AI For Everyone
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take.
In this course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can–and cannot–do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.
ChatGPT Prompt Engineering for Developers
A free course on ChatGPT prompt engineering by DeepLearning AI and OpenAI.
Your Guide to Communicating with Artificial Intelligence. Learn how to use ChatGPT and other AI tools to accomplish your goals using our free and open source curriculum, designed for all skill levels!
Drive forward your understanding of Natural Language Processing (NLP), the basis of how large language models (LLMs) like GPT-4 work.
This lecture covers:
- Human language and word meaning (15 min)
- Word2vec algorithm introduction (15 min)
- Word2vec objective function gradients (25 min)
- Optimization basics (5min)
- Looking at word vectors (10 min or less)
Key learning: The (really surprising!) result that word meaning can be representing rather well by a large vector of real numbers.
This course will teach:
- The foundations of the effective modern methods for deep learning applied to NLP. Basics first, then key methods used in NLP: recurrent networks, attention, transformers, etc.
- A big picture understanding of human languages and the difficulties in understanding and producing them
- An understanding of an ability to build systems (in Pytorch) for some of the major problems in NLP. Word meaning, dependency parsing, machine translation, question answering.
Deep Learning is one of the most highly sought after skills in AI.
In this course, you will learn:
The foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more
To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare.
This class will provide a solid introduction to the field of reinforcement learning and students will learn about the core challenges and approaches, including generalization and exploration.
By the end of the class students should be able to:
- Define the key features of reinforcement learning that distinguishes it from AI and non-interactive machine learning (as assessed by the exam).
- Given an application problem (e.g. from computer vision, robotics, etc), decide if it should be formulated as a RL problem; if yes be able to define it formally (in terms of the state space, action space, dynamics and reward model), state what algorithm (from class) is best suited for addressing it and justify your answer (as assessed by the exam).
- Implement in code common RL algorithms (as assessed by the assignments).
- Describe (list and define) multiple criteria for analyzing RL algorithms and evaluate algorithms on these metrics: e.g. regret, sample complexity, computational complexity, empirical performance, convergence, etc (as assessed by assignments and the exam).
- Describe the exploration vs exploitation challenge and compare and contrast at least two approaches for addressing this challenge (in terms of performance, scalability, complexity of implementation, and theoretical guarantees) (as assessed by an assignment and the exam).
Harvard’s Data Science: Machine Learning
Harvard’s Fundamentals of TinyML
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
What you’ll learn
- Fundamentals of Machine Learning (ML)
- Fundamentals of Deep Learning
- How to gather data for ML
- How to train and deploy ML models
- Understanding embedded ML
- Responsible AI Design
Harvard’s Applications of TinyML
Harvard’s Deploying TinyML
Google ML Certification
Machine Learning Crash Course with TensorFlow APIs. Google’s fast-paced, practical introduction to machine learning, featuring a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.