Robot Framework is a generic test automation framework for acceptance testing and acceptance test-driven development (ATDD). It has easy-to-use tabular test data syntax and it utilizes the keyword-driven testing approach. Its testing capabilities can be extended by test libraries implemented either with Python or Java, and users can create new higher-level keywords from existing ones using the same syntax that is used for creating test cases. Robot Framework project is hosted on GitHub where you can find further documentation, source code, and issue tracker. Downloads are hosted at PyPI. The framework has a rich ecosystem around it consisting of various generic test libraries and tools that are developed as separate projects.

Programming is becoming more and more popular all around the world. Programming provides extreme amounts of power and flexibility, because you can take control of your computer's actual power, and you can develop your own systems and prototypes. Python is becoming especially popular, because it's extremely user-friendly whilst still maintaining a lot of the power that other, more complex, programming languages offer. Additionally, Python has a huge community, which means all sorts of modern projects, such as Big Data, Artificial Intelligence, Machine Learning, Deep Learning, etc. have been develop for the Python community to use, which makes implementing these sorts of advanced techniques extremely easy (sometimes only 1-2 lines of code). Employers love employees that can program, because they can go out and quickly produce results or create prototypes.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. The main purpose of this course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning and big data. He is the South East Asia s First XNA/DirectX MVP (Most Valuable Professional). He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Sri Lanka and India.

This course will guide you on what optimization is and what metaheuristics are. You will learn why we use metaheuristics in optimization problems as sometimes, when you have a complex problem you'd like to optimize, deterministic methods will not do; you will not be able to reach the best and optimal solution to your problem, therefore, metaheuristics should be used. This course covers information on metaheuristics and four widely used techniques which are Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies. By the end of this course, you will learn what Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies are, why they are used, how they work, and best of all, how to code them in Python! You will also learn how to handle constraints.

Given the constantly increasing amounts of data they're faced with, programmers have to come up with better solutions to make machines smarter and reduce manual work. In this Machine Learning course, you'll use Python to craft better solutions and process them effectively. We start by focusing on key ML algorithms and how they can be trained for classification and regression. We will also work with Supervised and Unsupervised learning to help to get to grips with both types of algorithm. We will use the highly popular Scikit-learn library throughout the course while performing various ML tasks.

If you're plugged into the tech industry, you'll know that two things have been making consistent waves in many areas over the past few years; machine learning and Python. What happens when you combine the new gold standard programming language with the most significant tech development in areas such as financial trading, online search, digital marketing and even data and personal security (among others)? This course will show you what's what, and get you started on becoming a machine learning guru. If you have a desire to learn machine learning concepts and have some previous programming or Python experience, this course is perfect for you. If you're more of a beginner than an intermediate, don't worry; each module starts with theory to explain upcoming concepts.

When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go. We saw AIs playing video games like Doom and Super Mario.

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you. You already learned about backpropagation, but there were a lot of unanswered questions.

We begin this journey through our favorite articles of the month by discovering and reading one of the most influential paper of natural language processing (NLP). Papers can be intimidating to read. So I liked the author's idea of combining screenshots from the original paper, explanations in plain English and code snippets with an actual implementation of the paper in Python. The article can be read at different levels: from the presentation of encoders, decoders and the attention function to real-world examples including the use of regularization and GPU training. One of the world-famous universities of California -- Berkleley -- launches its new data science program.

CATIA (Computer Aided Three-Dimensional Interactive Application) is a professional CAD / CAM-based software produced by the French company Dassault Systèmes. Especially the automotive sector, aircraft production and other simulation sectors that can respond to the needs of the program is used more often and every sector is appealing to cutting. Almost all automotive industry in the world is using computer aided design and manufacturing. Catia ensures that the products that are to be produced can be processed in the virtual environment during the production process. After a product is designed by the designer in the Catia program, the ergonomist explores the ergonomics of the design.