Nov
2018 Dec Opinions
All (86) | Courses, Education (3) | Meetings (5) | News (14) | Opinions (28) | Top Stories, Tweets (9) | Tutorials, Overviews (23) | Webcasts & Webinars (4)
- Supervised Learning: Model Popularity from Past to Present
- Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
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The Essence of Machine Learning
- Dec 28, 2018.
And so now, as an exercise in what may seem to be semantics, let's explore some 30,000 feet definitions of what machine learning is. - A Case For Explainable AI & Machine Learning
- Dec 27, 2018.
In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.
- Using the Economics Value Curve to Drive Digital Transformation
- Dec 27, 2018.
Optimizing a single objective, or a single point, is actually quite easy because there are no conflicting objectives. The real business challenge, and the source of much innovation, is trying to optimize a decision across multiple variables. Let’s explore this further.
- Interspeech 2018: Highlights for Data Scientists
- Dec 24, 2018.
Key highlights from the Interspeech conference, with topics covering end-to-end models for automatic speech recognition, information theory approach to deep learning, speech processing and education, and more.
- Twas the Night Before Analysis or A Visit from the Chief Data Scientist
- Dec 24, 2018.
The holiday classic gets a data science makeover. Let your belly shake like a bowl full of jelly.
- Feature engineering, Explained
- Dec 21, 2018.
A brief introduction to feature engineering, covering coordinate transformation, continuous data, categorical features, missing values, normalization, and more.
- Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI
- Dec 20, 2018.
We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.
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Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning
- Dec 19, 2018.
Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning. - The brain as a neural network: this is why we can’t get along
- Dec 19, 2018.
This article sets out to answer the question: what insights can we gain about ourselves by thinking of the brain as a machine learning model?
- Think Twice Before You Accept That Fancy Data Science Job
- Dec 19, 2018.
Before you figure out what skills you need to freshen up on, or the most optimal driving path to work to avoid traffic patterns, you need to make sure this new role is a right fit and that you'll be happy working there.
- How will automation tools change data science?
- Dec 18, 2018.
This article provides an overview of recent trends in machine learning and data science automation tools and addresses how those tools will change data science.
- Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019
- Dec 18, 2018.
This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!
- 2018 Year-in-Review: Machine Learning Open Source Projects & Frameworks
- Dec 17, 2018.
This post is a look at the top open source projects and major developments in machine learning frameworks over the past 12 months.
- NLP Breakthrough Imagenet Moment has arrived
- Dec 14, 2018.
A comprehensive review of the current state of Natural Language Processing, covering the process from shallow to deep pre-training, what's in an ImageNet, the case for language modelling, and more.
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Why You Shouldn’t be a Data Science Generalist
- Dec 14, 2018.
But it’s hard to avoid becoming a generalist if you don’t know which common problem classes you could specialize in in the fist place. That’s why I put together a list of the five problem classes that are often lumped together under the “data science” heading. - Four Approaches to Explaining AI and Machine Learning
- Dec 12, 2018.
We discuss several explainability techniques being championed today, including LOCO (leave one column out), permutation impact, and LIME (local interpretable model-agnostic explanations).
- Machine Learning & AI Main Developments in 2018 and Key Trends for 2019
- Dec 11, 2018.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.
- Learning Machine Learning vs Learning Data Science
- Dec 11, 2018.
We clarify some important and often-overlooked distinctions between Machine Learning and Data Science, covering education, scalable vs non-scalable jobs, career paths, and more.
- Should you become a data scientist?
- Dec 10, 2018.
An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.
- How Different are Conventional Programming and Machine Learning?
- Dec 10, 2018.
When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.
- Here are the most popular Python IDEs / Editors
- Dec 7, 2018.
We report on the most popular IDE and Editors, based on our poll. Jupyter is the favorite across all regions and employment types, but there is competition for no. 2 and no. 3 spots.
- 6 Step Plan to Starting Your Data Science Career
- Dec 5, 2018.
When people want to launch data science careers but haven't made the first move, they're in a scenario that's understandably daunting and full of uncertainty. Here are six steps to get started.
- Kick Start Your Data Career! Tips From the Frontline
- Dec 5, 2018.
I am going to provide very interesting and useful tips through this blog series which will help students to kick start their career in Data.
- Data Science Projects Employers Want To See: How To Show A Business Impact
- Dec 4, 2018.
The best way to create better data science projects that employers want to see is to provide a business impact. This article highlights the process using customer churn prediction in R as a case-study.
- Why Primary Research?
- Dec 4, 2018.
Primary studies have always been a strength of marketing research. Many younger marketing researchers, however, have only been exposed to standardized ready-made research products or big data. This is a concern. What is the point of the word research in marketing research?
- AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019
- Dec 3, 2018.
Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.
- Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools
- Dec 3, 2018.
We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.
How Much Math do you need in Data Science?
Learning by Forgetting: Deep Neural Networks and the Jennifer Aniston Neuron