural Networks: Learn perceptrons, activation functions, and backpropagation.
• CNNs & RNNs: Understand architectures like LeNet, LSTM, and apply them to real projects.
• Advanced Topics: Work on transfer learning, GANs, and autoencoders.
• Capstone Project: Build and deploy a full end-to-end ML project using TensorFlow or PyTorch.

🧠 What You’ll Learn

• Core mathematical foundations for AI/ML
• Python programming and data handling
• Feature engineering and model evaluation
• Deep learning with CNNs, RNNs, and TensorFlow
• Project design, experimentation, and deployment

🎯 Why You Should Follow This Roadmap

• Step-by-step weekly plan for 3 months
• Includes project-based learning and real datasets
• 100% beginner-friendly and practical
• Ideal for students and professionals entering AI/ML

🚀 Learning Resources Included

• 📘 Coursera, Fast.ai, and MIT OpenCourseWare
• 💻 Kaggle, UCI ML Repository, and Google Colab
• 📊 TensorFlow, Scikit-learn, and PyTorch documentation
• 🎥 YouTube Channels: StatQuest, Sentdex, Two Minute Papers

💡 Tips for Success

1. Code daily — consistency is key to mastering ML.
2. Join online ML communities for discussions and guidance.
3. Document your progress and projects on GitHub.
4. Focus on hands-on projects, not just theory.
5. Stay curious — AI/ML is a fast-evolving field!

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