Apple recently announced their partnership with IBM to leverage IBM's Watson service to train machine learning models for CoreML. So that mean you now can build apps that leverage Watson machine learning models on iPhone and iPad, even when your device is offline. Your apps can quickly analyze images, accurately classify visual content, and easily train models using Watson Services. With this video series you will learn to onboard with not only pre-trained Watson models but customize and train models that continuously learn over time. In Apple's own words "You can build apps that seamlessly integrate with IBM Cloud using the IBM Cloud Developer Console for Apple. This allows you to quickly tap into Watson Services for Core ML, as well as other IBM cloud services including authentication, data, analytics, and more. The console provides a catalog of starter kits designed for common frameworks that integrate with IBM Cloud."

As the buzz around data science grows every day, there is a slew of self-taught professionals who have kick-started the machine learning journey with Andrew Ng's online courses. Many enthusiasts are gravitating towards the computer science field. But if one wants to pursue a career in Machine Learning, they need to be familiar with statistics and linear algebra. With computer science and ML applications becoming more pervasive in everyday life, people from a non-technical background are also interested in joining the field. In this article, we have discussed in-depth roles a person from non-tech background can explore in the data science/AI field.

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 do research as experienced investor. Learning stock technical analysis is indispensable for finance careers in areas such as equity research and equity trading. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors stock technical trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.

About this course: Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data.

Apache Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. It's used to create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. If you're a data professional who is familiar with machine learning and wants to use Apache Spark for developing efficient and fast machine learning systems, then this learning path is for you. This comprehensive 2-in-1 course teaches you to build machine learning systems, perform analytics, and predictions with Apache Spark. You'll learn through practical demonstrations of use cases, clear explanations, and interesting real-world applications. Each section briefly establishes theoretical basis for the topic under discussion and then cement your understanding with practical use cases.

Here I will be posting some of the data science and machine learning projects that I have been working on. The main motivation for making this blog is that I will soon be starting the Fast AI Deep Learning course. Blogging along with the lectures seemed like a great opportunity for me to be really hands-on with the material and get acquainted with other students. Let me first start by giving you a quick background of my journey into data science. About a year ago I started writing my master thesis for the study Business Administration.

This is a very basic introductory course to the fundamentals of the Scala programming language for anyone new to the language. Scala was derived from Java which is one of the top-five programming languages in the world today. It is a versatile and elegant object –oriented programming language. This means it is class based and treats everything as an object. It has a robust security .

For the 7,000 people in the audience for Google's (GOOG, GOOGL) I/O keynote last week, the Google Duplex demo was a mind-fryer. CEO Sundar Pichai had said to his phone, "OK Google, book me a haircut appointment on Tuesday between 10 a.m. and noon." And then, silently and invisibly (to him), Google Assistant had made a phone call to a human receptionist at the salon and had held a conversation, flawlessly impersonating an actual person, complete with "umms" and "ahhs." The receptionist never knew she'd been talking to AI. "That the many in Google did not erupt in utter panic and disgust at the first suggestion of this is incredible to me," tweeted Zeynep Tufekci, a University of North Carolina professor. "This is horrible and so obviously wrong. And on "CBS This Morning," Salesforce (CRM) CEO Marc Benioff spoke about it in the context of his call for a new, national privacy law. "That was the most amazing AI technology I've seen.

In this video from Intel AI DevCon 2018, Andrew Ng from Deeplearning.ai and Landing.ai When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Andrew NG says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is."

Edureka's Masters Program is a thoughtful compilation of Instructor -Led and Self Paced Courses, allowing the learners to be guided by industry experts, as well as learn skills at their own pace. In the Data Science Masters Program, Data Science Certification Course using R, Python Certification Training for Data Science, Apache Spark and Scala Certification Training, AI & Deep Learning with TensorFlow, Tableau Training & Certification are Instructor - led Online Courses.