I am excited that I can now finally talk about this initiative publicly: an online AI training course geared towards business decision makers.

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Forbes points out that the term “Big Data” has been eclipsed by “Data Science” in the hype cycle. However, the Great Hype Cycle resembles Game of Thrones and I think we can all agree that “AI” or “Machine Learning” is next to sit on the Iron Throne of Hype.

In a world in which “big data” and “data science” seem to adorn every technology-related news article and social media post, have the terms finally reached public interest saturation? As the use of large amounts of data has become mainstream, is the role of “data science” replacing the hype of “big data?”

With the rise of Machine Learning came the rise of developer tools and libraries. What are they good for and what are the top ones that every data scientist and ML engineer should know. This article sheds some light on those questions.

A deep learning framework is an interface, library or a tool which allows us to build deep learning models more easily and quickly, without getting into the details of underlying algorithms. They provide a clear and concise way for defining models using a collection of pre-built and optimized components.

Predicting the stock market is one of the most difficult things to do given all the variables. There are numerous factors involved – physical factors vs. psychological, rational and irrational behavior, etc. All these aspects combine to make share prices volatile and very difficult to predict accurately.

In this article, we will work with historical data about the stock prices of a publicly listed company. We will implement a mix of machine learning algorithms to predict the future stock price of this company, starting with simple algorithms like averaging and linear regression, and then move on to advanced techniques like Auto ARIMA and LSTM.

While Uber is known as one of the pioneers in self-driving vehicels, its autonomous vehicle division has been a source of contention for investors. TechCrunch recently reported numbers that were less than flattering : The ride-hailing company was spending $20 million a month on developing self-driving technologies.The Wall Street Journal estimates that Uber spent about $750 million on building out self-driving technologies before scaling back in 2018.

However, all is not bleak: Uber ’s autonomous vehicle unit may be about to get a massive ($1 billion+) cash injection?

It’s highly possible, according to news reports indicating a group of investors including SoftBank Group is putting money into the division. The Wall Street Journal reported last night that Uber, more formally known as Uber Technologies Inc., was in “late-stage” discussions with a consortium that would invest in the startup’s self-driving vehicle division.

Jerry Chi, Data Science Manager at SmartNews, has compiled a list of “mind-blowing” ML/AI breakthroughs of the last year or two.  I have to say that I agree with most of his choices.

Compared to other fields, machine learning / artificial intelligence seems to have a much higher frequency of super-interesting developments these days. Things that make you say “wow” or even “what a time to be alive!” (as the creator of Two Minute Papers always says)

Normally, I say the best way to get started with machine learning is to do machine learning. So, strictly speaking, there’s no “required math” to get started and follow along with online tutorials. However, if you want to take your AI game to the next level, then it’s time to get deep into the math.

Data Science Central outlines specifics around the subjects to tackle and where to find them online.

  1. Linear Algebra — Professor Strang’s textbook and MIT Open Courseware course are recommended for good reason. Khan Academy also has some great resources, and there is a helpful set of review notes from Stanford.
  2. Multivariate Calculus — Again, MIT Open Courseware has good courses, and so does Khan Academy.
  3. Probability — Stanford’s CS 229, a course I’ve mentioned later, has an awesome probability review worth checking out.