- Introduction to Kubeflow MPI Operator and Industry Adoption - Mar 27, 2020.
Kubeflow just announced its first major 1.0 release recently. This post introduces the MPI Operator, one of the core components of Kubeflow, currently in alpha, which makes it easy to run synchronized, allreduce-style distributed training on Kubernetes.
- Deep Learning Breakthrough: a sub-linear deep learning algorithm that does not need a GPU? - Mar 26, 2020.
Deep Learning sits at the forefront of many important advances underway in machine learning. With backpropagation being a primary training method, its computational inefficiencies require sophisticated hardware, such as GPUs. Learn about this recent breakthrough algorithmic advancement with improvements to the backpropgation calculations on a CPU that outperforms large neural network training with a GPU.
- Making sense of ensemble learning techniques - Mar 26, 2020.
This article breaks down ensemble learning and how it can be used for problem solving.
- Diffusion Map for Manifold Learning, Theory and Implementation - Mar 25, 2020.
This article aims to introduce one of the manifold learning techniques called Diffusion Map. This technique enables us to understand the underlying geometric structure of high dimensional data as well as to reduce the dimensions, if required, by neatly capturing the non-linear relationships between the original dimensions.
- KDnuggets™ News 20:n12, Mar 25: 24 Best (and Free) Books To Understand Machine Learning; Coronavirus Daily Change and Poll Analysis; 9 lessons learned during 1st year as a Data Scientist - Mar 25, 2020.
Read our analysis of coronavirus data and poll results; Use your time indoors to learn with 24 best and free books to understand Machine Learning; Study the 9 important lessons from the first year as a Data Scientist; Understand the SVM, a top ML algorithm; check a comprehensive list of AI resources for online learning; and more.
- Made With ML: Discover, build, and showcase machine learning projects - Mar 23, 2020.
This is a short introduction to Made With ML, a useful resource for machine learning engineers looking to get ideas for projects to build, and for those looking to share innovative portfolio projects once built.
- Exploring TensorFlow Quantum, Google’s New Framework for Creating Quantum Machine Learning Models - Mar 23, 2020.
TensorFlow Quantum allow data scientists to build machine learning models that work on quantum architectures.
- 24 Best (and Free) Books To Understand Machine Learning - Mar 20, 2020.
We have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field.
- A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM) - Mar 18, 2020.
Support Vector Machines (SVMs) are powerful for solving regression and classification problems. You should have this approach in your machine learning arsenal, and this article provides all the mathematics you need to know -- it's not as hard you might think.
- KDnuggets™ News 20:n11, Mar 18: Covid-19, your community, and you – a data science perspective; When Will AutoML replace Data Scientists? Poll Results and Analysis - Mar 18, 2020.
A Data Science perspective on Covid-19, the novel coronavirus; The results and analysis of a previous KDnuggets Poll: When Will AutoML replace Data Scientists? How to build a mature Machine Learning team; The Most Useful Machine Learning Tools of 2020; and more.
- Building a Mature Machine Learning Team - Mar 13, 2020.
After spending a lot of time thinking about the paths that software companies take toward ML maturity, this framework was created to follow as you adopt ML and then mature as an organization. The framework covers every aspect of building a team including product, process, technical, and organizational readiness, as well as recognizes the importance of cross-functional expertise and process improvements for bringing AI-driven products to market.
- The Most Useful Machine Learning Tools of 2020 - Mar 13, 2020.
This articles outlines 5 sets of tools every lazy full-stack data scientist should use.
- Decision Boundary for a Series of Machine Learning Models - Mar 13, 2020.
I train a series of Machine Learning models using the iris dataset, construct synthetic data from the extreme points within the data and test a number of Machine Learning models in order to draw the decision boundaries from which the models make predictions in a 2D space, which is useful for illustrative purposes and understanding on how different Machine Learning models make predictions.
- Few-Shot Image Classification with Meta-Learning - Mar 12, 2020.
Here is how you can teach your model to learn quickly from a few examples.
- Google Open Sources TFCO to Help Build Fair Machine Learning Models - Mar 12, 2020.
A new optimization framework helps to incorporate fairness constraints in machine learning models.
