Index

The Foundation: What Everyone Needs 🏗️

Path 1: The ML Engineer 🛠️

Path 2: The ML Researcher 🧠

The Hybrid Path: Best of Both Worlds 🦄

What No One Tells You About ML Careers 🧐

Some Random (Banger) Resources I’ve Collected 🤓

Hey there! 👋 I am Yashwanth.

A few people here asked me questions like ‘How do I start with ML?’ or ‘How should my roadmap look like in ML?’, I figured it was time to write up a proper guide that breaks down the journey without the fluff. This blog will dive into the different paths you can take, and what to expect along the way. (tell me what you guys want more and I am happy to update this)

The Foundation: What Everyone Needs 🏗️

No matter which ML path you choose, you'll need these basics:

• Python fundamentals: variables, loops, functions, classes (there are plenty of free courses on YouTube, Codecademy, or Real Python)
• Core ML libraries:
  • NumPy (for numerical operations)
  • Pandas (for data manipulation)
  • Matplotlib/Seaborn (for visualization)
  • Scikit-learn (for classic ML algorithms)
• ML concepts: supervised vs. unsupervised learning, regression, classification, evaluation metrics

Best resources to start with:

• Machine Learning Specialization by Andrew Ng (Coursera) - you can audit for free
• Fast.ai - a practical approach that gets you building models quickly

Timeline: Expect to spend 2-3 months getting comfortable with these basics if you're starting from scratch and working part-time on learning.