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The DS-ML track on Launchpad takes you from working with raw data to building and evaluating machine learning and deep learning models. It is organised into three areas, so you can start wherever your foundation is and move up without gaps.

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What you will learn

Learn to handle, clean, and interpret data, and to communicate what you find. This area covers analytics foundations, working with data in Python, and the business intelligence tools most commonly asked for in analytics interviews.
Build the statistical and mathematical intuition every modelling decision rests on, from descriptive statistics and exploratory data analysis to probability, distributions, inferential statistics, and the linear algebra and calculus behind machine learning.
Work through the supervised algorithms that anchor most ML interview rounds and real tabular problems, covering both regression and classification, along with how models are evaluated and tuned.
Combine models to push accuracy further, and learn the unsupervised techniques used for clustering, segmentation, and dimensionality reduction when labelled data is not available.
Move from a single neuron to full neural network architectures, and connect the theory to hands-on model building.
Apply deep learning to language and images, covering natural language processing through to sequence and attention models, and the convolutional foundations of computer vision.

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Learn by doing

Courses in this track pair concepts with practice rather than video alone:
  • MCQ practice after each concept to check your understanding before you move on.
  • Coding question sets and assignments so you write the code yourself, not just read it.
  • Assessments throughout the statistics and machine learning courses.
  • Projects in notebooks you open in Colab, solve, and upload for evaluation.
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.

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Keep building

Gen AI

Generative AI foundations and building LLM applications end to end.

Programming

Strengthen your Python before taking on the statistics and modelling courses.