- Software Interfaces for Machine Learning Deployment - Mar 11, 2020.
While building a machine learning model might be the fun part, it won't do much for anyone else unless it can be deployed into a production environment. How to implement machine learning deployments is a special challenge with differences from traditional software engineering, and this post examines a fundamental first step -- how to create software interfaces so you can develop deployments that are automated and repeatable.
- 20+ Machine Learning Datasets & Project Ideas - Mar 9, 2020.
Upgrading your machine learning, AI, and Data Science skills requires practice. To practice, you need to develop models with a large amount of data. Finding good datasets to work with can be challenging, so this article discusses more than 20 great datasets along with machine learning project ideas for you to tackle today.
- A Crash Course in Game Theory for Machine Learning: Classic and New Ideas - Mar 9, 2020.
Game theory is experiencing a renaissance driven by the evolution of AI. What are some classic and new ideas that data scientists should be aware of.
- Resources for Women in AI, Data Science, and Machine Learning - Mar 8, 2020.
For the international women's day, we feature resources to help more women enter and succeed in AI, Big Data, Data Science, and Machine Learning fields.
- Phishytics – Machine Learning for Detecting Phishing Websites - Mar 6, 2020.
Since phishing is such a widespread problem in the cybersecurity domain, let us take a look at the application of machine learning for phishing website detection.
- Trends in Machine Learning in 2020 - Mar 5, 2020.
Many industries realize the potential of Machine Learning and are incorporating it as a core technology. Progress and new applications of these tools are moving quickly in the field, and we discuss expected upcoming trends in Machine Learning for 2020.
- A simple and interpretable performance measure for a binary classifier - Mar 4, 2020.
Binary classification tasks are the bread and butter of machine learning. However, the standard statistic for its performance is a mathematical tool that is difficult to interpret -- the ROC-AUC. Here, a performance measure is introduced that simply considers the probability of making a correct binary classification.
- The Augmented Scientist Part 1: Practical Application Machine Learning in Classification of SEM Images - Mar 3, 2020.
Our goal here is to see if we can build a classifier that can identify patterns in Scanning Electron Microscope (SEM) images, and compare the performance of our classifier to the current state-of-the-art.
- 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2) - Mar 2, 2020.
We explain important AI, ML, Data Science terms you should know in 2020, including Double Descent, Ethics in AI, Explainability (Explainable AI), Full Stack Data Science, Geospatial, GPT-2, NLG (Natural Language Generation), PyTorch, Reinforcement Learning, and Transformer Architecture.
- Uber Unveils a New Service for Backtesting Machine Learning Models at Scale - Mar 2, 2020.
The transportation giant built a new service and architecture for backtesting forecasting models.
- Decision Tree Intuition: From Concept to Application - Feb 27, 2020.
While the use of Decision Trees in machine learning has been around for awhile, the technique remains powerful and popular. This guide first provides an introductory understanding of the method and then shows you how to construct a decision tree, calculate important analysis parameters, and plot the resulting tree.
- KDnuggets™ News 20:n08, Feb 26: Gartner 2020 Magic Quadrant for Data Science & Machine Learning Platforms; Will AutoML Replace Data Scientists? - Feb 26, 2020.
This week in KDnuggets: The Death of Data Scientists - will AutoML replace them?; Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms; Hand labeling is the past. The future is #NoLabel AI; The Forgotten Algorithm; Getting Started with R Programming; and much, much more.
- Free Mathematics Courses for Data Science & Machine Learning - Feb 25, 2020.
It's no secret that mathematics is the foundation of data science. Here are a selection of courses to help increase your maths skills to excel in data science, machine learning, and beyond.
- Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 24, 2020.
The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.
- Getting Started with R Programming - Feb 19, 2020.
An end to end Data Analysis using R, the second most requested programming language in Data Science.
- KDnuggets™ News 20:n07, Feb 19: 20 AI, Data Science, Machine Learning Terms for 2020; Why Did I Reject a Data Scientist Job? - Feb 19, 2020.
This week on KDnuggets: 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020; Why Did I Reject a Data Scientist Job?; Fourier Transformation for a Data Scientist; Math for Programmers; Deep Neural Networks; Practical Hyperparameter Optimization; and much more!
