- [PDF] Executive Guide To Machine Learning - Mar 13, 2019.
The Executive Guide covers the benefits to your business, the build-or-buy process, and gives a practical overview for implementing ML in your organization.
- Towards Automatic Text Summarization: Extractive Methods - Mar 13, 2019.
The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.
- AI: Arms Race 2.0 - Mar 12, 2019.
An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.
- Monash: Research Fellow [Clayton, Australia] - Mar 9, 2019.
The Data Science and AI group is seeking a go-getter Research Fellow to work at the interface of computer science, machine learning and medical research. Apply by April 4, 2019.
- Automated Machine Learning 101: Is Your Company Ready? - Mar 8, 2019.
In this webinar from DataRobot, learn common automated machine learning use cases how automated machine learning enables more employees to take part in AI initiatives while making existing data science teams more productive, and more!
- Beating the Bookies with Machine Learning - Mar 8, 2019.
We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.
- 19 Inspiring Women in AI, Big Data, Data Science, Machine Learning - Mar 8, 2019.
For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.
- Designing Ethical Algorithms - Mar 8, 2019.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis - Mar 7, 2019.
CRM/Consumer analytics, health care, banking, finance, and science were the top sectors in 2018. The greatest increases were in mobile apps, investment, security, entertainment, and social policy, while fraud detection, retail, advertising, direct marketing, and social media saw the greatest declines.
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- Another 10 Free Must-Read Books for Machine Learning and Data Science - Mar 6, 2019.
Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.
- KDnuggets™ News 19:n10, Mar 6: What no one will tell you about data science job applications; The rise of ML Engineering - Mar 6, 2019.
Also most impactful AI trends of 2018: The rise of ML Engineering; How to do Everything in Computer Vision; GANs Need Some Attention, Too; OpenAI GPT-2.
- GANs Need Some Attention, Too - Mar 5, 2019.
Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.
- Most impactful AI trends of 2018: The rise of ML Engineering - Mar 1, 2019.
As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018’s foundation and truly blossom in 2019.
- [Webinar] Managing the Complete Machine Learning Lifecycle - Feb 28, 2019.
Join Databricks Mar 7, 2019, to learn how using MLflow can help you keep track of experiment runs and results across frameworks, execute projects remotely on to a Databricks cluster, and quickly reproduce your runs, and more. Sign up for this webinar now.
- Join the future of AI and Data at DATAx San Francisco this May with Microsoft, Google and so many more - Feb 27, 2019.
Join us as we bring you the leading innovations and insights to the fast-paced world of AI & Data from Machine Learning, Healthcare, Marketing, Gaming analytics.
- Acquiring Labeled Data to Train Your Models at Low Costs - Feb 27, 2019.
We discuss groundbreaking and unique methods to acquire labeled data at low cost, including 3rd-Party Plug-and-Play AI Model, Zero-Shot Learning, and Restructuring the Existing Data Set.
- 4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.
- Where did you apply Analytics, Data Science, Machine Learning in 2018? - Feb 25, 2019.
Where did you apply Analytics, Machine Learning, and Data Science in 2018? Take part in the latest KDnuggets poll to share your input, and see what others have to say.
- What are Some “Advanced” AI and Machine Learning Online Courses? - Feb 22, 2019.
Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?
- Artificial Neural Network Implementation using NumPy and Image Classification - Feb 21, 2019.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
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- State of the art in AI and Machine Learning – highlights of papers with code - Feb 20, 2019.
We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.
- How to Setup a Python Environment for Machine Learning - Feb 18, 2019.
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
- The Persuasion Paradox – How Computers Optimize their Influence on You - Feb 16, 2019.
How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence, considering it's so important? In this season finale episode, Eric Siegel introduces machine learning methods designed to persuade.
- Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
- Accelerating Time Series Analysis with Automated Machine Learning - Feb 14, 2019.
This IDC Solution Spotlight examines how automated machine learning tools can augment the analysis, modeling, and prediction of time series data to deliver easily understood and actionable insights for businesses in a simple and agile fashion. Get the report now.
- An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.
- KDnuggets™ News 19:n07, Feb 13: The Best and Worst Data Visualizations of 2018; Gartner 2019 Magic Quadrant for Data Science Platforms - Feb 13, 2019.
