Practical guides to learning AI and machine learning, written by Gaurav Jain. Worked examples, projects with complete code and learning plans you can follow.
Learn core Python first, then NumPy, pandas, Matplotlib and scikit-learn, in that order. What each stage gives you, what to skip and when you know enough.
learning path9 min read
Start machine learning with Python, then NumPy and pandas, the maths a model uses, and linear regression. A four-step path for beginners, with checkpoints.
learning path10 min read
Three machine learning portfolio project briefs for prediction, document search and image classification, each with a baseline and clear deliverables.
project5 min read
Learn AI from scratch in order: Python, data tools, maths, statistics, a first model, then an AI app. A 12-week plan with checkpoints and free resources.
learning path13 min read
A machine learning engineer roadmap in seven stages, from Python as software to a portfolio. Each stage links to free lessons and ends in a checkpoint.
learning path11 min read
Get an AI or machine learning internship: the four skills interviewers test, two projects worth building, where to find openings and how to apply.
learning path8 min read
An ML engineer trains and deploys models; an AI engineer builds products on models like LLMs. Compare the work, skills and maths, and what to learn.
comparison8 min read
The courses take each idea from the intuition and the maths to code you write yourself, from your first line of Python to machine learning.