- 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. 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
- CS50’s Introduction to Artificial Intelligence with Python - Learn to use machine learning in Python in this introductory course on artificial intelligence. CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
- CS224N: Natural Language Processing with Deep Learning - Investigate the fundamental concepts and ideas in natural language processing (NLP), and gain a thorough introduction to cutting-edge neural networks for NLP. You will develop an in-depth understanding of both the algorithms available for processing linguistic information and the underlying computational properties of natural languages. The focus is on deep learning approaches: implementing, training, debugging, and extending neural network models for a variety of language understanding tasks. You will progress from word-level and syntactic processing to coreference, question answering and machine translation. For your final project, you will apply a complex neural network model to a large-scale NLP problem.
- Data Science: Machine Learning - Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
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- CS230: Deep Learning - 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.
- Fundamentals of TinyML - The first course in the TinyML Certificate series, Fundamentals of TinyML will focus on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Throughout the course, you will learn data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models. At the end of this course, you will be able to understand the “language” behind TinyML and be ready to dive into the application of TinyML in future courses.
- Applications of TinyML - Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.
- Deploying TinyML - Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.
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9. 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.
10. CS 330: Deep Multi-Task and Meta Learning - While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively.
11. CS234: Reinforcement Learning - 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. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning — an extremely promising new area that combines deep learning techniques with reinforcement learning.
Elyse Y. Robinson is the Founder of Switch Into Tech where she does monthly seminars, posts weekly freebies to help you switch into tech, Writer of Nube: Switch Into A Cloud Career, podcaster for Nobody Wants To Work Tho, creator of FullTuitionScholarships.org to help you not go into college debt, and in school for Data Science. Elyse is in love with Mexico, researching any and everything, and helping people switch into tech.