Also: Data-science? Agile? Cycles?; How I used NLP (Spacy) to screen Data Science Resumes; Neural Networks - an Intuition; A Quick Guide to Feature Engineering; Understanding Gradient Boosting Machines
- Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.
- How to Adopt Machine Learning: Interviews with Technical & Business Leaders - Feb 11, 2019.
This 8 chapter series includes interviews with technical and business leaders from a number of large companies with the aim to help you adopt machine learning in your organization.
- QCon.ai San Francisco: Applied AI Software Conference for Developers – KDnuggets Offer - Feb 8, 2019.
QCon.ai is a three-day conference focused on the major machine learning and AI software trends affecting software engineers today. Register by Feb 23 with code "KDN" and save.
- 10 Trending Data Science Topics at ODSC East 2019 - Feb 7, 2019.
ODSC East 2019, Boston, Apr 30 - May 3, will host over 300+ of the leading experts in data science and AI. Here are a few standout topics and presentations in this rapidly evolving field. Register for ODSC East at 50% off till Feb 8.
- Neural Networks – an Intuition - Feb 7, 2019.
Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.
- The Essential Data Science Venn Diagram - Feb 4, 2019.
A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.
- Five Ways Your Safety Depends on Machine Learning - Feb 2, 2019.
Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.
- KDnuggets™ News 19:n05, Jan 30: Your AI skills are worth less than you think; 7 Steps to Mastering Basic Machine Learning - Jan 30, 2019.
Also: Logistic Regression: A Concise Technical Overview; AI is a Big Fat Lie; How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy; Airbnb Rental Listings Dataset Mining; Data Science Project Flow for Startups
- The Algorithms Aren’t Biased, We Are - Jan 29, 2019.
We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”
- 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition - Jan 29, 2019.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!
- Machine Learning Security - Jan 25, 2019.
We take a look at how malicious actors can break machine learning models and what some of the best practices are when it comes to stopping them.
- How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy - Jan 23, 2019.
We explain how to retrieve estimates of a model's performance using scoring metrics, before taking a look at finding and diagnosing the potential problems of a machine learning algorithm.
- Logistic Regression: A Concise Technical Overview - Jan 23, 2019.
Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.
- What were the most significant machine learning/AI advances in 2018? - Jan 22, 2019.
2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights.
- How to Monitor Machine Learning Models in Real-Time - Jan 18, 2019.
We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.
- Automated Machine Learning in Python - Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
- Comparing Machine Learning Models: Statistical vs. Practical Significance - Jan 18, 2019.
Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.
- The Hundred-Page Machine Learning Book - Jan 17, 2019.
This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.
- Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples - Jan 17, 2019.
We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.
- How to build an API for a machine learning model in 5 minutes using Flask - Jan 17, 2019.
Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.
- KDnuggets™ News 19:n03, Jan 16: Top 10 Books on NLP and Text Analysis; End To End Guide For Machine Learning Projects - Jan 16, 2019.
Also: Why Vegetarians Miss Fewer Flights - Five Bizarre Insights from Data; 4 Myths of Big Data and 4 Ways to Improve with Deep Data; The Role of the Data Engineer is Changing; How to solve 90% of NLP problems: a step-by-step guide
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
- Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated - Jan 14, 2019.
Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
- End To End Guide For Machine Learning Projects - Jan 14, 2019.
Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.
- Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data - Jan 12, 2019.
A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.
- The year in AI/Machine Learning advances: Xavier Amatriain 2018 Roundup - Jan 11, 2019.
A summary of the main machine learning advances from 2018, including AI hype cooling down, interpretability, deep learning, NLP, and more.
- Top KDnuggets tweets, Jan 02-08: 10 Free Must-Read Books for Machine Learning and Data Science - Jan 9, 2019.
Also: Papers with Code: A Fantastic GitHub Resource; Most Recommended #DataScience and #MachineLearning Books by Top MS programs;10 More Free Must-Read Books for ML and DS
- [Webinar] Accelerating Machine Learning on Databricks - Jan 9, 2019.
In this webinar, we will cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning.
- 4 Myths of Big Data and 4 Ways to Improve with Deep Data - Jan 9, 2019.
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.
- Core Principles of Sustainable Data Science, Machine Learning and AI Product Development: Research as a core driver - Jan 9, 2019.
