Practical guides to learning AI and machine learning, written by Gaurav Jain. Worked examples, projects with complete code and learning plans you can follow.
Machine learning interview questions with worked answers: splits, leakage, precision and recall, baselines, gradient descent and regularisation, in Python.
interview guide10 min read
Learn linear regression with a worked prediction, runnable Python, a held-out test and a baseline. Explain coefficients and check where a line can fail.
tutorial6 min read
Understand machine learning without coding: follow a worked example, read a model report and ask useful questions before starting a project.
explanation6 min read
Use machine learning to flag unusual rows for review. Run an Isolation Forest example, compare simple rules and understand thresholds and false alarms.
tutorial6 min read
Build a predictive analytics experiment with a demand example: define the target, use prediction-time inputs, beat a baseline and test on held-out data.
tutorial6 min read
Explore unlabeled data with a runnable clustering example. Understand cluster IDs, scaling, useful checks and when you still need human labels.
tutorial6 min read
Build three NumPy projects with complete code: summarise weekly sales, clean sensor readings and scale features while practising shapes and masks.
project9 min read
Build three pandas projects with complete Python code: clean orders, join product tables and calculate a daily trend, then check each output.
project9 min read
Build three beginner Python projects with complete code: a study tracker, quiz report and CSV stock checker. Check the output, then extend each one.
project9 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.