Dive deep into AI foundations. Study the mathematical theory, read seminal papers, and understand the principles behind modern machine learning.
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Strong foundation in linear algebra, calculus, and optimization
Expert Python skills; experience with ML frameworks
Stanford CS229: Machine Learning
AdvancedMachine Learning with Python: from Linear Models to Deep Learning
AdvancedComputational Linear Algebra for Coders
AdvancedTransformers, explained: Understand the model behind GPT, BERT, and T5
AdvancedFrom Deep Learning Foundations to Stable Diffusion
AdvancedState-of-the-Art Machine Learning Papers Implementation
AdvancedNatural Language Processing Specialization
AdvancedStatistical Learning
AdvancedLeveraging AI in User Research & Design
IntermediateUsing AI in a Fraud Investigation Self-Study Course
IntermediateFacial Recognition Considerations for Researchers
IntermediateExploratory Data Analysis with R: A Case Study
IntermediateFrom Paper to Production: Automating Data Quality for AI
BeginnerUsing AI for UX Design and Research
IntermediateML Paper Explanations - Yannic Kilcher
AdvancedStanford CS229: Machine Learning
AdvancedMachine Learning with Python: from Linear Models to Deep Learning
AdvancedComputational Linear Algebra for Coders
AdvancedTransformers, explained: Understand the model behind GPT, BERT, and T5
AdvancedFrom Deep Learning Foundations to Stable Diffusion
AdvancedState-of-the-Art Machine Learning Papers Implementation
AdvancedNatural Language Processing Specialization
AdvancedStatistical Learning
AdvancedLeveraging AI in User Research & Design
IntermediateUsing AI in a Fraud Investigation Self-Study Course
IntermediateFacial Recognition Considerations for Researchers
IntermediateExploratory Data Analysis with R: A Case Study
IntermediateFrom Paper to Production: Automating Data Quality for AI
BeginnerUsing AI for UX Design and Research
IntermediateML Paper Explanations - Yannic Kilcher
AdvancedFollow these courses in order to complete the learning path. Click on any course to enroll.
Graduate-level ML course. Supervised learning, unsupervised learning, deep learning, reinforcement learning theory.
Rigorous ML theory and implementation. Linear models, neural networks, deep learning, reinforcement learning.
Linear algebra foundations for ML/AI. Matrix decompositions, optimization, randomized algorithms.
Learn Transformers, explained: Understand the model behind GPT, BERT, and T5
Build Stable Diffusion from scratch, understand diffusion models, transformers, advanced PyTorch.
State-of-the-Art Machine Learning Papers Implementation
Complete natural language processing specialization covering transformers, attention mechanisms, and modern NLP techniques.
This course teaches how to harness the power of AI to enhance user research and design methodologies. Participants will explore AI tools and techniques that can streamline data gathering, analysis, and interpretation in user research and enhance ideation and prototyping.
A self-study course for professionals to integrate AI technologies into fraud investigation practices.
This webinar explains how facial recognition systems work, their benefits and risks, and common commercial applications. It also discusses social, ethical, and regulatory considerations for the use of these systems in research, including potential harms and privacy considerations.
A project-based course where you'll apply EDA techniques to a real-world dataset of UN voting records, using R packages like dplyr and ggplot2.
This talk covers the basics of data governance, including the people, processes, and tools needed to automate data quality at scale. It addresses how to define data domains, organize data architecture, create data QA, and build more transparency into algorithms.
A specialized course that explores how artificial intelligence can enhance UX design processes. It covers practical tools, ethical considerations, and strategies to create inclusive and effective AI-driven designs.
Deep dives into the latest machine learning research papers. Understand cutting-edge AI research with clear explanations.
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See the side-by-side breakdown and our pick by scenario
See the side-by-side breakdown and our pick by scenario
Dive deep into AI foundations. Study the mathematical theory, read seminal papers, and understand the principles behind modern machine learning.
You'll work through Master cutting-edge techniques and research; Handle complex, production-scale systems; Contribute to open source and research; Build and train machine learning models from scratch; Understand supervised, unsupervised, and reinforcement learning paradigms; Evaluate model performance using industry-standard metrics.
About 250 hours of study across 15 courses — and you can go at your own pace.
It's pitched at advanced level, so a little prior familiarity helps.
15 curated courses, sequenced from foundational to advanced.
Around $115 total if you buy every course — but many include free audit options.
Enroll in this path to track your progress and stay motivated.