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.

Artificial intelligence plays a role in nearly everyone's life now, so it only seems fair that everyone should also have the opportunity to learn exactly what it is and how it functions. At least, that's what Helsinki University in Finland thinks. The school is offering the world's first online artificial intelligence course geared towards beginners, as Engadget reports. Not only can anyone with web access enroll, but it's also free. Because the course only takes about 30 hours to complete, it's possible it might help people get to know--and form opinions on--artificial intelligence.

The team of specialists at the AI training center will work on developing a range of future capabilities to help increase police efficiency and efficacy, including software to enable vehicle, speech and critical event recognition. Initially, the team will focus on training the AI algorithm for automatic license plate recognition to aid law enforcement in automated data analysis. Today, without the purchase of expensive, specialized hardware, officers must go through the process of manually viewing license plates and physically typing them into a system to match against a database. "Officers spend, on average, two-thirds of each shift sitting behind a keyboard doing paperwork," says Moji Solgi, Axon's Director of AI and Machine Learning. "Our team is applying AI, machine learning and data science to common policing problems and eliminating pain points for agencies, including manual paperwork. The ultimate goal of the AI training center is to help accelerate the introduction of AI-powered capabilities for public safety."

Richard Lifton, filmmaker Laura Mulvey, artificial intelligence pioneer Judea Pearl, Jazz musician Willie Ruff, and Rowan Williams, the 104th Archbishop of Canterbury, joined Tyson and Bassett in receiving honorary degrees from the school this year.

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.

Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don't quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first project will give you the confidence to tackle any data science problem. This series of articles will walk through a complete machine learning solution with a real-world dataset to let you see how all the pieces come together. We'll follow the general machine learning workflow step-by-step: Along the way, we'll see how each step flows into the next and how to specifically implement each part in Python. The complete project is available on GitHub, with the first notebook here.

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.

One year it might be an automaton programmed to launch Nerf balls. Another year it could be a cyborg tossing a Frisbee. Or, as exemplified just months ago, it could be a robot zipping about a competition floor, picking up crates and stacking them atop a large scale in a matter of minutes. Such is the world of the First Robotics Competition – a seasonal initiative that encourages high school students all across the country to build and operate robots designed to perform a specific task. Ultimately, these robotics teams duke it out in regional and national competitions held each academic year.

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.