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MOOC
The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.
- Course related:
- COMP4431 Artificial Intelligence
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC
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MOOC
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, 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. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI.
- Course related:
- AMA564 Deep Learning
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Neural networks (Computer science) Machine learning
- Resource Type:
- MOOC
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Courseware
In this course, you will walk away with an up-to-date examination of the maturing FinTech industry and an understanding of the technologies set to shape the future of finance. Insight into who is currently adopting and driving financial technology innovation and the potential for partnerships between incumbents, start-ups and investors. The ability to critically assess the future of the financial services industry, through exploring complex real-world problems and how FinTech can be used to find solutions.A strategic framework to apply within your own role, and the opportunity to share this with like-minded professionals at an additional conference week.
- Course related:
- COMP4142 E-Payment and Cryptocurrency and COMP5521 Distributed Ledger Technology
- Subjects:
- Computing, Data Science and Artificial Intelligence and Finance
- Keywords:
- Financial services industry -- Technological innovations Finance -- Technological innovations
- Resource Type:
- Courseware
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MOOC
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
- Course related:
- COMP4434 Big Data Analytics and EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and. Information Processing
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC
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MOOC
This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course.
- Course related:
- COMP1011 Programming Fundamentals
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Computer programming Computer science
- Resource Type:
- MOOC
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Others
freeCodeCamp is a proven path to your first software developer job. More than 40,000 people have gotten developer jobs after completing this – including at big companies like Google and Microsoft. If you are new to programming, we recommend you start at the beginning and earn these certifications in order. To earn each certification, build its 5 required projects and get all their tests to pass.You can add these certifications to your résumé or LinkedIn. But more important than the certifications is the practice you get along the way.If you feel overwhelmed, that is normal. Programming is hard. Practice is the key. Practice, practice, practice. And this curriculum will give you thousands of hours of hands-on programming practice. And if you want to learn more math and computer science theory, we also have thousands of hours of video courses on freeCodeCamp's YouTube channel. If you want to get a developer job or freelance clients, programming skills will be just part of the puzzle. You also need to build your personal network and your reputation as a developer. You can do this on Twitter and GitHub, and also on the freeCodeCamp forum. Happy coding.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Programming languages (Electronic computers) Computer programming Coding theory
- Resource Type:
- Others
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Others
Symbolab is an advanced math education tool. It allows users to learn, practice and discover math topics using mathematical symbols and scientific notations as well as text. Symbolab provides automated step by step solutions to algebraic, trigonometric and calculus topics covering from middle school through college. Symbolab offers a wealth of smart calculators including: equations, simultaneous equations, inequalities, integrals, derivatives, limits , tangent line, trigonometric equations, functions and more. The stated goal of the site is to make scientific content universally accessible by expanding the searchable data space onto scientific notations, expressions, equations and formulas. This is done by applying proprietary machine learning algorithms in order to understand the meaning and context of the queries. Symbolab, making math simpler.
- Course related:
- AMA1007 Calculus and Linear Algebra
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Matrices Mathematics -- Computer programs Matrices -- Computer programs
- Resource Type:
- Others
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Video
We are on a mission to provide free and subsidized education. We believe that the real world exists beyond the walls of costly institutions. We are here to help you in all the possible ways. A small step to educate the world. Join us and be a part of our creative community. "Every individual is different and talented, what you need are passion and curiosity."
- Course related:
- EE3001A Analogue and Digital Circuits, EE2003A Electronics, EE3008A Linear Systems and Signal Processing, and EE4004A Power Systems
- Subjects:
- Computing, Data Science and Artificial Intelligence, Electrical Engineering, and Electronic and Information Engineering
- Keywords:
- Engineering Engineering -- Data processing Computer science
- Resource Type:
- Video
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MOOC
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).
- Course related:
- LGT6801 Guided Study in Logistics I
- Subjects:
- Computing, Data Science and Artificial Intelligence and Mathematics and Statistics
- Keywords:
- Big data Data mining Mathematical statistics -- Data processing Machine learning
- Resource Type:
- MOOC
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Others
The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.We believe this is best done together with the community and powered by automation.
- Course related:
- COMP5212 Software Design and Architecture, COMP5222 Software Testing and Quality Assurance, COMP5121 Data Mining and Data Warehousing Applications, COMP5123 Intelligent Information Systems, and COMP5131 Introduction to Information Systems
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- Others