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Through MIT OpenCourseWare, MITx, and MIT xPRO learn about machine learning, computational thinking, deepfakes, and more.

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With the rise of artificial intelligence, the job landscape is changing — rapidly. MIT Open Learning offers online courses and resources straight from the MIT classroom that are designed to empower learners and professionals across industries with the competencies essential for succeeding in an increasingly AI-powered world.

Elevate your skills, unlock new opportunities, and advance your career with the following courses and materials available through MIT OpenCourseWare, MITx, and MIT xPRO — all part of MIT Open Learning.

Free MIT courses and resources

AI 101: Get an introduction to artificial intelligence that’s designed for those with little to no background in the subject.
Artificial Intelligence: Examine the power of AI with MIT’s foundational course about the basic knowledge representation, problem solving, and learning methods of artificial intelligence.
Introduction to Algorithms: Explore mathematical modeling of computational problems, common algorithms, algorithmic paradigms, and data structures used to solve these problems.
Introduction to Computational Thinking and Data Science: Learn how to use computation to understand real-world phenomena.
Introduction to Machine Learning: Get to know the principles, algorithms, and applications of machine learning from the point of view of modeling and prediction.
Machine Learning with Python: From Linear Models to Deep Learning: Get an in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects.
Machine Vision: Understand the process of generating a symbolic description of the environment from an image, by exploring the physics of image formation, image analysis, binary image processing, and filtering.
Matrix Calculus for Machine Learning and Beyond: Learn a coherent approach to matrix calculus showing techniques that allow you to think of a matrix holistically — not just as an array of scalars — generalize and compute derivatives of important matrix factorizations, and understand how differentiation formulas must be reimagined in large-scale computing.
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning: Review linear algebra with applications to probability and statistics and optimization, and get a full explanation of deep learning.
Generative Artificial Intelligence in K-12 Education: Get an introduction to the foundations of generative AI technology and the new opportunities it enables for K-12 education.
Media Literacy in the Age of Deepfakes: Gain critical skills to better understand the past and contemporary threat of misinformation.
Sorting Truth from Fiction: Civic Online Reasoning: Master quick and effective practices for evaluating online information that you can bring back to your classroom.
Ethics of Technology: Discover the tools of philosophical ethics through application to contemporary issues concerning technology, including topics such as privacy and surveillance, algorithmic bias, the promise and peril of artificial intelligence, automation and the future of work, and threats to democracy in the digital age.
Social and Ethical Responsibilities of Computing: AI and Algorithms: Learn how to practice responsible technology development through insights and methods from the humanities and social sciences, including an emphasis on social responsibility.
Exploring Fairness In Machine Learning For International Development: Discover how and why machine learning is used in international development, explore the ethical challenges of machine learning bias and fairness in this context, and consider guiding principles for its use.
Understanding the World through Data: Become a data explorer by learning how to leverage data and basic machine learning algorithms to understand the world.

Paid MIT courses for working professionals

Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI: Demystify machine learning through computational engineering principles and applications in this two-course program specifically designed for engineers, scientists, and researchers.
Course 1: Machine Learning, Modeling, and Simulation Principles
Course 2: Applying Machine Learning to Engineering and Science
Deep Learning: Mastering Neural Networks: Explore the core mathematical and conceptual ideas underlying deep neural networks; experiment with deep learning models and algorithms using available machine learning toolkits; and examine application approaches and case studies where deep learning is being used.
Designing and Building AI Products and Services: Learn the four stages of AI product design; identify applicable AI technologies to improve organizational processes; and analyze technical and operational requirements to build AI models.
Artificial Intelligence in Healthcare: Fundamentals and Applications: Discover the AI design process model through its various stages; understand different machine learning algorithms and how they can be applied in varying scenarios; and examine neural network NLP algorithms and their widespread application.
AI for Senior Executives: Strategically harness AI tools to improve efficiencies, cut costs, provide customer insights, and generate new product ideas; develop a strong foundation in generative AI; and understand the benefits, challenges, and ethical considerations of implementing generative AI and prompt engineering in an organization.

MIT OpenCourseWare offers free, online, open educational resources from more than 2,500 courses that span the MIT undergraduate and graduate curriculum. MITx offers hundreds of high-quality massive open online courses adapted from the MIT classroom for learners worldwide. MIT xPRO offers paid courses designed using cutting-edge research in the neuroscience of learning; its online learning programs leverage vetted content from world-renowned experts to make learning accessible anytime, anywhere.

Explore the world of artificial intelligence with online courses from MIT was originally published in MIT Open Learning on Medium, where people are continuing the conversation by highlighting and responding to this story.

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