기계 학습 강좌는 대규모 데이터를 활용하고 학습할 수 있는 시스템 만들기에 중점을 두고 있습니다. 연구 주제는 예측적 알고리즘, 자연 언어 처리 및 통계 패턴 인식을 포함하고 있습니다....
AI is not only for engineers. This non-technical course will help you apply AI in your own organization.
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Machine learning is a branch of artificial intelligence that seeks to build computer systems that can learn from data without human intervention. These powerful techniques rely on the creation of sophisticated analytical models that are “trained” to recognize patterns within a specific dataset before being unleashed to apply these patterns to more and more data, steadily improving performance without further guidance.
For example, machine learning is making increasingly accurate image recognition algorithms possible. Human programmers provide a relatively small set of images that are labeled as “cars” or “not cars,” for instance, and then expose the algorithms to vastly larger numbers of images to learn from. While the iterative algorithms typically used in machine learning aren’t new, the power of today’s computing systems have enabled this method of data analysis to become more effective more rapidly than ever.
Machine learning is in some ways a hybrid field, existing at the intersection of computer science, data science, and algorithms and mathematical theory. On the computer science side, machine learning engineers and other professionals in this field typically need strong software engineering skills, from fundamentals like confident programming and coding ability to big picture familiarity with system design principles.
A familiarity with data science concepts is also important, particularly skills in data modeling and evaluation to ensure that the algorithms perform well and become more, not less accurate over time. And, because machine learning relies heavily on algorithms as well as the statistics and probability principles that underlie them, a solid theoretical background in mathematics can also be invaluable.
Machine learning skills can open the door to a wide range of careers, as more and more companies seek to harness these techniques and artificial intelligence (AI) to automate a growing range of processes. Some companies may specifically hire for machine learning engineers, but machine learning skills can also be important for data scientists, data analysts, and data engineers.
There are more specialized roles available for machine learning experts, too. Many companies in the financial industry may employ business intelligence analysts and decision scientists who can leverage machine learning skills to automate systems for delivering market insights. And companies building Internet of Things (IoT) that rely on voice recognition or other human inputs may employ natural language processing engineers or human-centered machine learning designers.
Like other topics in computer science, learners have plenty of options to build their machine learning skills through online courses. Coursera offers professional certificates, MasterTrack certificates, Specializations, and courses in machine learning from top universities like Stanford University, University of Washington, and companies like Google, IBM, and Deeplearning.ai. Popular courses include machine learning foundations, advanced machine learning, applied data science, convolutional neural networks, deep learning, statistics, machine learning, and more.
If you want to develop your machine learning skills in the context of a degree program, you can do that online too! Coursera currently offers computer science and data science degrees from top-ranked colleges like University of Illinois, Imperial College London, University of Michigan, University of Colorado Boulder, and University of Pennsylvania, all of which offer opportunities to learn about machine learning at top-ranked universities from anywhere in the world.