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MOOC
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
- Course related:
- AAE5103 Artificial Intelligence in Aviation Industry
- Subjects:
- Computing
- Keywords:
- Machine learning Artificial intelligence
- Resource Type:
- MOOC
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MOOC
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.
This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:
- The major steps involved in tackling a data science problem.
- The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!
- Course related:
- LGT6801 Guided Study in Logistics I, LGT6202: Stochastic Models and Decision under Uncertainty, LGT6802 Guided Study in Logistics II, and LGT6803: Guided Study in Logistics III
- Subjects:
- Business Information Technology and Computing
- Keywords:
- Electronic data processing Data mining Problem solving
- Resource Type:
- MOOC
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MOOC
Learn the core ideas in machine learning, and build your first models.
- Course related:
- ENG2002 Computer Programming
- Subjects:
- Computing
- Keywords:
- Machine learning
- Resource Type:
- MOOC
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MOOC
本系列課程從零開始，教授一般認為最適合初學者的程式語言「Python」，目標是讓大家在完成本課程之後，一方面獲得程式設計與運算思維的基本概念，一方面也能獨立寫出能解決運算問題的程式。本課程和一般程式設計課程最不同的地方，在於它是以解決商管領域的運算問題為導向，因此課程不會只含有質因數分解、紅球白球排列組合、三角不等式、萬年曆、數字排序等傳統程式設計課程的範例與作業，而是包含了生產、物流、存貨、投資、定價等問題，讓大家在學會程式設計的同時，也直接體會程式設計與資訊技術在商管領域的各種應用。 本系列課程共分為三門課程。本門課程做為第一門課程，將介紹程式設計的基本觀念、Python 語言的基本語法、選擇、迴圈、清單，並以作業管理領域的一些簡單演算法作結。
- Course related:
- MM2425 Introduction to Business Analytics
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- MOOC
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MOOC
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- MOOC
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MOOC
In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.
- Subjects:
- Computing
- Keywords:
- Artificial intelligence
- Resource Type:
- MOOC
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MOOC
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
- Subjects:
- Computing
- Keywords:
- Artificial intelligence
- Resource Type:
- MOOC
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MOOC
This course is designed to teach you the foundations in order to write simple programs in Python using the most common structures. No previous exposure to programming is needed. By the end of this course, you'll understand the benefits of programming in IT roles; be able to write simple programs using Python; figure out how the building blocks of programming fit together; and combine all of this knowledge to solve a complex programming problem. We'll start off by diving into the basics of writing a computer program. Along the way, you’ll get hands-on experience with programming concepts through interactive exercises and real-world examples. You’ll quickly start to see how computers can perform a multitude of tasks — you just have to write code that tells them what to do.
- Course related:
- COMP1001 Problem Solving Methodology in Information Technology
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- MOOC
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MOOC
Those who work in modern language service industry are required to be capable of using computers and Internet to aid their translation job by adapting a variety of efficient tools, rather than just using word processor tools and several basic computer-aided translation software. This course teaches the basic concepts of computer-aided translation technology, helps students learn to use a variety of computer-aided translation tools, enhances their ability to engage in various kinds of language service in such a technical environment, and helps them understand what the modern language service industry looks like. This course covers introduction to modern language services industry, basic principles and concepts of translation technology, information technology used in the process of language translation, how to use electronic dictionaries, Internet resources and corpus tools, practice of different computer-aided translation tools, translation quality assessment, basic concepts of machine translation, globalization, localization and so on. As a compulsory course for students majoring in Translation and Interpreting, this course is also suitable for students with or without language major background. By learning this course, students can better understand modern language service industry and their work efficiency will be improved for them to better deliver translation service. The course is one of the PKU-DeTao MOOCs, which is a joint effort by Peking University and DeTao Masters Academy.
- Subjects:
- Translating and Interpreting and Computing
- Keywords:
- Machine translating Translating interpreting
- Resource Type:
- MOOC
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MOOC
New to web design? Start here first. Instructor James Williamson introduces the fundamental concepts, tools, and learning paths for web design. He explains what it means to be a web designer, the various areas of specialization, and whether web design is the right hobby or career for you. Along the way, he talks to five prominent designers and developers, who have each found success in a different corner of the web. If you want to get up and running fast, check out the chapter on getting online, choosing a domain name and web host, and getting around the backend of a standard website. Need to stock your tool chest? Learn what you'll need to build a brand new site, from web design software and content management systems, to testing and prototyping tools and development frameworks. Finally, James outlines learning paths for where to go next, touching on subjects such as standards and accessibility, responsive design, and the three core web technologies: HTML, CSS, and JavaScript.
