How to learn AI and ML for beginners?

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.