Opinions, Interviews, Reports
We welcome guest blogs and opinions - see submission guidelines.
Latest:
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Choosing a Machine Learning Model - Oct 14, 2019.
Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications. -
There is No Such Thing as a Free Lunch: Part 1 - Oct 11, 2019.
You have heard the expression “there is no such thing as a free lunch” – well in machine learning the same principle holds. In fact there is even a theorem with the same name. -
The problem with metrics is a big problem for AI - Oct 11, 2019.
The practice of optimizing metrics is not new nor unique to AI, yet AI can be particularly efficient (even too efficient!) at doing so. -
8 Paths to Getting a Machine Learning Job Interview - Oct 10, 2019.
While you may be focused on your performance during your next job interview, landing that interview can be just as hard. Check out these tips for finding and securing an interview for a machine learning job. -
Lemma, Lemma, Red Pyjama: Or, doing words with AI - Oct 10, 2019.
If we want a machine learning model to be able to generalize these forms together, we need to map them to a shared representation. But when are two different words the same for our purposes? It depends. -
Four questions to help accurately scope analytics engineering project - Oct 9, 2019.
Being really good at scoping analytics projects is crucial for team productivity and profitability. You can consistently deliver on time if you work out the issue first, and these four questions can help you prepare. -
Data Science is Boring (Part 2) - Oct 9, 2019.
Why I love boring ML problems and how I think about them. -
Why the ‘why way’ is the right way to restoring trust in AI - Oct 8, 2019.
As so many more organizations now rely on AI to deliver services and consumer experiences, establishing a public trust in the AI is crucial as these systems begin to make harder decisions that impact customers. -
Overcoming Deep Learning Stumbling Blocks - Oct 4, 2019.
Find out what was presented at the 6th annual Deep Learning Summit in London where industry leaders, academics, researchers, and innovative startups presenting the latest technological advancements and industry application methods in the field of deep learning. -
Training a Machine Learning Engineer - Oct 3, 2019.
There is no clear outline on how to study Machine Learning/Deep Learning due to which many individuals apply all the possible algorithms that they have heard of and hope that one of implemented algorithms work for their problem in hand. Below, I've listed out some of the steps that one should adopt while solving a machine learning problem. -
A European Approach to Master’s Degrees in Data Science - Oct 1, 2019.
Data science education in Europe has been reevaluated and new recommendations are leading the way to the next generation of data science Master's courses to better support and train students.
September:
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How AI will transform healthcare (and can it fix the US healthcare system?)
This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services. - Data Mapping Using Machine Learning
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6 bits of advice for Data Scientists
As a data scientist, you can get lost in your daily dives into the data. Consider this advice to be certain to follow in your work for being diligent and more impactful for your organization.
- The thin line between data science and data engineering
- Data Quality Assessment Is Not All Roses. What Challenges Should You Be Aware Of?
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5 Famous Deep Learning Courses/Schools of 2019
Deep Learning is/has become the hottest skill in Data Science at the moment. There is a plethora of articles, courses, technologies, influencers and resources that we can leverage to gain the Deep Learning skills. -
12 Deep Learning Researchers and Leaders
Our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in AI. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.
- The Hidden Risk of AI and Big Data
- 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python
- Data Science is Boring (Part 1)
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Which Data Science Skills are core and which are hot/emerging ones?
We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.
- 5 Alternative Data Science Tools
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My journey path from a Software Engineer to BI Specialist to a Data Scientist
The career path of the Data Scientist remains a hot target for many with its continuing high demand. Becoming one requires developing a broad set of skills including statistics, programming, and even business acumen. Learn more about one person's experience making this journey, and discover the many resources available to help you find your way into a world of data science.
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Cartoon: Unsupervised Machine Learning?
New KDnuggets Cartoon looks at one of the hottest directions in Machine Learning and asks "Can Machine Learning be too unsupervised?"
- Many Heads Are Better Than One: The Case For Ensemble Learning
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There is No Free Lunch in Data Science
There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.
- Classification vs Prediction
- Common Machine Learning Obstacles
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BERT is changing the NLP landscape
BERT is changing the NLP landscape and making chatbots much smarter by enabling computers to better understand speech and respond intelligently in real-time.
- What’s the difference between analytics and statistics?
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I wasn’t getting hired as a Data Scientist. So I sought data on who is.
Instead of focusing on skills thought to be required of data scientists, we can look at what they have actually done before. - 3 Ways to Manage Human Bias in the Analytics Process
- Automated Machine Learning: Just How Much?
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Advice on building a machine learning career and reading research papers by Prof. Andrew Ng
This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers.
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TensorFlow vs PyTorch vs Keras for NLP
These three deep learning frameworks are your go-to tools for NLP, so which is the best? Check out this comparative analysis based on the needs of NLP, and find out where things are headed in the future.
- 6 Tips for Building a Training Data Strategy for Machine Learning
- Cartoon: Labor Day in the age of AI