- Subjects:
- Computing
- Keywords:
- Web sites -- Design
- Resource Type:
- MOOC
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MOOC
In this course, you'll learn the fundamentals of the Python programming language, along with programming best practices. You’ll learn to represent and store data using Python data types and variables, and use conditionals and loops to control the flow of your programs. You’ll harness the power of complex data structures like lists, sets, dictionaries, and tuples to store collections of related data. You’ll define and document your own custom functions, write scripts, and handle errors. Lastly, you’ll learn to find and use modules in the Python Standard Library and other third-party libraries.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- MOOC
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MOOC
After completed this online course, you will be able to: (1) read the blockchain-related news easily; (2) explain 99% of blockchain vocabulary in one minute and (3) to publish the exclusive cryptocurrency on the blockchain within one click.
- Course related:
- APSS3225 Media and Society
- Subjects:
- Computing
- Keywords:
- Cryptocurrencies Blockchains (Databases)
- Resource Type:
- MOOC
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MOOC
Data science has critical applications across most industries, and is one of the most in-demand careers in computer science. Data scientists are the detectives of the big data era, responsible for unearthing valuable data insights through analysis of massive datasets. And just like a detective is responsible for finding clues, interpreting them, and ultimately arguing their case in court, the field of data science encompasses the entire data life cycle. That starts with capturing lots of raw data using data collection techniques, and then building and maintaining data pipelines and data warehouses that efficiently “clean” the data and make it accessible for analysis at scale. This data infrastructure allows data scientists to efficiently process datasets using data mining and data modeling skills, as well as analyze these outputs with sophisticated techniques like predictive analysis and qualitative analysis. Finally, these findings must be presented using data visualization and data reporting skills to help business decision makers. Depending on the size of the company, data scientists may be responsible for this entire data life cycle, or they might specialize in a particular portion of the life cycle as part of a larger data science team
- Subjects:
- Computing
- Keywords:
- Machine learning Data mining Big data
- Resource Type:
- MOOC
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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
- 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
- Keywords:
- Machine learning Neural networks (Computer science) Artificial intelligence
- Resource Type:
- MOOC
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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:
- EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and. Information Processing and COMP4434 Big Data Analytics
- Subjects:
- Computing
- 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
- Keywords:
- Computer programming Computer science
- Resource Type:
- MOOC
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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 and Mathematics and Statistics
- Keywords:
- Big data Data mining Machine learning Mathematical statistics -- Data processing
- Resource Type:
- MOOC
-
MOOC
Video games as a medium go back more than 50 years to mainframe computers. Even the central design of video games can be traced back to the first games themselves. To be a good game designer, it's essential to have an understanding of the video game design industry's fascinating history. We've partnered with The Strong National Museum of Play to give you a unique look into the history of all things video game. The International Center for the History of Electronic Games at The Strong is the largest and most comprehensive public assemblage of video games and related materials in the world. The staff are celebrated experts in the field and the ICHEG is visited by scholars of video games from around the world. You'll gain amazing insight into the history of video games with a guided exploration of key artifacts from the collection of more than 100,000 electronic games and materials.
- Subjects:
- Interactive and Digital Media and Computing
- Keywords:
- Video games -- Design History
- Resource Type:
- MOOC
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MOOC
Game designers work with a wide range of asset creators, programmers, producers, and others to bring a video game from concept to product. In this course, you will learn about the different types of teams a game designer is a member of, both large and small.
- Subjects:
- Interactive and Digital Media and Computing
- Keywords:
- Video games -- Design
- Resource Type:
- MOOC
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MOOC
Modern video games are incredibly complex multimedia productions involving still and motion graphics, code, audio, interface elements, narrative elements and much more. In this course, you will learn how and where all these pieces come from, who's in charge of each piece and the different stages of the game design process. We will also show you how everything is brought together to create a final product.
- Subjects:
- Interactive and Digital Media and Computing
- Keywords:
- Video games
- Resource Type:
- MOOC
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MOOC
Virtual reality is changing the way we interact with the world. But how does it work, what hardware is involved, and how is software written for it? In this course, part of the Virtual Reality Professional Certificate program, we will explore the foundations of user-friendly virtual reality app development for consumers, as well as enterprise solutions. Both hardware and software aspects will be discussed. You will learn to evaluate devices necessary for virtual reality applications, what their differences are, how you write interactive applications for virtual reality, and we will discuss the most frequent problems you are going to need to solve to write virtual reality software. In this course, you will explore the basics of virtual reality software through copying and modifying JavaScript to explore tradeoffs in VR application design. Extensive programming experience is not required. By the end of this course, you will understand what is important for successful virtual reality software and learn how to write simple virtual reality programs themselves with WebVR. This course is taught by an instructor with almost two decades of experience in virtual reality who leads the Immersive Visualization Laboratory at UC San Diego.
- Subjects:
- Interactive and Digital Media and Computing
- Keywords:
- Computer simulation Virtual reality Human-computer interaction
- Resource Type:
- MOOC
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MOOC
Learn about the integrative power of knowledge management, Big Data and Cloud Computing, and how they impact the new business era.
- Subjects:
- Business Information Technology, Management, Computing, and Industrial and Systems Engineering
- Keywords:
- Big data Knowledge management
- Resource Type:
- MOOC