Regardless of the size of your organisation, if you are developing machine learning or AI products, the core asset you have is a research professional, data scientist or AI scientist, regardless of their academic background.
- KDnuggets™ News 19:n02, Jan 9: The cold start problem: how to build your machine learning portfolio; 5 Best Data Visualization Libraries - Jan 9, 2019.
Learn how to bootstrap your Machine Learning portfolio, which data visualization libraries to use, main approaches to ensemble learning, how to do text summarization, and check our special offers for leading analytics, AI, and Data Science events below.
- Math for Machine Learning - Jan 4, 2019.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
- The cold start problem: how to build your machine learning portfolio - Jan 4, 2019.
This post outlines what makes a good machine leaning portfolio, with useful examples to help you begin to understand the type of project that gets noticed by big companies.
- What to do when your training and testing data come from different distributions - Jan 4, 2019.
However, sometimes only a limited amount of data from the target distribution can be collected. It may not be sufficient to build the needed train/dev/test sets. What to do in such a case? Let us discuss some ideas!
- Improve your AI and Machine Learning skills at AI NEXTCon in Seattle, Jan 23-27 - Jan 3, 2019.
If you are a developer looking to hone your skills, a tech lead and manager to learn latest AI tech that apply to your engineering teams to innovate products and services, or someone who just wants to learn more about the AI industry that's re-shaping the tech world, the AI NEXTCon is right for you.
- Ensemble Learning: 5 Main Approaches - Jan 3, 2019.
We outline the most popular Ensemble methods including bagging, boosting, stacking, and more.
- KDnuggets™ News 19:n01, Jan 3: The Essence of Machine Learning; A Guide to Decision Trees for Machine Learning and Data Science - Jan 3, 2019.
Also: 10 More Must-See Free Courses for Machine Learning and Data Science; Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning; Feature engineering, Explained; Papers with Code: A Fantastic GitHub Resource for Machine Learning; BERT: State of the Art NLP Model, Explained
- 3 More Google Colab Environment Management Tips - Jan 2, 2019.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
- Papers with Code: A Fantastic GitHub Resource for Machine Learning - Dec 31, 2018.
Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out.
- 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?
- 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.
- Synthetic Data Generation: A must-have skill for new data scientists - Dec 27, 2018.
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
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- A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.
- Six Steps to Master Machine Learning with Data Preparation - Dec 21, 2018.
To prepare data for both analytics and machine learning initiatives teams can accelerate machine learning and data science projects to deliver an immersive business consumer experience that accelerates and automates the data-to-insight pipeline by following six critical steps.
- 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.
- 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
- Improve ML transparency without sacrificing accuracy - Dec 19, 2018.
O'Reilly begins to shed some light on the accuracy/complexity tradeoff in machine learning, with An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI. Get the ebook now!
- 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.
- KDnuggets™ News 18:n48, Dec 19: Why You Shouldn’t be a Data Science Generalist; Industry Data Science & Machine Learning 2019 Predictions - Dec 19, 2018.
Also: Top Stories of 2018; NLP Breakthrough Imagenet Moment has arrived; Four Approaches to Explaining AI and Machine Learning; Solve any Image Classification Problem Quickly and Easily
- 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.
- Four Real-Life Machine Learning Use Cases - Dec 13, 2018.
This ebook will walk you through four use cases for Machine Learning on Databricks, covering loan risk, advertising analytics and predictive use case, market basket analysis, suspicious behaviour identification in video use, and more.
- 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).
- KDnuggets™ News 18:n47, Dec 12: Common mistakes when doing machine learning; Here are the most popular Python IDEs / Editors - Dec 12, 2018.
Common mistakes when carrying out machine learning and data science; Most popular Python IDEs/Editors; Machine Learning / AI Main Developments in 2018 and Key Trends for 2019; Machine Learning Project checklist.
- A Machine Learning Deep Dive [Webinar, Dec 13] - Dec 11, 2018.
Learn how ShopRunner uses Databricks on AWS and Snowflake to tackle data science problems across personalization, recommendations, targeting, and analysis of text and images.
- 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.
- P&G: Data Scientist – Machine Learning/NLP [Cincinnati, OH] - Dec 11, 2018.
P&G is seeking a Data Scientist - Machine Learning/NLP in Cincinnati, OH. In this role you will have multiple projects on which you will leverage machine learning tools to solve these types of problems.
