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.

Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go. Sure, AI systems have mastered an array of games, from chess and Go to "Jeopardy" and poker, but the technology continues to struggle in the real world. Robots fall over while opening doors, prototype driverless cars frequently need human intervention, and nobody has yet designed a machine that can read reliably at the level of a sixth-grader, let alone a college student. Computers that can educate themselves -- a mark of true intelligence -- remain a dream. Even the trendy technique of "deep learning," which uses artificial neural networks to discern complex statistical correlations in huge amounts of data, often comes up short.

We present a new Frank-Wolfe (FW) type algorithm that is applicable to minimization problems with a nonsmooth convex objective. We provide convergence bounds and show that the scheme yields so-called coreset results for various Machine Learning problems including 1-median, Balanced Development, Sparse PCA, Graph Cuts, and the $\ell_1$-norm-regularized Support Vector Machine (SVM) among others. This means that the algorithm provides approximate solutions to these problems in time complexity bounds that are not dependent on the size of the input problem. Our framework, motivated by a growing body of work on sublinear algorithms for various data analysis problems, is entirely deterministic and makes no use of smoothing or proximal operators. Apart from these theoretical results, we show experimentally that the algorithm is very practical and in some cases also offers significant computational advantages on large problem instances. We provide an open source implementation that can be adapted for other problems that fit the overall structure.

When you think artificial intelligence, it's likely that scenes from a science fiction thriller come to mind. Robots fighting humans, men falling in love with a computer that learns to feel, iPhones outsmarting their user. For years, educators have struggled to help each and every student with their individualized educational needs. That gets incredibly tough in a classroom of twenty, thirty, forty, or fifty students all required to pass the same standardized test, regardless of personal growth. The use of artificial intelligence has the potential to disrupt the traditional and potentially damaging one-size-fits all model of modern teaching.