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The DS-ML track on Launchpad takes you from statistical foundations to production-style machine learning and deep learning. You will move through the same layers a working ML engineer touches, so the projects you build map cleanly to the questions asked in DS and ML placement interviews.

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What you will learn

Build the statistical intuition that every data science and machine learning workflow depends on, from descriptive summaries to the probability concepts behind modeling decisions.
Work through the core supervised and unsupervised algorithms that anchor most ML interview rounds and real-world tabular problems.
Combine models to squeeze out extra accuracy, and understand why ensemble methods are the go-to choice for competitions and production tabular models.
Learn how text becomes features and predictions, so you can build NLP systems and answer language-focused ML interview questions with confidence.
Move from a single neuron to full deep learning architectures, and connect the theory to hands-on model building.
If you are targeting Gen AI or LLM-focused roles, follow this track with the Gen AI course to layer LLM application building on top of your ML foundation.