On January 16, a new course launched on the online learning platform Coursera with an unassuming name: The Google IT Support Professional Certificate. It promised to prepare beginners for entry-level jobs in IT in eight to 12 months. That day, it attracted the largest-ever group of first-time Coursera users, almost half of them people without college degrees. More than 18,000 people have enrolled in the $49-a-month program so far, 160 of whom have completed it. "Even as we were building it, even as it was about to launch, I never anticipated the success of it," says Natalie Van Kleef Conley, Google's product lead for the program.

Training and education of the workforce is key to the digital transformation success of many businesses. One tool that has helped foster this is e-learning, especially, as Digital Journal has reported, e-learning is leading the way as businesses shift their training priorities to embrace a digital-first approach. There are many different forms of e-learning, involving a mix of different channels, content, use of video and so on. A shared objective is the importance of flexibility and a blended approach. Advantages to businesses include lower costs, since one training session can be delivered to many people.

Every year a large amount of data is generated which needs to be stored and analyzed. Apache Spark allows you to process such big data. The real power and value proposition of Apache Spark is its speed and platform to execute data science tasks. Spark's unique use case is that it combines ETL, batch analytic, real-time stream analysis, machine learning, graph processing, and visualizations to allow data scientists to tackle the complexities that come with raw unstructured data sets. Spark embraces this approach and has the vision to make the transition from working on a single machine to working on a cluster, something that makes data science tasks a lot more agile.

Python is gaining an ever larger footprint in the interesting world of Data Science. KDnuggets says that Python R is even over as leader in the AI and Machine Learning standings platforms. But what makes Python so special for data science? One of the reasons is that Python is a general-purpose programming language. This means that Python has no specific purpose, which makes it possible to use models directly in a broader context.

Matrix calculations, derivatives, eigenvalues, Set Theory, functions, vectors, linear transformations, etc. are extremely important to understand the theory behind statistical methods and programming. Therefore, before starting your next MOOC or Machine Learning book it's crucial to review all those concepts again. Most schools request students to be proficient at these methods in order to graduate, but the silver lining is that it won't require too much of your time to refresh or obtain this knowledge. There are plenty of resources to start, but what worked for me was The Manga Guide to Linear Algebra, which is very simple, graphic and provides a great foundation prior getting into more complex stuff. My suggestion is to schedule some weeks to review these concepts and to use the Feynman Technique to be able to explain in simple terms each of these topics. One of the issues people face today when trying to get into a field such as Data Science is Information Overload, a term used when talking in relation to the effect of having too many resources at the disposal.

After taking these robotics classes you can also get a robotics certification online. However, you can get robotics degree online from a lot of places other than Udemy like coursera, EDx, Futurelearn and so on. Open career opportunities and have fun to learn electronics focused on building robots/automation! Open doors to careers and hobbies and have fun while learning digital electronics! Description: An autonomous light-seeking an obstacle avoiding robot for Arduino Makers that want to learn the hard way.

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list. We decided to share this resource with you, and so here are Udemy's top selling courses.

We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed of several {\em steps}, in which it chooses an action and observes a feedback signal. Moreover, in each step, it can take a special action, called the $stop$ action, that ends the current episode. After the $stop$ action is taken, the learner collects a terminal reward, and observes the costs and terminal rewards associated with each step of the episode. The goal of the learner is to maximize its cumulative gain (i.e., the terminal reward minus costs) over all episodes by learning to choose the best sequence of actions based on the feedback. First, we define an {\em oracle} benchmark, which sequentially selects the actions that maximize the expected immediate gain. Then, we propose our online learning algorithm, named {\em FeedBack Adaptive Learning} (FeedBAL), and prove that its regret with respect to the benchmark is bounded with high probability and increases logarithmically in expectation. Moreover, the regret only has polynomial dependence on the number of steps, actions and states. eMAB can be used to model applications that involve humans in the loop, ranging from personalized medical screening to personalized web-based education, where sequences of actions are taken in each episode, and optimal behavior requires adapting the chosen actions based on the feedback.

If you love Andrew Ng's first Coursera course on machine learning as much as I do, you were equally hyped when you heard that deeplearning.ai Since everybody's on a tight schedule, let's try the impossible and finish a course that is laid out to last one month in one week. Let's not rush through though, but actually understand the material. And of course, we'll do it while continuing our 40h/week job. What are the advantages of finishing the course quickly you ask?

"Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed" -- Arthur Samuel, 1959. Machine learning and artificial intelligence have been a rising field of research in both the corporate and the academic world. Machine learning proves to be incredibly powerful when it comes to making predictions or calculated suggestions that are based on large amounts of data. If an individual wants to master machine learning, how do you start and from where? In order to learn about Machine Learning, one not only needs a keen interest in it but also have the right resources.