What you'll learn in Build Machine Learning Systems from Scratch
What you'll leave with
- 1Working machine learning models you built from scratch
- 2Strong foundation for advanced AI and data work
- 3Confidence to apply machine learning to real problems
- 4Certificate of completion
Course Curriculum
Week 1Machine Learning Fundamentals
Machine Learning Fundamentals
- What machine learning is and when to use it
- Types of machine learning: supervised and unsupervised
- How data flows through a machine learning system
- Understanding features, labels, and datasets
- Common machine learning use cases in the real world
- Common beginner mistakes and misconceptions
- Hands-On Outcome + Resources
Week 2Working with Data
Working with Data
- Understanding raw vs cleaned data
- Handling missing and inconsistent values
- Basic data transformation and normalization
- Feature selection and feature creation
- Splitting data into training and testing sets
- Avoiding data leakage
- Hands-On / Outcome + Resources
Week 3Building and Training Models
Building and Training Models
- Training simple machine learning models
- Understanding how models learn from data
- Making predictions and interpreting results
- Measuring model performance
Week 4Evaluate, Improve, and Ship
Evaluate, Improve, and Ship
- Identifying common modeling mistakes
- Improving results through iteration
- Capstone: Build your own ML model
- Presentation and certificate
Who Build Machine Learning Systems from Scratch is designed for
Prerequisites
Your Instructors
Aki Wijesundara, PhD
AI Founder | Educator | Google AI Accelerator Alum
Aki Wijesundara is an AI leader with a PhD in Machine Learning and extensive experience mentoring startups at Google's AI Accelerator. With a career spanning both research and applied AI, Aki has taught 5,000+ students worldwide how to design and deploy production-ready AI systems. He has worked across cutting-edge areas of applied AI, from LangChain and RAG pipelines to observability and large-scale deployment. As a researcher and educator, Aki bridges the gap between theory and practice, making complex systems approachable and actionable for engineers, founders, and product leaders.
Manu Jayawardana
Exited AI Founder | Co-Founder, Snapdrum | Co-Founder & CEO, Krybe
Manu Jayawardana is a serial entrepreneur with multiple AI startup successes. He exited Rise AI, a fintech app with over 35,000 users, to a private investor. He co-founded Snapdrum, delivering enterprise AI systems and scaling paid acquisition engines that drove 5,000+ premium customers. He is also Co-Founder & CEO of Krybe, a London-based Voice AI startup serving 1,000+ users and part of the NVIDIA Inception ecosystem. With a background in quant finance and AI engineering, he operates at the intersection of AI, distribution, and execution.
What Students Say
“The AI training approach is outstanding. Our team learned to build practical AI solutions that we could implement immediately in our educational platform. The hands-on methodology made complex AI concepts accessible to our entire development team.”
Kavi T.
CEO of Tilli Kids / Stanford PhD
“I sent my team through this training for upskilling, and the results have been remarkable. Within weeks, they became much more efficient at building automations and deploying AI agents at work. This program bridges the gap between theory and practice and it's had a real impact on our productivity.”
Aamir Faaiz
CEO of Bayseian
Frequently Asked Questions about Build Machine Learning Systems from Scratch
What will I learn in Build Machine Learning Systems from Scratch?
Understand what machine learning is and how it works from the ground up. Learn the difference between supervised and unsupervised learning. Collect, clean, and prepare data for modeling correctly. Build and train machine learning models step by step. Evaluate model performance with basic metrics. Identify and fix common modeling mistakes through iteration
Who is Build Machine Learning Systems from Scratch designed for?
Beginners who want to learn machine learning from scratch. Career switchers looking to enter machine learning or AI roles. Developers who want a strong foundation in machine learning. Students exploring a future in AI, data, or software engineering
What are the prerequisites for Build Machine Learning Systems from Scratch?
No prior machine learning experience required. Basic computer literacy
How long does Build Machine Learning Systems from Scratch take?
4 weeks. Format: Cohort-based.
What will I leave with after completing Build Machine Learning Systems from Scratch?
Working machine learning models you built from scratch. Strong foundation for advanced AI and data work. Confidence to apply machine learning to real problems. Certificate of completion
Is Build Machine Learning Systems from Scratch available online?
Yes, Build Machine Learning Systems from Scratch is delivered entirely online as a cohort-based.
Who teaches Build Machine Learning Systems from Scratch?
Aki Wijesundara, PhD — AI Founder | Educator | Google AI Accelerator Alum. Manu Jayawardana — Exited AI Founder | Co-Founder, Snapdrum | Co-Founder & CEO, Krybe
How do I enroll in Build Machine Learning Systems from Scratch?
You can enroll via Maven at https://maven.com/theaiinternship/build-machine-learning-systems-from-scratch. Click the "Enroll on Maven" button on this page.
Topics covered
Corporate Training & Team Upskilling
Train your entire team on Build Machine Learning Systems from Scratch. We offer corporate group training, custom cohorts, and enterprise licensing. Trusted by teams at Google, Meta, Oracle, and more.