In the rapidly expanding universe of artificial intelligence and machine learning, there's a lot to learn and to stay on top of. Use the range of courses in this learning path to augment your skills related to AI, ML, and data science by understanding generative models and exploring the fields of MLOps and Responsible AI.
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Gain hands-on training with generative models.
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Refine your skills in deep learning and neural networks.
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Explore the developing fields of MLOps and responsible AI.
Courses
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1
Applied Machine Learning: Ensemble Learning2h 25mApplied Machine Learning: Ensemble Learning
By: Derek Jedamski
Explore how to make powerful, accurate predictions with ensemble learners, one of the most common classes of machine learning algorithms.
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2
Deep Learning: Model Optimization and Tuning54mDeep Learning: Model Optimization and Tuning
By: Kumaran Ponnambalam
Learn about various optimization and tuning options available for deep learning models and use them to improve models.
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3
Reinforcement Learning Foundations44mReinforcement Learning Foundations
By: Khaulat Abdulhakeem
Learn the basics of reinforcement learning (RL), including the terminology, the kinds of problems you can solve with RL, and the different methods for solving those problems.
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4
Introduction to Attention-Based Neural Networks2h 11mIntroduction to Attention-Based Neural Networks
By: Janani Ravi
Learn what attention-based models are, how they work, and what they can do for recurrent neural networks.
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5
Training Neural Networks in Python2h 7mTraining Neural Networks in Python
By: Eduardo Corpeño
Take a deep dive into the inner workings of neural networks by learning how to create one from scratch in Python.
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6
Introduction to Generative Adversarial Networks (GANs)29mIntroduction to Generative Adversarial Networks (GANs)
By: Martin Kemka
Gain a better understanding of Generative Adversarial Networks (GANs), how they are created, how they train, and how they are able to create new media.
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7
AI Workshop: Hands-on with GANs Using Dense Neural Networks1h 24mAI Workshop: Hands-on with GANs Using Dense Neural Networks
By: Janani Ravi
Learn how to build and train generative adversarial networks (GANs) using dense neural networks in this interactive, workshop-style coding course.
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8
AI Workshop: Hands-on with GANs with Deep Convolutional Networks1h 36mAI Workshop: Hands-on with GANs with Deep Convolutional Networks
By: Janani Ravi
Learn how to build and train deep convolutional generative adversarial networks (GANs) in this interactive, workshop-style coding course.
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9
Exploring AIOps18mExploring AIOps
By: Morten Rand-Hendriksen
Discover how the exciting new world of AIOps is changing everyday IT workflows and practices around the world.
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10
MLOps Essentials: Model Development and Integration1h 30mMLOps Essentials: Model Development and Integration
By: Kumaran Ponnambalam
Get started with MLOps Concepts for Model Development and Integration, to organize machine learning (ML) development and deliver scalable and reliable ML products.
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11
MLOps Essentials: Model Deployment and Monitoring1h 23mMLOps Essentials: Model Deployment and Monitoring
By: Kumaran Ponnambalam
Learn how to deploy and monitor machine learning models to deliver scalable, reliable ML products and services.
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12
MLOps Essentials: Monitoring Model Drift and Bias1h 5mMLOps Essentials: Monitoring Model Drift and Bias
By: Kumaran Ponnambalam
Learn about the growing field of MLOps and the modeling techniques used to monitor model drift and bias.
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13
UX for AI: Design Practices for AI Developers59mUX for AI: Design Practices for AI Developers
By: John Maeda
Discover new approaches to building better UX for AI applications using the Microsoft Copilot stack.
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14
Foundations of Responsible AI2h 30mFoundations of Responsible AI
By: Ayodele Odubela
Learn about the practices needed to perform fairness testing and implement responsible AI systems.
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15
Responsible AI: Principles and Practical Applications1h 6mResponsible AI: Principles and Practical Applications
By: Jill Finlayson
Learn how AI is being used today and how to ensure its responsible usage into the future.
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16
Introduction to AI Governance59mIntroduction to AI Governance
By: Vidhi Chugh
Explore core concepts and practical strategies to effectively implement and manage AI governance.
Instructors
Derek Jedamski
Senior Manager, Copilot Prompt Engineering @ GitHub
Kumaran Ponnambalam
AI / ML Leader & Author
Khaulat Abdulhakeem
education • career • lifestyle
Janani Ravi
Co-Founder at Loonycorn
Eduardo Corpeño
Electrical & Computer Engineer, Creator of the world-renowned Brainfuino platform.
Martin Kemka
Morten Rand-Hendriksen
Tech and society, explained.
John Maeda
VP AI & Design, MSFT / LinkedIn Top US Influencers / Author, How To Speak Machine: Gentle Intro to AI+ML
Ayodele Odubela
Jill Finlayson
Advocate for women in tech, impact-focused innovation, equitable workplaces. Strategic advisor for startups, biz, gov.
Brandie Nonnecke
Host, TecHype; Founding Director, CITRIS Policy Lab; Assoc. Research Professor, Goldman School of Public Policy, UC Berkeley
Tsu-Jae Liu
Dean and Roy W. Carlson Professor of Engineering
Vidhi Chugh
Head of Product - AI, Futurist, AI Strategist | World's Top 200 Business and Technology Innovators | LinkedIn Top Voice in AI | Inventor | Author