Mar
2018 Apr Opinions, Interviews
All (100) | Courses, Education (4) | Meetings (10) | News, Features (11) | Opinions, Interviews (33) | Top Stories, Tweets (10) | Tutorials, Overviews (27) | Webcasts & Webinars (5)
- Operational Machine Learning: Seven Considerations for Successful MLOps - Apr 30, 2018.
In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives
- How to Make AI More Accessible - Apr 30, 2018.
I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.
- What should be focus areas for Machine Learning / AI in 2018? - Apr 27, 2018.
This article looks at what are the recent trends in data science/ML/AI and suggests subareas DS groups need to focus on.
- Machine Learning Engineer, Researcher, Data Scientist have the highest job satisfaction - Apr 26, 2018.
KDnuggets poll finds that Machine Learning Engineer, Researcher, and Data Scientist have the highest job satisfaction. Job satisfaction usually starts high, but drops significantly after 4 years on the job. Data professionals in Asia and Latin America are most unsatisfied.
- The Dirty Little Secret Every Data Scientist Knows (but won’t admit) - Apr 26, 2018.
Most people don’t realize, but the actual “fancy” machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!
- Doing Real-Time Data Analysis With Db2 Event Store - Apr 26, 2018.
IBM unveiled the updated Db2 Event Store platform and various features at its Think 2018 conference, tailored for those in the data industry, including data scientists, and application developers.
- Bitcoin Trade Signals - Apr 25, 2018.
This article covers the transformation of public emotions, big news and blockchain data into signals which can provide us with a better understanding as well as instructions for investing.
- Data Science Interview Guide - Apr 25, 2018.
Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).
- How I Unknowingly Contributed To Open Source - Apr 24, 2018.
This article explains what is meant by the term 'open source' and why all data scientists should be a part of it.
- Data Exchange and Marketplace, a New Business Model in Making - Apr 23, 2018.
This article covers how an ever-increasing amount of data will trigger the evolution of a new ecosystem that will spur entrepreneurial activity, offering an opportunity to start a wide range of new businesses.
- Let’s Admit It: We’re a Long Way from Using “Real Intelligence” in AI - Apr 19, 2018.
With the growth of AI systems and unstructured data, there is a need for an independent means of data curation, evaluation and measurement of output that does not depend on the natural language constructs of AI and creates a comparative method of how the data is processed.
- Presto for Data Scientists – SQL on anything - Apr 19, 2018.
Presto enables data scientists to run interactive SQL across multiple data sources. This open source engine supports querying anything, anywhere, and at large scale.
- Hedge Yourself From a Risky Data Science Job - Apr 18, 2018.
This article covers why it's important to consider all the factors when being hired as a data scientist.
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7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - Apr 17, 2018.
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help. - Role of IoT in Education - Apr 17, 2018.
In this article, I will discuss the significance of IoT and gain insights on why this technology is becoming an integral part of the daily learning and teaching methodologies.
- When Do We Trust Machines? - Apr 16, 2018.
We propose a framework of "trust heatmap", show how the trust in machines depends on two key elements: their error rate and the costs of mistakes, and examine the automation frontier.
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Key Algorithms and Statistical Models for Aspiring Data Scientists - Apr 16, 2018.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques. - Are High Level APIs Dumbing Down Machine Learning? - Apr 16, 2018.
Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?
- Don’t learn Machine Learning in 24 hours - Apr 13, 2018.
When it comes to machine learning, there's no quick way of teaching yourself - you're in it for the long haul.
- Machine Learning Engineer, Data Scientist among the best US Jobs in 2018 - Apr 12, 2018.
Machine Learning Engineer, with avg. salary of $136K and Data Scientist, with avg. salary $133K are among the top US jobs in 2018, according to job site Indeed.
- Onboarding Your Machine Learning Program - Apr 12, 2018.
Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.
- Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
- Top 8 Free Must-Read Books on Deep Learning - Apr 10, 2018.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.
- The Cold Start Problem with AI - Apr 10, 2018.
Why companies struggle with implementing AI and how to overcome it.
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis - Apr 9, 2018.
CRM/Consumer Analytics, Finance, and Banking are still the leading applications, but Health Care and Fraud Detection are gaining. Anti-spam, Manufacturing, and Social are the fastest growing sectors in 2017, while Oil / Gas / Energy and Social Networks analysis have declined.
- Why so many data scientists are leaving their jobs, by Jonny Brooks - Apr 9, 2018.
We look at some of the big challenges and frustrations that data scientists face on a regular basis.
- Build a Foundation that Supports AI and Machine Learning - Apr 6, 2018.
In an upcoming livestream on April 19, we’ll dig into how to build a foundation that supports AI and Machine Learning with industry experts and uncover what many companies are going through.
- Why Data Scientists Must Focus on Developing Product Sense - Apr 6, 2018.
Data Scientists should focus on developing product sense to move fast and systematically, create models that are relevant and to able to know when to stop.
- Are New Technologies Killing Their Ancestors? - Apr 6, 2018.
Are automatic feature learning models (e.g. CNN) killing their previous manually engineered models? This is an important question that is to be answered in this article.
- Data Science and the Art of Producing Entertainment at Netflix - Apr 5, 2018.
Each Netflix production is a logistical challenge that consumes and produces a vast amount of data. The tech giant is utilising this data to help them create new content and assist them at every stage, from pre-production to launching the show.
- What Does GDPR Mean for Machine Learning? - Apr 4, 2018.
This post investigates how the GDPR, which comes into force at the end of May, will effect machine learning.
- How Do I Get My First Data Science Job? - Apr 2, 2018.
Here are the steps you need to obtain your first job in data science, including details on how to create a good portfolio, key networking tips, getting the right education and managing expectations.
- A Day in the Life of a Data Scientist: Part 4 - Apr 2, 2018.
Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning
Key Algorithms and Statistical Models for Aspiring Data Scientists
Ten Machine Learning Algorithms You Should Know to Become a Data Scientist