Robot Framework is an open-source Test Automation framework. It was initially developed by Nokia Networks, however, it is now maintained by the Robot Framework Foundation. You will learn about the features, pros, and cons of the Framework along with instructions to install the needed software. Robot Framework is a Test Automation tool in which the test cases are written using keywords that makes it easy to learn and use. These keywords are written in a tabular form. With Robot Framework, the Test Scripts are replaced by a few keywords thereby replacing the need for large pieces of code.

Here is a list of awesome courses available on YouTube that you should definitely follow and are 100% free.

This tutorial will focus on two main broad topics that are Simple Linear Regression and Multiple Linear regression. Throughout the tutorial, key points are illustrated with clear, step-by-step examples for better understanding. By the end of the tutorial, you will be able to compute all of the essential outputs for simple linear regression and multiple regression. Most important, you will be able to correctly interpret the outputs you produce. Linear regression is a common Statistical Data Analysis technique that is widely being used.

Highlights: Welcome back to the all-new series on Machine Learning. In the previous post, we gave you a sneak peak into the basics of Machine Learning, the two types of Machine Learning, viz., Supervised & Unsupervised, and implemented some examples using various algorithms in each of the techniques. In this new tutorial post, we will explore one of the most widely used Supervised Learning algorithms in the world today – Linear Regression. We will start off with some theory and go on to build a simple model in Python, from scratch. In our previous post (also the first post of this Machine Learning tutorial series), we brushed the fundamentals of Linear Regression using the example of housing price prediction, given the size of the house. If you remember, the prediction was based on the linear relationship that existed between the house price and the size of the house. Have a look at the image below. In the graph above, the size of the house is shown along the horizontal axis and the price of a house is shown along the vertical axis. Here, each data point is a house with its respective size and the price that the house was recently sold for.

If you are a student or professional interested in the latest trends in the computing world, you would have heard of terms like artificial intelligence, data science, machine learning, deep learning, etc. The first article in this series on artificial intelligence explains these terms, and sets the platform for a simple tutorial that will help beginners get started with AI. Today it is absolutely necessary for any student or professional in the field of computer science to learn at least the basics of AI, data science, machine learning and deep learning. However, where does one begin to do so? To answer this question, I have gone through a number of textbooks and tutorials that teach AI. Some start at a theoretical level (a lot of maths), some teach you AI in a language-agnostic way (they don't care whether you know C, C, Java, Python, or some other programming language), and yet others assume you are an expert in linear algebra, probability, statistics, etc. In my opinion, all of them are useful to a great extent. But the important question remains -- where should an absolute beginner interested in AI begin his or her journey? Frankly, there are many fine ways to begin your AI journey.

Have you ever wondered how the first couple of years of a general Computer Science university course looks like? Maybe you'd like to know if this is the discipline for you and if you are good at it? Wonder no longer, for we will show you how to get there, how to get good as you go along on your journey, and how life as a computer scientist is like! Money is precious, time even more so, so our priority is to push you ahead, and give you a head-start into university life. Our goal with this course to help you decide if you want to do computer science & engineering at a university level, and what you can expect should you enroll into a course!

Artificial intelligence is changing the paradigm for many industries, and materials-focused commerce is no exception, where tremendous opportunities lie ahead. With the success of effective and generalizable deep learning tools, the materials industry is primed to take advantage of unprecedented breakthroughs, leveraging materials modeling, analysis, and design toward a more efficient, less costly, and more versatile response to market demands and opportunities, through materiomics. With data available from autonomous experimentation, large databases like the Materials Project within the Materials Genome initiative, or synthetic data, there exist many opportunities to accelerate and expand your materials design platform. Today, practicing engineers are expected to have both domain knowledge and a solid understanding of modern machine learning tools. This course will teach all the fundamentals necessary for you to reach the next milestone in practicing materiomics, by navigating the complex world of AI.

The goal of this level is to get you familiar with the ML universe. You will learn a bit of everything. The goal of this level is to get you introduced to the practical side of machine learning. What you learn at this level would really help you out there in the wild. This is the level where you would dive into different domains of Machine Learning.

Unsupervised machine learning algorithms analyze and cluster unlabeled datasets. Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There's an endless supply of industries and applications that machine learning can make more efficient and intelligent. You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups?

This is one of the most applied courses on Genetic Algorithms (GA), which presents an integrated framework to solve real-world optimization problems in the most simple way. For the first time, we have presented a practical course in the domain of metaheuristics algorithms required for students, researchers and practitioners. Firstly, we will introduce the basic theory of GA, then implement the simplest version of GA, namely Binary GA, into Matlab, and then present the continuous version, real GA, of it. Therefore, the main focus will be on the Genetic Algorithm as the most well-regarded optimization algorithm in the literature. In the following sections, we will introduce some well-known operation research problems, including transportation problems, hub location problems (HLP), quadratic assignment problems and travelling salesman problems (TSP) and try to solve them via GA.