- 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1) - Feb 18, 2020.
2020 is well underway, and we bring you 20 AI, data science, and machine learning terms we should all be familiar with as the year marches onward.
- Using AI to Identify Wildlife in Camera Trap Images from the Serengeti - Feb 17, 2020.
With recent developments in machine learning and computer vision, we acquired the tools to provide the biodiversity community with an ability to tap the potential of the knowledge generated automatically with systems triggered by a combination of heat and motion.
- Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything - Feb 17, 2020.
Meena is one of the major milestones in the history of NLU. How did Google build it?
- What Does it Mean to Deploy a Machine Learning Model? - Feb 14, 2020.
You are a Data Scientist who knows how to develop machine learning models. You might also be a Data Scientist who is too afraid to ask how to deploy your machine learning models. The answer isn't entirely straightforward, and so is a major pain point of the community. This article will help you take a step in the right direction for production deployments that are automated, reproducible, and auditable.
- Adversarial Validation Overview - Feb 13, 2020.
Learn how to implement adversarial validation that builds a classifier to determine if your data is from the training or testing sets. If you can do this, then your data has issues, and your adversarial validation model can help you diagnose the problem.
- Practical Hyperparameter Optimization - Feb 13, 2020.
An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.
- Sharing your machine learning models through a common API - Feb 12, 2020.
DEEPaaS API is a software component developed to expose machine learning models through a REST API. In this article we describe how to do it.
- AI and Machine Learning In Our Every Day Life - Feb 7, 2020.
The curiosity and buzz around the most talked-about technology -- Artificial Intelligence -- have experts and technophiles busy decoding its exciting future applications. Of course, the use of AI and machine learning is already pervasive in our daily lives, as we review many of these popular features in this article.
- The Data Science Puzzle — 2020 Edition - Feb 7, 2020.
The data science puzzle is once again re-examined through the relationship between several key concepts of the landscape, incorporating updates and observations since last time. Check out the results here.
- The Future of Machine Learning Will Include a Lot Less Engineering - Feb 6, 2020.
Despite getting less attention, the systems-level design and engineering challenges in ML are still very important — creating something useful requires more than building good models, it requires building good systems.
- Intro to Machine Learning and AI based on high school knowledge - Feb 5, 2020.
Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.
- Optimal Estimation Algorithms: Kalman and Particle Filters - Feb 5, 2020.
An introduction to the Kalman and Particle Filters and their applications in fields such as Robotics and Reinforcement Learning.
- Serverless Machine Learning with R on Cloud Run - Feb 4, 2020.
Expedite the deployment of your machine models using serverless cloud infrastructure. In this tutorial, we explore creating and deploying a model which scraps real time Twitter data and returns interactive visualization using R.
- Why are Machine Learning Projects so Hard to Manage? - Feb 3, 2020.
What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.
- 12-Hour Machine Learning Challenge: Build & deploy an app with Streamlit and DevOps tools - Feb 3, 2020.
This article will present the knowledge, process, tools, and frameworks required for completing a 12-hour ML challenge. I hope you can find it useful for your personal or professional projects.
- Data Validation for Machine Learning - Jan 31, 2020.
While the validation process cannot directly find what is wrong, the process can show us sometimes that there is a problem with the stability of the model.
- Amazon Gets Into the AutoML Race with AutoGluon: Some AutoML Architectures You Should Know About - Jan 30, 2020.
Amazon, Microsoft, Salesforce, Waymo have produced some of the most innovative AutoML architectures in the market.
- Exoplanet Hunting Using Machine Learning - Jan 28, 2020.
Search for exoplanets — those planets beyond our own solar system — using machine learning, and implement these searches in Python.
- Artificial Intelligence Books to Read in 2020 - Jan 21, 2020.
Here are some AI-related books that I’ve read and recommend for you to add to your 2020 reading list!
- The Future of Machine Learning - Jan 17, 2020.
This summary overviews the keynote at TensorFlow World by Jeff Dean, Head of AI at Google, that considered the advancements of computer vision and language models and predicted the direction machine learning model building should follow for the future.
- Classify A Rare Event Using 5 Machine Learning Algorithms - Jan 15, 2020.
