How to learn AI and ML for beginners?

March 19, 2024

For an even more streamlined journey from basic to advanced in AI and ML, focusing on the essentials, here’s a refined blueprint. This plan emphasizes a gradual buildup of skills, starting from absolute basics, progressing through core machine learning concepts, and then advancing into deep learning and specialization, all while integrating practical project experience.


Initial Setup: Getting Started (1-3 Months)

  • Objective: Familiarize yourself with basic programming and mathematical concepts.
  • Programming: Start with Python basics through interactive platforms like Codecademy or Khan Academy.
  • Mathematics: Brush up on basic algebra and statistics through Khan Academy.

Year 1: Building Foundations (4-12 Months)

Programming Skills

  • Python: Deep dive into Python with "Automate the Boring Stuff with Python" by Al Sweigart.

Core Mathematics

  • Linear Algebra and Calculus: Use MIT OpenCourseWare or Khan Academy for foundational courses.
  • Statistics: "Statistics 101" on Khan Academy or Coursera.

Year 2: Core Machine Learning and Data Handling (Months 13-24)

Machine Learning Basics

  • Course: "Machine Learning" by Andrew Ng on Coursera for a comprehensive foundation.
  • Apply Concepts: Tackle simple ML projects on Kaggle to apply what you've learned.

Data Science Introduction

  • Learn: Python for Data Analysis with "Python for Data Science Handbook" by Jake VanderPlas.
  • Projects: Start with data cleaning and visualization projects.

Year 3: Deep Learning and Specializations (Months 25-36)

Introduction to Deep Learning

  • Deep Learning: Begin with "Neural Networks and Deep Learning" by Michael Nielsen (free online).
  • Practical Application: Use TensorFlow or PyTorch for hands-on projects.

Choose a Specialization

  • Options: Dive into NLP, computer vision, or reinforcement learning based on your interest. Coursera and Udacity offer specific courses in these areas.

Year 4: Advanced Techniques and Real-World Applications (Months 37-48)

Advanced Projects

  • Objective: Implement complex projects that solve real-world problems. Use GitHub to document and share your work.

Cutting-Edge Learning

  • Continuous Learning: Stay updated with the latest research and techniques through arXiv and attending webinars or virtual conferences.

Soft Skills and Networking

  • Communication: Enhance your presentation and communication skills.
  • Networking: Engage with the community through LinkedIn, GitHub, and AI/ML conferences.

Continuous Learning and Improvement

  • Review and Refresh: Regularly revisit core concepts and stay abreast of new developments.
  • Contribute: Consider contributing to open-source projects related to your area of specialization.

Final Tips

  • Adapt and Customize: Tailor this blueprint to your learning pace and areas of interest. The field evolves, so should your learning path.
  • Hands-on Practice: The importance of applying theory through projects cannot be overstated. They are crucial for understanding and skill development.
  • Stay Curious: AI and ML are vast fields. Always be open to exploring new subfields and technologies.