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Since this article will probably come out during Income tax season, let me start with the following example: Suppose we would like to build a program that calculates income tax for people. According to US federal income tax rules: "For single filers, all income less than $9,875 is subject to a 10% tax rate. Therefore, if you have $9,900 in taxable income, the first $9,875 is subject to the 10% rate and the remaining $25 is subject to the tax rate of the next bracket (12%)". This is an example of rules or an algorithm (set of instructions) for a computer. Let's look at this from a formal, pragmatic point of view. A computer equipped with this program can achieve the goal (calculate tax) without human help.

In Uber's ride-hailing business, a driver picks up a user from a curbside or other location, and then drops them off at their destination, completing a trip. Uber Eats, our food delivery service, faces a more complex trip model. When a user requests a food order in the app, the specified restaurant begins preparing the order. When that order is ready, we dispatch a delivery-partner to pick it up and bring it to the eater. Modeling the real world logistics that go into an Uber Eats trip is a complex problem.

Now that artificial intelligence and machine learning have become increasingly common tools in a business' arsenal, it is equally important to have employees who are capable of using – or developing – such tools. Chief among them should be a data scientist: someone with the experience and ability necessary to work with structured and unstructured data, and build systems capable of mining that data to come up with actionable and useful insights. The exact responsibilities of a data scientist can vary depending on the type of organization they work for, which means that it's up to an individual business to determine what they are, and how they want to incorporate data science and machine learning into their company. In addition, data science is a relatively new field so, as a Forbes article put it, "large number[s] of data scientists are willing to apply yet few have the required experience." As careers in data science, artificial intelligence and machine learning grow increasingly lucrative, it is not unusual to see hordes of recent graduates looking for jobs in those fields. This, coupled with the dearth of data scientists with extensive job experience, means that for the time being, most companies will be hiring people who are either straight out of school or have limited work experience.

As of the beginning of January 2020, it's my great pleasure to join The Machine Learning Times as editor in chief! I've taken over the main editorial duties from Eric Siegel, who founded the ML Times (also the founder of the Predictive Analytics World conference series). As you've likely noticed, we've renamed to The Machine Learning Times what until recently was The Predictive Analytics Times. In addition to a new, shiny name, this rebranding corresponds with new efforts to expand and intensify our breadth of coverage. One particular area of focus will be to increase our coverage of deep learning.

In anticipation of his upcoming conference presentation at Predictive Analytics World for Business Las Vegas, May 31-June 4, 2020, we asked Kumaran Ponnambalam, Analytics Architect at Cisco, a few questions about their deployment of predictive analytics. Catch a glimpse of his presentation, Using Association Rules Mining for Segmentation and Profiling, and see what's in store at the PAW Business conference in Las Vegas. Q: In your work with predictive analytics, what behavior or outcome do your models predict? A: My models deal with natural language understanding for contact center voice calls. They transcribe the calls and derive call summaries, intent and sentiment based on the transcriptions.

Originally published in SAP Blogs, October 16, 2019. For today's leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, May 31-June 4, 2020. Machine learning algorithms, especially deep learning neural networks often produce models that improve the accuracy of prediction. But the accuracy comes at the expense of higher computation and memory consumption. A deep learning algorithm, also known as a model, consists of layers of computations where thousands of parameters are computed in each layer and passed to the next, iteratively.

A shorter version of this article was originally published by Scientific American. With articles like these, the press will have you believe that machine learning can reliably predict whether you're gay, whether you'll develop psychosis, whether you'll have a heart attack, and whether you're a criminal – as well as other ambitious predictions such as when you'll die and whether your unpublished book will be a bestseller. Machine learning can't confidently tell such things about each individual. In most cases, these things are simply too difficult to predict with certainty. Researchers report high "accuracy," but then later reveal – buried within the details of a technical paper – that they were actually misusing the word "accuracy" to mean another measure of performance related to accuracy but in actuality not nearly as impressive.

Economic disruption is a reality which has been a gradual development over the last several decades. Artificial intelligence (AI) has simply accelerated this process. Virtually every industry has been impacted by AI and certainly data science is no exception. Yet, we may also inquire how does machine learning fit within this overall discussion. The explosion of literature on these topics over the last several years is a testament to the popularity of both topics.

Python is one of the most widely used languages in data science, and an incredibly popular general programming language on its own. Many prospective data scientists are first faced with the issue of which programming language might be their choice when diving into data science. This is further complicated if you don't already bring a set of existing programming skills on which to rely. Even better would be a thorough understanding of Python as you shift to data science (substitute another language if it is to be your preferred data science programming tool), but many newcomers to the field find themselves either starting from relative scratch when it comes to either programming in general, or Python more specifically. This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey.