"Human plus machine isn't the future, it's the present," Garry Kasparov said in a recent TED talk. And this "present" is transforming the world of education at a rapid pace. With children increasingly using tablets and coding becoming part of national curricula around the world, technology is becoming an integral part of classrooms, just like chalk and blackboards. We have already witnessed the rise and impact of education technology especially through multitude of adaptive learning platforms such as Khan Academy and Coursera that allow learners to strengthen their skills and knowledge. And now virtual reality (VR) and artificial intelligence (AI) are gaining traction. A recent report by Pearson deciphers how artificial intelligence will positively transform education in the coming years.

The real power and value proposition of Apache Spark is its speed and platform to execute Data Science tasks. Spark's unique use case is that it combines ETL, batch analytic, real-time stream analysis, machine learning, graph processing, and visualizations to allow Data Scientists to tackle the complexities that come with raw unstructured data sets. Spark embraces this approach and has the vision to make the transition from working on a single machine to working on a cluster, something that makes data science tasks a lot more agile. In this course, you'll get a hands-on technical resource that will enable you to become comfortable and confident working with Spark for Data Science. We won't just explore Spark's Data Science libraries, we'll dive deeper and expand on the topics.

R is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with R, then go for this course. This course is a blend of text, videos, code examples, assessments, case studies, and a mini project which together makes your learning journey all the more exciting and truly rewarding. It is meticulously designed and developed in order to empower you with all the right and relevant information on R. Let's take a look at this learning journey. The course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. You will learn the various data types, operators, and control structures.

Java is arguably the single most important technology out there. Core Java Programming is an excellent introduction in to the world of Java programming. The instructor will take you through the basics of Java syntax and the complexities of Object Oriented Programming. This course is a stand-alone course, however it would be a huge aid to the online student who is taking a self-directed course, an individual who is trying to learn how to program. At the end of this course, you will be well versed with how to program in Java from the very basic level to an intermediate level of programming.

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. Learning regression machine learning is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. And it is necessary for any business forecasting related decision. But as learning curve can become steep as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.

Yet the inexperienced or rushed data scientist skipped past feature engineering, the critical stage at which those invalid fields would have been removed. The experienced data scientist would know to invest lots of time in feature engineering to explicitly screen out potential bias from our training data. If our hiring data to date has a past human bias of not hiring women at the same rate as men, our machine learning model would learn to emulate that behavior unless we explicitly removed gender from consideration. It's easy to see how bias could creep in if inexperienced or rushed data scientists are building models from massive datasets.

Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

The role of data scientists will be transformed as machine learning techniques become more widely used by businesses, according to Stephen Brobst, CTO of analytics firm Teradata. While many of the principles behind'AI' approaches are not new, interest within an enterprise setting has exploded in recent years. And as usage becomes more widespread and sophisticated, the role of data scientists will begin to evolve too, according to Brobst. He explains that data scientists have typically spent much of their time'wrangling' data to feed into predictive models. In future, more of this work will be automated and data scientists will instead be more focused on selecting which machine learning or deep learning tools to utilise for specific tasks.

Key Points: – We're kicking off Watson Lab's high school internship program for the spring semester – The curriculum prepares the students to work as Software Developers at IBM during their Senior year. It is that time of year again. At Watson Lab we are piloting a high school internship program for the spring semester. We love to welcome visitors from the community to learn about our projects with AI and Cognitive Computing and experience some of the groundbreaking work we're pursuing behind the scenes. Computer Science students from Connally High School from Pflugerville visited us last spring.