Machine Learning (1856)

• Implementing MLOps on an Edge Device - Aug 4, 2020.
  
  This article introduces developers to MLOps and strategies for implementing MLOps on edge devices.

  Edge Analytics, Machine Learning, MLOps, Speech Recognition, Workflow

• Setting Up Your Data Science & Machine Learning Capability in Python - Aug 4, 2020.
  
  With the rich and dynamic ecosystem of Python continuing to be a leading programming language for data science and machine learning, establishing and maintaining a cost-effective development environment is crucial to your business impact. So, do you rent or buy? This overview considers the hidden and obvious factors involved in selecting and implementing your Python platform.

  Cloud Computing, Data Science, Machine Learning, Python, Saturn Cloud

• Announcing PyCaret 2.0 - Aug 3, 2020.
  
  PyCaret 2.0 has been released! Find out about all of the updates and see examples of how to use them right here.

  Machine Learning, PyCaret, Python

• The Machine Learning Field Guide - Aug 3, 2020.
  
  This straightforward guide offers a structured overview of all machine learning prerequisites needed to start working on your project, including the complete data pipeline from importing and cleaning data to modelling and production.

  Data Preparation, Machine Learning, Pandas, Predictive Modeling, Python

• Awesome Machine Learning and AI Courses - Jul 30, 2020.
  
  Check out this list of awesome, free machine learning and artificial intelligence courses with video lectures.

  AI, Courses, Machine Learning

• A Tour of End-to-End Machine Learning Platforms - Jul 29, 2020.
  
  An end-to-end machine learning platform needs a holistic approach. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

  AirBnB, Data Science Platform, Google, Machine Learning, MLOps, Netflix, Pipeline, Uber, Workflow

• Why You Should Get Google’s New Machine Learning Certificate - Jul 29, 2020.
  
  Google is offering a new ML Engineer certificate, geared towards professionals who want to display their competency in topics like distributed model training and scaling to production. Is it worth it?

  Certificate, Courses, Google, Machine Learning

• Essential Resources to Learn Bayesian Statistics - Jul 28, 2020.
  
  If you are interesting in becoming better at statistics and machine learning, then some time should be invested in diving deeper into Bayesian Statistics. While the topic is more advanced, applying these fundamentals to your work will advance your understanding and success as an ML expert.

  Bayesian, Machine Learning, Markov Chain, Statistics

• Labelling Data Using Snorkel - Jul 24, 2020.
  
  In this tutorial, we walk through the process of using Snorkel to generate labels for an unlabelled dataset. We will provide you examples of basic Snorkel components by guiding you through a real clinical application of Snorkel.

  Data Labeling, Data Science, Deep Learning, Machine Learning, NLP, Python

• KDnuggets™ News 20:n28, Jul 22: Data Science MOOCs are too Superficial; The Bitter Lesson of Machine Learning - Jul 22, 2020.
  
  Data Science MOOCs are too Superficial; The Bitter Lesson of Machine Learning; Building a REST API with Tensorflow Serving (Part 1); 3 Advanced Python Features You Should Know; Understanding How Neural Networks Think;

  API, Data Science, Machine Learning, MOOC, Neural Networks, Python, Richard Sutton, TensorFlow

• What I learned from looking at 200 machine learning tools - Jul 21, 2020.
  
  While hundreds of machine learning tools are available today, the ML software landscape may still be underdeveloped with more room to mature. This review considers the state of ML tools, existing challenges, and which frameworks are addressing the future of machine learning software.

  Data Science Platform, Data Science Tools, Machine Learning, MLOps, Open Source, Tools

• Data Mining and Machine Learning: Fundamental Concepts and Algorithms: The Free eBook - Jul 21, 2020.
  
  The second edition of Data Mining and Machine Learning: Fundamental Concepts and Algorithms is available to read freely online, and includes a new part on regression with chapters on linear regression, logistic regression, neural networks, deep learning and regression assessment.

  Algorithms, Data Mining, Free ebook, Machine Learning

• Wrapping Machine Learning Techniques Within AI-JACK Library in R - Jul 17, 2020.
  
  The article shows an approach to solving problem of selecting best technique in machine learning. This can be done in R using just one library called AI-JACK and the article shows how to use this tool.

  Automated Machine Learning, AutoML, Machine Learning, Modeling, R

• Understanding How Neural Networks Think - Jul 16, 2020.
  
  A couple of years ago, Google published one of the most seminal papers in machine learning interpretability.

  Google, Interpretability, Machine Learning

• The Bitter Lesson of Machine Learning - Jul 15, 2020.
  
  Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.

  AI, AlphaGo, Chess, Machine Learning, Reinforcement Learning, Richard Sutton, Scalability, Trends

• 5 Things You Don’t Know About PyCaret - Jul 9, 2020.
  
  In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few words only.

  Machine Learning, PyCaret, Python

• Some Things Uber Learned from Running Machine Learning at Scale - Jul 7, 2020.
  
  Uber machine learning runtime Michelangelo has been in operation for a few years. What has the Uber team learned?

  Machine Learning, Scalability, Uber

• 5th International Summer School 2020 on Resource-aware Machine Learning (REAML) - Jul 7, 2020.
  
  The Resource-aware Machine Learning summer school provides lectures on the latest research in machine learning, with the twist on resource consumption and how these can be reduced. This year it will be held online between 31st of August and 4th of September, and is free of charge. Register now.

  Machine Learning, Online Education, Resource-aware, Summer School, TU Dortmund

• Deploy Machine Learning Pipeline on AWS Fargate - Jul 3, 2020.
  