- 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.
- Math for Machine Learning - Dec 10, 2018.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
- 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.
- Dr. Data Show Video: Five Reasons Computers Predict When You’ll Die - Dec 8, 2018.
Death prediction is a very morbid topic indeed — but a very practical one. So how does it work? What technological sorcery is at play? And how accurate is it? And, gee whiz, what's it used for?
- XGBoost on GPUs: Unlocking Machine Learning Performance and Productivity - Dec 7, 2018.
On Dec 18, 11:00 AM PT, join NVIDIA for a technical deep dive into GPU-accelerated machine learning, to exploring the benefits of XGBoost on GPUs and much more.
- A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more - Dec 7, 2018.
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.
- The Machine Learning Project Checklist - Dec 7, 2018.
In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."
- DATAx Presents: AI AND MACHINE LEARNING TRENDS IN 2019 - Dec 6, 2018.
This free Ebook from DATAx offers advice on using AI and machine learning to enhance customer satisfaction, how chief data officers are taking the reins on AI strategy, successful case studies from across the business, and more.
- Common mistakes when carrying out machine learning and data science - Dec 6, 2018.
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!
- Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies - Dec 6, 2018.
The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.
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- KDnuggets™ News 18:n46, Dec 5: AI, Data Science, Analytics 2018 Main Developments, 2019 Key Trends; Deep Learning Cheat Sheets - Dec 5, 2018.
Also: Best Machine Learning languages, Data Visualization Tools, DL Frameworks, and Big Data Tools; How to Build a Machine Learning Team When You Are Not Google or Facebook; A Complete Guide to Choosing the Best Machine Learning Course; Handling Imbalanced Datasets in Deep Learning
- Graph-Powered Machine Learning - Dec 3, 2018.
This book from Manning Publications is a wonderful introduction to graphs for machine learning enthusiasts, as well as a great entrée into machine learning for graph experts.
- 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.
- Interpretability is crucial for trusting AI and machine learning - Nov 30, 2018.
We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.
- A Complete Guide to Choosing the Best Machine Learning Course - Nov 30, 2018.
A collection of the best courses covering machine learning concepts and techniques, including supervised and unsupervised learning, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.
- Deep Learning for the Masses (… and The Semantic Layer) - Nov 30, 2018.
Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.
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- Variational Autoencoders Explained in Detail - Nov 30, 2018.
We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.
- How to Build a Machine Learning Team When You Are Not Google or Facebook - Nov 28, 2018.
If you don’t have a clear application for machine learning, you’re going to regret your investment. We provide tips on how to go about setting up your machine learning team - no matter the size of your business.
- Making Machine Learning Accessible [Webinar Replay] - Nov 27, 2018.
Learn the business "why" and technical "how" for implementing machine learning in your organization - watch now.
- Bringing Machine Learning Research to Product Commercialization - Nov 27, 2018.
In this blog post I want to share some of the insights into the differences between academia and industry when applying deep learning to real-world problems as we experienced them at Merantix over the last two years.
- KDnuggets™ News 18:n44, Nov 21: What is the Best Python IDE for Data Science?; Anticipating the next move in data science - Nov 21, 2018.
Also: Mastering The New Generation of Gradient Boosting; Top 10 Python Data Science Libraries; Predictive Analytics in 2018: Salaries & Industry Shifts; Sorry I didn't get that! How to understand what your users want; Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU
- Machine Learning in Action: Going Beyond Decision Support Data Science - Nov 20, 2018.
In order to disrupt business, machine learning models must adopt a product-focused approach, which is a much more significant undertaking.
- How Important is that Machine Learning Model be Understandable? We analyze poll results - Nov 19, 2018.
About 85% of respondents said it was always or frequently important that Machine Learning model be understandable. This was is especially important for academic researchers, and surprisingly more in US/Canada than in Europe or Asia.
- What I Learned About Machine Learning at ODSC West 2018 - Nov 19, 2018.
Reflecting back on the ODSC West 2018 conference, with a review of some of the best talks on topics including active learning, interactive coefficient plots, time-series forecasting, and more.
- How to Choose an AI Vendor - Nov 16, 2018.
This report explores why it is so challenging to choose an AI vendor and what you should consider as you seek a partner in AI. Download now.
