Comprehensive AI/ML/DL Learning Roadmap

A definitive, step-by-step learning roadmap for beginners entering the fields of Artificial Intelligence, Machine Learning, and Deep Learning.

Table of Contents

• Foundation Phase
• Core ML Phase
• Deep Learning Phase
• Specialization Phase
• Certification Path
• Continuous Learning System
• Essential Tools
• Community Engagement
• Success Metrics

Foundation Phase (3-4 months)

Mathematics (Month 1-2)

• Primary: Khan Academy Linear Algebra
• Essential: 3Blue1Brown's Essence of Linear Algebra
• Book: "Mathematics for Machine Learning" (Imperial College London)
• Practice: Daily Khan Academy exercises

Programming (Month 2-3)

• Primary: Python for Everybody
• Supplementary: Automate the Boring Stuff with Python
• Tools: VS Code, Jupyter Notebooks
• Project: Build a data analysis dashboard

Time Split: 60% practice, 40% theory
Weekly Commitment: 15-20 hours

Core ML Phase (4-5 months)

Fundamentals (Month 4-5)

• Primary: Machine Learning Specialization (Andrew Ng)
• Book: "Hands-On Machine Learning with Scikit-Learn" (3rd Ed.)
• Project: Kaggle Titanic Competition

Applied ML (Month 6-7)

• Primary: IBM Machine Learning Professional Certificate
• Tools: scikit-learn, pandas, numpy
• Project: Real-world regression problem

Time Split: 50% coding, 30% theory, 20% projects
Weekly Commitment: 20-25 hours

Deep Learning Phase (3-4 months)

Neural Networks (Month 8-9)

• Primary: Deep Learning Specialization
• Framework: PyTorch
• Project: Image classification system

Modern AI (Month 10-11)

• Primary: Hugging Face NLP Course
• Supplementary: Fast.ai Practical Deep Learning
• Project: Fine-tune an LLM

Time Split: 40% coding, 30% theory, 30% projects
Weekly Commitment: 25-30 hours

Specialization Phase (2-3 months)

Choose One Track:

1. LLM Track

   • Full Stack LLM Bootcamp
   • Project: Build a custom chatbot

2. Computer Vision Track

   • CS231n
   • Project: Object detection system

3. MLOps Track

   • MLOps Fundamentals
   • Project: Deploy ML system on cloud

Time Split: 30% theory, 70% practical
Weekly Commitment: 25-30 hours

Certification Path

1. IBM AI Engineering Professional Certificate
2. AWS Machine Learning Specialty
3. TensorFlow Developer Certificate

Continuous Learning System

Daily Practice

• 30 minutes coding (LeetCode/HackerRank)
• Read one ML paper abstract
• Work on current project

Weekly Practice

• One Kaggle notebook
• One ML paper in depth
• Two hours math review

Monthly Goals

• Complete one project
• Participate in one competition
• Learn one new tool/framework

Essential Tools

• Version Control: Git/GitHub
• Cloud: AWS/GCP
• IDE: VS Code with Python extensions
• Notebooks: Jupyter/Colab
• ML Frameworks: PyTorch, Hugging Face
• Visualization: Matplotlib, Seaborn

Community Engagement

• Join Fast.ai forums
• Follow r/MachineLearning
• Subscribe to The Batch
• Participate in Kaggle discussions

Success Metrics

• GitHub portfolio with 3-5 significant projects
• Active Kaggle profile
• One deployed application
• Professional certification
• Understanding of recent papers

Key Principles

1. Structured learning from traditional courses
2. Practical application through projects
3. Modern tools and frameworks
4. Community engagement
5. Continuous learning practices

Remember: Consistency over intensity. Follow the time splits and weekly commitments strictly.