This course will take you on a step-by-step guide that will take you from the basic syntax and concepts of JavaScript, all the way through the more advanced concepts and usage that are covered in technical interviews. It includes the latest advancements in generative AI, offering invaluable insights and tools for developers.
JavaScript started as a basic language for performing simple computations on web pages, but it’s now become one of the most popular programming languages. It is a tremendously powerful programming language that can be used for any kind of web development, but also for many other types of development, such as data science, mobile app dev, desktop app dev, and much more. It is a clean, readable programming language that is used everywhere and gaining proficiency in JavaScript makes any developer much more valuable to prospective employers these days.
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Pearson is in Tier 4: commercial training companies and platform-made courses of our institution ranking (60/100).
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
A curated collection of videos featuring top experts in artificial intelligence.
-Learn more and enroll in the program: https://online.stanford.edu/programs/generative-ai-technology-business-and-society-program Recent advancements in generative AI are reshaping industries, pushing technological boundaries, and revolutionizing creative processes. In this fast-changing landscape…
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Delve into the exciting world of Artificial Intelligence with insights and research from the world's top experts. This playlist offers a comprehensive journey, from foundational machine learning con…
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabili…
