Apache Spark has become the de-facto data processing and AI engine in enterprises today due to its speed, ease of use, and sophisticated analytics. As the first Unified Analytics engine to unify data with AI, Spark allows data engineering and data science teams to simplify data preparation and model training -- enabling innovative AI use cases that leverage advanced analytics like machine learning, graph analytics, and deep learning. Join Bill Chambers, author of the book "Spark: The Definitive Guide," and Matei Zaharia, Chief Technologist and Co-founder of Databricks and the orginal creator of Apache Spark, in this Data Science Central webinar as they break down the basic operations and common functions of Spark and walk through sample use cases where Spark has helped accelerate AI innovation. In this webinar, we will cover: A gentle overview of big data and Spark Expert guidance on how to use, deploy and maintain Spark The fundamentals of monitoring, tuning, and debugging Spark An exploration into machine learning techniques and scenarios for employing MLlib, Spark's scalable machine-learning library Speakers: Bill Chambers, Product Manager -- Databricks Matei Zaharia, Co-founder and Chief Technologist -- Databricks Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

If I could go back in time, I would try myself in Machine Learning 12 years ago! Right when I finished undergrad and came to the USA. After starting Andrew Ng's Machine Learning course on Coursera last month, I dropped everything except most urgent things and completed an 11 week course in just 3 weeks. The somewhat sad truth is, I first enrolled in this course many months ago, but I didn't start it then. Stars finally aligned in August and I started that course.

Many programmers are moving towards data science and machine learning hoping for better pay and career opportunities -- and there is a reason for it. The Data scientist has been ranked the number one job on Glassdoor for last a couple of years and the average salary of a data scientist is over $120,000 in the United States according to Indeed. Data science is not only a rewarding career in terms of money but it also provides the opportunity for you to solve some of the world's most interesting problems. IMHO, that's the main motivation many good programmers are moving towards data science, machine learning, and artificial intelligence. If you are in the same boat and thinking about becoming a data scientist in 2018, then you have come to the right place.

Artificial Intelligence is one of the fastest-growing and most exciting fields in technology today! Knowledge of Deep Learning and Machine Learning is highly valued by companies that are creating cutting-edge technology and professionals with these skills can expect their career to skyrocket in the coming years. Edureka offers certification courses in TensorFlow and Mahout to help you take advantage of the career opportunities in Artificial Intelligence.

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work.

Fortnite developers have had to pull the new season's biggest feature just hours after it was released. When the new update arrived, players rushed to get their hands on the "shadow stone": an object that could make the players who held it invisible. It was the most central of the spooky new updates, which also include the ability to have pets and changes to the map. But when they arrived in the game, players found that the invisibility wasn't necessarily temporary, as expected. Instead, there was a way of going invisible forever.

A machine learning algorithm isn't worth much without great training data to power it. After all, algorithms learn from data, discovering relationships, developing understanding, making decisions, and evaluating their confidence from the training data they're given. And the better the training data is, the better the model performs. In fact, the quality and quantity of your training data has as much to do with the success of your data project as the algorithms themselves. Join us for this latest Data Science Central webinar on the basics of training data where we will cover: What training data is and why it's so important What training data looks like for a variety of projects Why training data should be labeled and how to get it labeled How much training data you need Speaker: Jennifer Prendki, VP of Machine Learning -- Figure Eight Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

Big data, data science, deep learning, artificial intelligence are the key words of intense hype related with a job market in full evolution, that impose to adapt the contents of our university professional trainings. Which artificial intelligence is mostly concerned by the job offers? Which methodologies and technologies should be favored in the training pprograms? Which objectives, tools and educational resources do we needed to put in place to meet these pressing needs? We answer these questions in describing the contents and operational ressources in the Data Science orientation of the speciality Applied Mathematics at INSA Toulouse. We focus on basic mathematics training (Optimization, Probability, Statistics), associated with the practical implementation of the most performing statistical learning algorithms, with the most appropriate technologies and on real examples. Considering the huge volatility of the technologies, it is imperative to train students in seft-training, this will be their technological watch tool when they will be in professional activity. This explains the structuring of the educational site https://github.com/wikistat/ into a set of tutorials. Finally, to motivate the thorough practice of these tutorials, a serious game is organized each year in the form of a prediction contest between students of Master degrees in Applied Mathematics for IA.

In recent years, there has been a lot of focus on adaptive e-learning, fueled by the advances of machine learning and artificial intelligence. As the one-size-fits-all approach of e-learning loses its appeal and online course attrition rates continue to rise, there is a move toward more personalized and adaptive learning to engage learners and achieve better learning outcomes. Personalized and adaptive learning has the ability to change learning content or the mode of delivery on the fly and to provide real-time feedback to learners. The origin of adaptive learning came from the research of intelligent tutoring systems, recommender systems and adaptive hypermedia. The advent of machine learning and artificial intelligence techniques have helped the plethora of platforms and tools that support adaptive learning flourish.

The course, recorded at the University of San Francisco as part of the Masters of Science in Data Science curriculum, covers the most important practical foundations for modern machine learning. There are 12 lessons, each of which is around two hours long--a list of all the lessons along with a screenshot from each is at the end of this post. There are some excellent machine learning courses already, most notably the wonderful Coursera course from Andrew Ng. But that course is showing its age now, particularly since it uses Matlab for coursework. This new course uses modern tools and libraries, including python, pandas, scikit-learn, and pytorch.