50 Days to AI/ML: From Zero to Hero (for Non-CS Background)

Intrigued by AI but lack a CS background? Feeling overwhelmed and unsure where to begin? You're not alone! This post will guide you through a 50-day roadmap specifically designed for beginners with no prior coding experience.

What You'll Need:

• Problem-solving mindset and a love for learning: That's the most crucial ingredient.
• Basic high school math: A grasp of linear algebra, matrices, statistics, and probability is ideal, but don't worry if you're a little rusty.

Forget the CS degree!

This roadmap focuses on practical application, not complex theory. You'll be up and running with code in no time. Here's a day-by-day breakdown:

Days 1-6: Building Your Foundation

• Day 1: Machine Learning Specialization by Andrew Ng (Deeplearning.ai or Coursera)
• Days 2-3: Master Python basics (PRINT statements, FUNCTIONS, CLASSES) using resources like Realpython, GeeksforGeeks, or video tutorials. Practice is key!
• Day 4: Deep dive into NumPy, focusing on dimensions and indexing. (Official documentation or same sources as Python)
• Day 5: Get comfortable with Pandas (data manipulation).
• Day 6: Learn Matplotlib for data visualization.

Days 7-34: Deep Learning with Andrew Ng

Delve into the renowned Deep Learning Specialization by Andrew Ng. It's a 5-course powerhouse (available on Coursera) that will equip you with the core concepts of deep learning. Aim for 4 weeks to comfortably complete it, with breaks for those inevitable moments of frustration. Remember, the math is there for understanding, not memorization. Focus on the bigger picture and practical application, especially in Courses 4 and 5.

Day 15 (or after Course 3): Tensorflow

TensorFlow is a must-have library for deep learning projects. After completing Course 3, explore TensorFlow's 4-part video series "Introduction to Machine Learning: ML zero to hero" on their YouTube channel. Practice exercises are included!

Days 35-39: Setting Up Your Development Environment

• Day 35: Download and install Conda, a package manager for Python. Read the official documentation and practice using it.
• Day 36: Choose your Python IDE: PyCharm (community version is free) is a popular option. Learn keyboard shortcuts for faster coding. Tutorials are available within the IDE.
• Day 37: GitLab and Git version control: Learn the basics of setting up and managing Git repositories using official resources.
• Day 38: Explore Flask (or Django) web frameworks. Miguel Grinberg's blog is a great resource for Flask.

Day 39: Master Docker for deploying Flask APIs.

Days 40-45: Sharpening Your Skills

Practice makes perfect! Use Python and NumPy in your preferred IDE to solidify your coding skills. Focus on areas that need improvement.

Days 46-49: Refresh and Prepare

Quickly revisit core machine learning and deep learning concepts. This is about practical application, not theory review. Get ready to put your knowledge to the test!

Day 50: Project Time!

Congratulations! You've reached the summit. Now comes the fun part: building your first deep learning or AI project! Choose something that excites you – the possibilities are endless.

This roadmap equips you with the foundational skills and knowledge to embark on your AI/ML journey. Remember, consistency and perseverance are key. Best of luck on your exciting adventure into the world of AI!