Which algorithm works best for unbalanced data? Are there any tradeoffs?
- KDnuggets™ News 20:n02, Jan 15: Top 5 Must-have Data Science Skills; Learn Machine Learning with THIS Book - Jan 15, 2020.
This week: learn the 5 must-have data science skills for the new year; find out which book is THE book to get started learning machine learning; pick up some Python tips and tricks; learn SQL, but learn it the hard way; and find an introductory guide to learning common NLP techniques.
- 7 AI Use Cases Transforming Live Sports Production and Distribution - Jan 14, 2020.
Here are 7 powerful AI led use cases both for linear television and for OTT apps that are transforming the live sports production landscape.
- Graph Machine Learning Meets UX: An uncharted love affair - Jan 13, 2020.
When machine learning tools are developed by technology first, they risk failing to deliver on what users actually need. It can also be difficult for development teams to establish meaningful direction. This article explores the challenges of designing an interface that enables users to visualise and interact with insights from graph machine learning, and explores the very new, uncharted relationship between machine learning and UX.
- The Book to Start You on Machine Learning - Jan 9, 2020.
This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.
- Introducing Generalized Integrated Gradients (GIG): A Practical Method for Explaining Diverse Ensemble Machine Learning Models - Jan 7, 2020.
There is a need for a new way to explain complex, ensembled ML models for high-stakes applications such as credit and lending. This is why we invented GIG.
- Live Webinar: Learn how to build better machine learning pipelines - Jan 6, 2020.
In this webinar, Jan 15 @ 12PM EST, we'll offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. Register now to attend live or to watch a recording afterwards.
- H2O Framework for Machine Learning - Jan 6, 2020.
This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.
- 10 Best and Free Machine Learning Courses, Online - Dec 26, 2019.
Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.
- 5 Ways to Apply Ethics to AI - Dec 19, 2019.
Here are six more lessons based on real life examples that I think we should all remember as people working in machine learning, whether you’re a researcher, engineer, or a decision-maker.
- The Ultimate Guide to Model Retraining - Dec 16, 2019.
Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. So, retraining and redeploying will likely be required. In other words, deployment should be treated as a continuous process. This guide defines model drift and how to identify it, and includes approaches to enable model training.
- Microsoft Introduces Icebreaker to Address the Famous Ice-Start Challenge in Machine Learning - Dec 16, 2019.
The new technique allows the deployment of machine learning models that operate with minimum training data.
- KDnuggets Poll: How well do current AutoML solutions work? - Dec 14, 2019.
Take part in our latest poll, asking readers their opinions on the effectiveness of current automated machine learning solutions.
- Dusting Under the Bed: Machine Learners’ Responsibility for the Future of Our Society - Dec 13, 2019.
The Machine Learning community must shape the world so that AI is built and implemented with a focus on the entire outcome for our society, and not just optimized for accuracy and/or profit.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020 - Dec 11, 2019.
We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment.
- KDnuggets™ News 19:n47, Dec 11: 10 Free Top Notch Machine Learning Courses; AI, Analytics, ML, DS Main Developments and Key Trends - Dec 11, 2019.
We asked top experts: What were the main developments in AI, Data Science, Deep Learning, and Machine Learning Research in 2019, and what key trends do you expect in 2020? Read their answers, and also check 10 Free Top Notch Machine Learning Courses; 4 Hottest Trends in Data Science; The Essential Toolbox for Data Cleaning, and more
- Deployment of Machine learning models using Flask - Dec 10, 2019.
This blog will explain the basics of deploying a machine learning algorithm, focusing on developing a Naïve Bayes model for spam message identification, and using Flask to create an API for that model.
- Scalable graph machine learning: a mountain we can climb? - Dec 10, 2019.
Graph machine learning is a developing area of research that brings many complexities. One challenge that both fascinates and infuriates those working with graph algorithms is — scalability. We take a close look at scalability for graph machine learning methods covering what it is, what makes it difficult, and an example of a method that tackles it head-on.
- 5 Great New Features in Latest Scikit-learn Release - Dec 10, 2019.
From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.
- Moving Predictive Maintenance from Theory to Practice - Dec 9, 2019.
