data science

Ruiz: Democratizing artificial intelligence and deep learning


Years ago, when I was taking my first steps in computer programming, coding was for geeks and computer programs had limited use. Development tools were very crude, writing code was hard (remember Assembly, C, Pascal?), compiling and linking was a nightmare (MAKE files anyone?), and debugging was even worse. Long story short, programming was not for the faint of heart. You needed nerves of steel and had to patiently fail over and over before you got the hang of writing good code. But as software gradually rose in prominence, the entry barrier to programming lowered.

What is Data Science? An Introduction to Enterprise Data Science Innovation Infographic


Systems or machines that mimic human intelligence. Often used interchangeably with its subfields, including machine learning and deep learning, artificial intelligence has become a catch-all term for applications that perform complex tasks that once required human input, such as chatting online with customers or playing chess.

Can't find data scientists? Don't worry about it


It's no secret that data scientists continue to be among the most sought-after professionals in all of IT. As organizations continue to look for ways to gain value and insights from their data, these are the people they frequently turn to in order to make sense of all the information pouring into their systems from a growing number of sources. The good news for companies desperate to find these needed skill sets is that data science is becoming "democratized," which will help bridge the talent gap. Five factors are democratizing data science and putting this critical capability into the hands of more professionals, potentially alleviating the crippling talent shortage, according to a report released today from consulting firm Deloitte. Some estimates show that data scientists spend about 80 percent of their time on repetitive and tedious tasks -- data preparation, feature engineering and selection, and algorithm selection and evaluation -- that can be fully or partially automated.

Learning Machine Learning vs Learning Data Science


When you think of "data science" and "machine learning", do the two terms blur together, like Currier and Ives or Sturm and Drang? If so, you've come to the right place. This article will clarify some important and often-overlooked distinctions between the two to help you better focus your learning and hiring. Machine learning has seen much hype from journalists who are not always careful with their terminology. In popular discourse, it has taken on a wide swath of meanings and implications well beyond its scope to practitioners.

Lack of skills stopping machine learning adoption, says Cloudera


We've all heard the dire predictions about robots coming to steal our jobs. As technologies such as machine learning, AI and automation advance by the day, workplaces everywhere are being transformed; naturally, some people fear that they will become redundant -- depending on their job, some may be right. In this age of AI and ML, ambivalence is ripe; but something ironic has emerged: when it comes to advancing these cutting-edge technologies, the lack of human skills and knowledge is slowing innovation down. In a new survey by Cloudera, the software firm, exploring the benefits and roadblocks of ML adoption across Europe, 51% of business leaders said that the skills shortage was holding them back from implementation. According to Cloudera, companies are eager to use ML -- it's second only to analytics as the key investment priority for businesses; ahead of other disciplines like IoT, artificial intelligence and data science.

Common mistakes when carrying out machine learning and data science


This is part two of this series, find part one here - How to build a data science project from scratch. After scraping or getting the data, there are many steps to accomplish before applying a machine learning model. You need to visualize each of the variables to see distributions, find the outliers, and understand why there are such outliers. What can you do with missing values in certain features? What would be the best way to convert categorical features into numerical ones?

Should you become a data scientist?


There is no shortage of articles attempting to lay out a step-by-step process of how to become a data scientist. Are you a recent graduate? Do this… Are you changing careers? Do that… And make sure you're focusing on the top skills: coding, statistics, machine learning, storytelling, databases, big data… Need resources? Check out Andrew Ng's Coursera ML course, …". Although these are important things to consider once you have made up your mind to pursue a career in data science, I hope to answer the question that should come before all of this. It's the question that should be on every aspiring data scientist's mind: "should I become a data scientist?" This question addresses the why before you try to answer the how. What is it about the field that draws you in and will keep you in it and excited for years to come? In order to answer this question, it's important to understand how we got here and where we are headed. Because by having a full picture of the data science landscape, you can determine whether data science makes sense for you. Before the convergence of computer science, data technology, visualization, mathematics, and statistics into what we call data science today, these fields existed in siloes -- independently laying the groundwork for the tools and products we are now able to develop, things like: Oculus, Google Home, Amazon Alexa, self-driving cars, recommendation engines, etc. The foundational ideas have been around for decades... early scientists dating back to the pre-1800s, coming from wide range of backgrounds, worked on developing our first computers, calculus, probability theory, and algorithms like: CNNs, reinforcement learning, least squares regression. With the explosion in data and computational power, we are able to resurrect these decade old ideas and apply them to real-world problems. In 2009 and 2012, articles were published by McKinsey and the Harvard Business Review, hyping up the role of the data scientist, showing how they were revolutionizing the way businesses are operating and how they would be critical to future business success. They not only saw the advantage of a data-driven approach, but also the importance of utilizing predictive analytics into the future in order to remain competitive and relevant. Around the same time in 2011, Andrew Ng came out with a free online course on machine learning, and the curse of AI FOMO (fear of missing out) kicked in. Companies began the search for highly skilled individuals to help them collect, store, visualize and make sense of all their data. "You want the title and the high pay?

Data Science Curriculum from Scratch 2018 (Part 1) – Benjamin Lau – Medium


There are no hard and fast rules for learning such a complex topic. The beauty of online learning is that you get to choose what you lack and what excite you. For this part 1 of the series, I will review the maths and python fundamental courses that I had taken. Please note that these are my personal opinion which might or might not resonate with you. I like to give special mention to Data Science A-Z by Kirill Eremenko and the SuperDataScience Team.

120 AI Predictions For 2019


Me: "Alexa, tell me what will happen in 2019." Amazon AI: "Do you want to open'this day in history'?" Me: "Alexa, give me a prediction for 2019." Amazon AI: "The crystal ball is clouded, I can't tell." My conversation with Amazon's "smart speaker" or "intelligent voice assistant" just about sums up the present state of "artificial intelligence" (AI) at home, the office, and the factory: Try a few times and sooner or later you will probably get the correct action the human intelligence behind it programmed it to perform. What will be the state of AI in 2019? The following list features 120 senior executives involved with AI, all peering into their not-so-clouded crystal ball, and promising less hype and more practical, precise, and narrow AI. "Self-Driving Finance is a practical implementation of AI that is already used in one form or another by millions of bank customers around the globe and will only get better in the coming years. Based on projects that are currently underway with ...

Pick Up the Machine Learning Master Class Bundle for Less than $50 - Make Tech Easier


Why guess what will happen tomorrow, the next day, or further into the future? Put your computer to work and allow it to do the predicting. Machine learning allows computers to learn from data and make predictions based on that, saving you the headache. With the Machine Learning Master Class Bundle, you can learn all you need to know about this technology, such as data visualization with Python, R programming, game development, and more. The following eight courses are included in this bundle.