As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for IT professionals and data-scientists. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language. This comprehensive 2-in-1 course is a comprehensive, practical guide to master the basics and learn from real-life applications of machine learning. Learn how to build and evaluate the performance of efficient models using scikit-learn. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

Kendal Parmar's son went from being a sporty and sociable boy who loved school, to a child who would stay in his room and rarely go outside. The change in his personality was down to a gaming disorder that crept up on him at the age of 12, when he started secondary school. Three years later, Joseph is still struggling with the problem. Parmar says the biggest sign that something was wrong was the amount of arguing that would occur when she asked him to stop playing video games. "Eventually his habits developed and he was gaming all the time. He became too terrified to go to school and he was terrified of people," she says.

Within buildings 10 and 30 of the Siemens complex on the outskirts of Munich, the next generation of German workers are toiling over a range of test projects. The assignments are carefully chosen to impart the skills needed to continue the German miracle in automated manufacturing. In one room, a group of young men train to be automotive mechatronic engineers. They've just spent the past week feverishly programming a diminutive working model of an automated production line--complete with sensors, conveyor belts, and tools that work without human input. They're able to discuss their work in surprisingly good English, but what sets them apart from their peers in the US is that none of them attend a university.

For all their stunning frequency, school shootings remain a confounding horror. Not only is there little consensus on how to stop them--with suggestions ranging from restricting gun access to arming teachers--but there's even less certainty about why a student would open fire on his classmates. Now, some scientists are starting to explore if artificial intelligence (AI) could help find answers. The idea is that algorithms might be able to better analyze data related to school shootings, and perhaps even identify patterns in student language or behavior that could foreshadow school violence. The research is still in its early stages, and the prospect of using machines to predict who might become a school shooter raises privacy issues and other ethical questions associated with any kind of profiling, particularly since the process would involve children.

Countries in the Common Market for Eastern and Southern Africa (COMESA) are lagging behind with respect to robotics, artificial intelligence and technology infrastructure and skills acquisition. Jean Baptiste Mutabazi, the regional bloc's Director of Infrastructure, noted this during the COMESA Connect Industry Dialogue in Kigali themed "Smart Technologies for Sustainable Businesses." Held in Kigali, the meeting was organised by the COMESA Business Council and Rwanda's Private Sector Federation of Rwanda (PSF). While Egypt, Seychelles, Kenya and Mauritius lead in terms of internet penetration and mobile density and in trade in ICT services, Mutabazi said, the rest of the region largely lags behind in a number of ways, particularly with respect to robotics, artificial intelligence and technology infrastructure and skills acquisition. COMESA member states are; Burundi, the Comoros, the Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Libya, Madagascar, Malawi, Mauritius, Rwanda, Sudan, Swaziland, Seychelles, Uganda, Zambia and Zimbabwe.

Rapid advancements in machine learning are creating a whole new set of content marketing tools for producing and delivering hyper-personalized content at scale. In view of the accelerated speed at which the machine learning and content automation markets are set to grow over the next 3-5 years, we expect brands and agencies who don't use them may be left behind. Clearly, users prefer to receive tailored content when it is helpful and relevant to their immediate needs. Digital marketers who can deliver content at the right time will easily outrank their competitors. Content marketing automation tools make this possible.

John Donahoe of ServiceNow and Jeff Weiner of LinkedIn speak about how businesses can play a role in improving work for people in the age of automation and artificial intelligence. How can CEOs stay ahead of the curve in training and developing their workforces for using automation and artificial intelligence (AI)? Since companies are doing the hiring and creating the jobs, what role do they play in talent and development? How should companies think about hiring as work changes? In this episode of the New World of Work podcast, McKinsey Global Institute director James Manyika speaks with two leaders on the forefront of applying AI techniques, such as automation and machine learning, in the business world. John Donahoe, president and CEO of ServiceNow, and Jeff Weiner, CEO of LinkedIn, tackle the tough questions facing companies today. James Manyika: When it comes to the issue of the future of work and automation, businesses are at the center for several reasons. For one, they are large employers of people and workers, and they are embracing these technologies that are starting to automate work. They play a central role through the choices that they make in using these technologies. Sometimes, they're also in the business of building products and services that also change and transform how we do work. And then sometimes, you come across rare business leaders who are far-forward-looking, think beyond their own businesses, and think about what these things mean for society. With that note, I'm quite delighted that we have two business leaders who satisfy all three of those criteria. They are employers, they are innovators building products and services, and they're also thinking beyond their own businesses to what this means for society.

Udacity's School of Artificial Intelligence has officially opened our new Deep Reinforcement Learning Nanodegree program for enrollment, and in doing so, we have completed a whirlwind effort that began at Intersect back in March of this year, when our School of AI was officially unveiled to the world: Today, anyone interested in entering the incredible world of Artificial Intelligence has the opportunity to do so, through the learning portal that is our School of AI. Upon arrival to the school's home page, you are prompted by a simple question: It's actually not that simple a question, of course, but we strive to make it so by offering you clear paths to pursue, depending on your current skills and experience, and your ultimate objectives. Whether you're new to the field, or already a working professional, we offer you a point-of-entry. Whether you want to work at a company focused on AI, or bring new AI techniques to a company that can benefit from them, we offer tailored curriculum to support your journey. Perhaps you're simply a future-minded thinker who sees where the world is headed, and you want to start planning ahead by adding valuable skills to your toolkit now.

It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge. However, these strategies are still unsatisfactory if the tasks are not disjoint but constitute a single incremental task (e.g., class-incremental learning). In this paper we point out the differences between multi-task and single-incremental-task scenarios and show that well-known approaches such as LWF, EWC and SI are not ideal for incremental task scenarios. A new approach, denoted as AR1, combining architectural and regularization strategies is then specifically proposed. AR1 overhead (in term of memory and computation) is very small thus making it suitable for online learning. When tested on CORe50 and iCIFAR-100, AR1 outperformed existing regularization strategies by a good margin.

Leaders get things done through others. Critical conversations with collaborators or partners to give feedback, negotiate, motivate, and solve conflicts are the core of their daily activities. Those moments are the ones when the difference between a leader and a boss is made. Focusing on critical conversations is a key element for trainers and coaches to make a durable and sustainable impact on leaders. More and more leaders are realizing the importance of mastering critical conversation skills and asking for more comprehensive development programs on it, as well as smart metrics to achieve and track results in conversational team leading.