What You'll Learn
Build and train supervised & unsupervised machine learning models from scratch
Design and deploy deep neural networks using PyTorch and TensorFlow
Work with Large Language Models (LLMs) and fine-tune transformer architectures
Engineer production-ready ML pipelines with CI/CD and model monitoring
Apply computer vision techniques: CNNs, object detection, image segmentation
Perform NLP tasks including sentiment analysis, summarization, and RAG systems
Understand AI ethics, bias detection, and responsible AI deployment
Present data insights and model results to non-technical stakeholders
Program Curriculum
Python for Data Science & ML Foundations
5 lessons · 6 hrs · 2 projectsClassical Machine Learning with Scikit-Learn
6 lessons · 7 hrs · 2 projectsDeep Learning with PyTorch
7 lessons · 9 hrs · 2 projectsNatural Language Processing & Transformers
6 lessons · 8 hrs · 2 projectsMLOps & Production Deployment
5 lessons · 7 hrs · 1 projectResponsible AI, Ethics & Governance
4 lessons · 4 hrsCapstone Project
End-to-end ML system from problem definition to deploymentSkills You'll Gain
Requirements
Basic familiarity with Python programming (variables, loops, functions). A free refresher module is provided for beginners.
High school-level mathematics (algebra & basic statistics). Calculus and linear algebra are introduced in-program.
A modern laptop or desktop computer (Mac, Windows, or Linux) with internet access.
Approximately 8–12 hours per week of dedicated study time.
Your Certificate of Completion
Upon successfully completing all coursework and the capstone project, you'll receive a verified digital certificate you can share directly to LinkedIn, embed on your portfolio, and present to employers.
Certificate of Completion
for Industry Readiness
Meet Your Instructors
Dr. Priya Nair
Dr. Nair holds a Ph.D. in Machine Learning from Carnegie Mellon University and spent eight years at Google Brain working on large-scale recommendation systems. She has published over 40 peer-reviewed papers and holds 12 patents in the AI/ML domain.
Marcus Webb, M.Sc.
Marcus leads the MLOps curriculum and brings 10 years of production ML experience from companies including Databricks, Stripe, and two Y Combinator startups. He is a core contributor to several open-source ML infrastructure projects.