- Mastering The New Generation of Gradient Boosting - Nov 15, 2018.
Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.
- [Download] Real-Life ML Examples + Notebooks - Nov 13, 2018.
In this eBook, we will walk you through four Machine Learning use cases on Databricks: Loan Risk Use Case; Advertising Analytics & Prediction Use Case; Market Basket Analysis Problem at Scale; Suspicious Behavior Identification in Video Use Case. Get your copy now!
- The ultimate guide to starting AI - Nov 13, 2018.
A step-by-step overview of how to begin your project, including advice on how to craft a wise performance metric, setting up testing criteria to overcome human bias, and more.
- Machine Learning Toronto Summit
Nov 20-21 – Special KDnuggets discount - Nov 12, 2018.
The Toronto Machine Learning Summit takes place Nov 20-21. Register to celebrate Canada's top AI Research, and enjoy a 30% off -Special Discount with code KDNUGGETS. Register now!
- Dr. Data Show Video: What the Hell Does “Data Science” Really Mean? - Nov 10, 2018.
The latest episode of the Dr. Data Show answers the question, "What the hell is data science?"
- 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
- 7 Best Practices for Machine Learning on a Data Lake - Nov 7, 2018.
Download this report to learn about the data requirements for advanced analytics on a data lake, and best practices such analytics with a focus on machine learning.
- Quantum Machine Learning: A look at myths, realities, and future projections - Nov 5, 2018.
An overview of quantum computing and quantum algorithm design, including current state of the hardware and algorithm design within the existing systems.
- Machine Learning Classification: A Dataset-based Pictorial - Nov 5, 2018.
In order to relate machine learning classification to the practical, let's see how this concept plays out, step by step (and with images), specifically in direct relation to a dataset.
- Learn how machine learning is transforming business, Nov 12 Webinar - Nov 2, 2018.
In this webinar on Nov 12, titled How to Transform Your Business with Automated Machine Learning, learn the difference between AI, machine learning, and deep learning, the challenges of implementing traditional data science solutions, and how automated machine learning enables more employees to take part in AI initiatives.
- How Machines Understand Our Language: An Introduction to Natural Language Processing - Oct 31, 2018.
The applications of NLP are endless. This is how a machine classifies whether an email is spam or not, if a review is positive or negative, and how a search engine recognizes what type of person you are based on the content of your query to customize the response accordingly.
- In-Depth Training for the Future of Data, Orlando, Nov 11-16 – Save with code KD30 - Oct 30, 2018.
Planning and implementing new data and analytics initiatives can be overwhelming. Join us in Orlando for in-depth training that will give you the needed skills and use code KD30 thru Nov 7 to save.
- New Poll: How Important is Understanding Machine Learning Models? - Oct 30, 2018.
New KDnuggets poll is asking: When building Machine Learning / Data Science models in 2018, how often was it important that the model be humanly understandable/explainable? Please vote
- Moody’s Analytics: Machine Learning / NLP – Research Scientist / Engineer [New York, NY] - Oct 30, 2018.
Moody's Analytics is seeking a Machine Learning / NLP - Research Scientist / Engineer in New York, NY, to drive algorithmic improvements by enabling new automation capabilities, and improving efficiency and performance across multiple business lines.
- Naive Bayes from Scratch using Python only – No Fancy Frameworks - Oct 25, 2018.
We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.
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- Implementing Automated Machine Learning Systems with Open Source Tools - Oct 25, 2018.
What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.
- Top KDnuggets tweets, Oct 17-23: Machine Learning Cheat Sheets - Oct 24, 2018.
Also 5 "Clean Code" Tips That Will Dramatically Improve Your Productivity; Data Science Cheat Sheet.
- New Book: Linear Algebra – what you need for Machine Learning and Data Science now - Oct 24, 2018.
From machine learning and data science to engineering and finance, linear algebra is an important prerequisite for the careers of today and of the future. Learn the math you need with this book.
- U. of Zurich: Assistant Professorship in AI and Machine Learning (Non-tenure Track) [Zurich, Switzerland] - Oct 24, 2018.
Candidates should hold a Ph.D. degree in Computer Science with specialization in Machine Learning and/or Data Mining including Deep Learning, lnteractive/ Cooperative Learning, statistical learning or privacy preserving modeling, and have an excellent record of academic achievements in the relevant fields.