Here are four common hurdles that need to be overcome before tapping into the benefits of predictive maintenance.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 - Dec 9, 2019.
As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.
- 10 Free Top Notch Machine Learning Courses - Dec 6, 2019.
Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
- Why software engineering processes and tools don’t work for machine learning - Dec 5, 2019.
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.
- Artificial Friend or Virtual Foe - Dec 5, 2019.
Is AI making more good than harm?
- Two Years In The Life of AI, Machine Learning, Deep Learning and Java - Nov 29, 2019.
Where does Java stand in the world of artificial intelligence, machine learning, and deep learning? Learn more about how to do these things in Java, and the libraries and frameworks to use.
- A Doomed Marriage of Machine Learning and Agile - Nov 28, 2019.
Sebastian Thrun, the founder of Udacity, ruined my machine learning project and wedding.
- KDnuggets™ News 19:n45, Nov 27: Interpretable vs black box models; Advice for New and Junior Data Scientists - Nov 27, 2019.
This week: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead; Advice for New and Junior Data Scientists; Python Tuples and Tuple Methods; Can Neural Networks Develop Attention? Google Thinks they Can; Three Methods of Data Pre-Processing for Text Classification
- Machine Learning 101: The What, Why, and How of Weighting - Nov 26, 2019.
Weighting is a technique for improving models. In this article, learn more about what weighting is, why you should (and shouldn’t) use it, and how to choose optimal weights to minimize business costs.
- Neural Networks 201: All About Autoencoders - Nov 21, 2019.
Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.
- Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead - Nov 20, 2019.
The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.
- How to apply machine learning and deep learning methods to audio analysis - Nov 19, 2019.
Find out how data scientists and AI practitioners can use a machine learning experimentation platform like Comet.ml to apply machine learning and deep learning to methods in the domain of audio analysis.
- GitHub Repo Raider and the Automation of Machine Learning - Nov 18, 2019.
Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML.
- Tips for a cost-effective machine learning project - Nov 15, 2019.
Spoiler: you don’t need a VM running 24/7 to handle 16 requests a day.
- Testing Your Machine Learning Pipelines - Nov 14, 2019.
Let’s take a look at traditional testing methodologies and how we can apply these to our data/ML pipelines.
- Transfer Learning Made Easy: Coding a Powerful Technique - Nov 13, 2019.
While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.
- Beginners Guide to the Three Types of Machine Learning - Nov 13, 2019.
The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn.
- How I Got Better at Machine Learning - Nov 13, 2019.
Check out this author's collection of tips and tricks that I learned over the years to get better at Machine Learning.
- MLOps for production-level machine learning [Nov 14 Webinar] - Nov 12, 2019.
This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.
- Research Guide: Advanced Loss Functions for Machine Learning Models - Nov 6, 2019.
This guide explores research centered on a variety of advanced loss functions for machine learning models.
- The Last Defense Against Another AI Winter - Nov 6, 2019.
My short answer is this: Yes, another AI Winter will be here if you don’t deploy more ML solutions. You and your Data Science teams are the last line of defense against the AI Winter. You need to solve five key challenges to keep the momentum up.
- Top Machine Learning Software Tools for Developers - Nov 1, 2019.
As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out.
- MLOps for production-level machine learning - Nov 1, 2019.
This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.
- What is Machine Learning on Code? - Nov 1, 2019.
Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.
- How to Build Your Own Logistic Regression Model in Python - Oct 31, 2019.
A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.
- Why is Machine Learning Deployment Hard? - Oct 29, 2019.
Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work.
- How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow - Oct 29, 2019.
In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.
- How Bayes’ Theorem is Applied in Machine Learning - Oct 28, 2019.
Learn how Bayes Theorem is in Machine Learning for classification and regression!
- DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models - Oct 28, 2019.
Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset.
- Feature Selection: Beyond feature importance? - Oct 24, 2019.
In this post, you will see 3 different techniques of how to do Feature Selection to your datasets and how to build an effective predictive model.
- Intro to Adversarial Machine Learning and Generative Adversarial Networks - Oct 23, 2019.
In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning.
- Building an intelligent Digital Assistant - Oct 18, 2019.
In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.
- How to Easily Deploy Machine Learning Models Using Flask - Oct 17, 2019.
This post aims to make you get started with putting your trained machine learning models into production using Flask API.
- The 5 Classification Evaluation Metrics Every Data Scientist Must Know - Oct 16, 2019.
This post is about various evaluation metrics and how and when to use them.
- KDnuggets™ News 19:n39, Oct 16: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI - Oct 16, 2019.
This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.
- 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.
- Upcoming Webinar, Machine Learning Vital Signs: Metrics and Monitoring Models in Production - Oct 11, 2019.
In this upcoming webinar on Oct 23 @ 10 AM PT, learn why you should invest time in monitoring your machine learning models, the dangers of not paying attention to how a model’s performance can change over time, metrics you should be gathering for each model and what they tell you, and much more.
- There is No Such Thing as a Free Lunch - 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.
- 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.
- Data Science is Boring (Part 2) - Oct 9, 2019.
Why I love boring ML problems and how I think about them.
- 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.
- Data Preparation for Machine learning 101: Why it’s important and how to do it - Oct 2, 2019.
As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models. Let’s begin.
- Will Machine Learning End Retail? Data Science Seattle Oct 17, 2019 - Sep 30, 2019.
In advance of the Data Science Salon taking place in Seattle on Oct 17, we asked our speakers to shed some light on how Artificial Intelligence and Machine Learning are impacting one of America’s most disruptive industries. Read for more insight, and then register with KDnuggets exclusive link for 20% off tickets.
- Webinar: Build auto-adaptive machine learning models with Kubernetes - Sep 27, 2019.
This live webinar, Oct 2 2019, will instruct data scientists and machine learning engineers how to build manage and deploy auto-adaptive machine learning models in production. Save your spot now.
- What is Hierarchical Clustering? - Sep 27, 2019.
The article contains a brief introduction to various concepts related to Hierarchical clustering algorithm.
- Data Mapping Using Machine Learning - Sep 27, 2019.
Data mapping is a way to organize various bits of data into a manageable and easy-to-understand system.
- Beyond Explainability: A Practical Guide to Managing Risks in Machine Learning Models - Sep 20, 2019.
This white paper provides the first-ever standard for managing risk in AI and ML, focusing on both practical processes and technical best practices “beyond explainability” alone. Download now.
- Automate Hyperparameter Tuning for Your Models - Sep 20, 2019.
When we create our machine learning models, a common task that falls on us is how to tune them. So that brings us to the quintessential question: Can we automate this process?
- Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning - Sep 19, 2019.
While mature algorithms and extensive open-source libraries are widely available for machine learning practitioners, sufficient data to apply these techniques remains a core challenge. Discover how to leverage scikit-learn and other tools to generate synthetic data appropriate for optimizing and fine-tuning your models.
- Applying Data Science to Cybersecurity Network Attacks & Events - Sep 19, 2019.
Check out this detailed tutorial on applying data science to the cybersecurity domain, written by an individual with backgrounds in both fields.
- 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python - Sep 19, 2019.
“I want to learn machine learning and artificial intelligence, where do I start?” Here.
- Data Science is Boring (Part 1) - Sep 18, 2019.
Read about how one data scientist copes with his boring days of deploying machine learning.
- Which Data Science Skills are core and which are hot/emerging ones? - Sep 17, 2019.
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.
- Explore the world of Bioinformatics with Machine Learning - Sep 17, 2019.
The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.
- Cartoon: Unsupervised Machine Learning? - Sep 14, 2019.
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 - Sep 13, 2019.
While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business.
- Version Control for Data Science: Tracking Machine Learning Models and Datasets - Sep 13, 2019.
I am a Git god, why do I need another version control system for Machine Learning Projects?
- There is No Free Lunch in Data Science - Sep 12, 2019.
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.
- Ensemble Methods for Machine Learning: AdaBoost - Sep 12, 2019.
It turned out that, if we ask the weak algorithm to create a whole bunch of classifiers (all weak for definition), and then combine them all, what may figure out is a stronger classifier.
- A Friendly Introduction to Support Vector Machines - Sep 12, 2019.
This article explains the Support Vector Machines (SVM) algorithm in an easy way.