  A step-by-step beginner’s guide to containerize and deploy ML pipeline serverless on AWS Fargate.

  AWS, Docker, Kubernetes, Machine Learning, Pipeline, PyCaret

• KDnuggets™ News 20:n25, Jun 24: PyTorch Fundamentals You Should Know; Free Math Courses to Boost Your Data Science Skills - Jun 24, 2020.
  
  A Classification Project in Machine Learning: a gentle step-by-step guide; Crop Disease Detection Using Machine Learning and Computer Vision; Bias in AI: A Primer; Machine Learning in Dask; How to Deal with Missing Values in Your Dataset

  Agriculture, Computer Vision, Courses, Data Science, Machine Learning, Mathematics, PyTorch, Tom Fawcett

• Machine Learning in Dask - Jun 22, 2020.
  
  In this piece, we’ll see how we can use Dask to work with large datasets on our local machines.

  Dask, Machine Learning, Pandas, Python

• Graph Machine Learning in Genomic Prediction - Jun 19, 2020.
  
  This work explores how genetic relationships can be exploited alongside genomic information to predict genetic traits with the aid of graph machine learning algorithms.

  Genomics, Graphs, Machine Learning, Prediction

• modelStudio and The Grammar of Interactive Explanatory Model Analysis - Jun 19, 2020.
  
  modelStudio is an R package that automates the exploration of ML models and allows for interactive examination. It works in a model agnostic fashion, therefore is compatible with most of the ML frameworks.

  Analysis, Explainability, Interpretability, Machine Learning, R

• LightGBM: A Highly-Efficient Gradient Boosting Decision Tree - Jun 18, 2020.
  
  LightGBM is a histogram-based algorithm which places continuous values into discrete bins, which leads to faster training and more efficient memory usage. In this piece, we’ll explore LightGBM in depth.

  Decision Trees, Gradient Boosting, Machine Learning, Python

• Tom Fawcett, in memoriam - Jun 17, 2020.
  
  Foster Provost in memoriam for Tom Fawcett, killed on June 4th in a freak bicycle accident. Tom was a brilliant scholar, a selfless collaborator, a substantial contributor to Data Science for three decades, and a unique individual.

  Foster Provost, History, Machine Learning, Tom Fawcett

• A Classification Project in Machine Learning: a gentle step-by-step guide - Jun 17, 2020.
  
  Classification is a core technique in the fields of data science and machine learning that is used to predict the categories to which data should belong. Follow this learning guide that demonstrates how to consider multiple classification models to predict data scrapped from the web.

  Beginners, Classification, Machine Learning

• Best Machine Learning Youtube Videos Under 10 Minutes - Jun 16, 2020.
  
  The Youtube videos on this list cover concepts such as what machine learning is, the basics of natural language processing, how computer vision works, and machine learning in video games.

  Machine Learning, Video, Youtube

• Uber’s Ludwig is an Open Source Framework for Low-Code Machine Learning - Jun 15, 2020.
  
  The new framework allow developers with minimum experience to create and train machine learning models.

  Low-Code, Machine Learning, No-Code, Open Source, Uber

• Understanding Machine Learning: The Free eBook - Jun 15, 2020.
  
  Time to get back to basics. This week we have a look at a book on foundational machine learning concepts, Understanding Machine Learning: From Theory to Algorithms.

  Algorithms, Book, Free ebook, Machine Learning

• Deploy a Machine Learning Pipeline to the Cloud Using a Docker Container - Jun 12, 2020.
  
  In this tutorial, we will use a previously-built machine learning pipeline and Flask app to demonstrate how to deploy a machine learning pipeline as a web app using the Microsoft Azure Web App Service.

  Cloud, Docker, Machine Learning, Pipeline, PyCaret, Python

• Upgrading the Brand Mobile App with Machine Learning - Jun 11, 2020.
  
  The tech progress in mobile app development, as well as digital enhancements, have created new chances for brands to allure and retain customers. In bridging the individualization gap, Machine Learning comes to the rescue.

  App, Machine Learning, Mobile

• How to make AI/Machine Learning models resilient during COVID-19 crisis - Jun 11, 2020.
  
  COVID-19-driven concept shift has created concern over the usage of AI/ML to continue to drive business value following cases of inaccurate outputs and misleading results from a variety of fields. Data Science teams must invest effort in post-model tracking and management as well as deploy an agility in the AI/ML process to curb problems related to concept shift.

  AI, Coronavirus, COVID-19, Machine Learning, Model Drift, Modeling

• Nitpicking Machine Learning Technical Debt - Jun 8, 2020.
  
  Technical Debt in software development is pervasive. With machine learning engineering maturing, this classic trouble is unsurprisingly rearing its ugly head. These 25 best practices, first described in 2015 and promptly overshadowed by shiny new ML techniques, are updated for 2020 and ready for you to follow -- and lead the way to better ML code and processes in your organization.

  Pages: 1 2

  Best Practices, DevOps, Explainability, Interpretability, Machine Learning, Monitoring, Pipeline, Technical Debt, Version Control

• Why Do AI Systems Need Human Intervention to Work Well? - Jun 5, 2020.
  
  All is not well with artificial intelligence-based systems during the coronavirus pandemic. No, the virus does not impact AI – however, it does impact humans, without whom AI and ML systems cannot function properly. Surprised?

  AI, Coronavirus, Humans, Machine Learning, Watson

• Upcoming Webinars and Online Events in AI, Data Science, Machine Learning: June - Jun 4, 2020.
  
  Here are some interesting upcoming webinar, online events and virtual conferences in in AI, Data Science, and Machine Learning.

  AI, Machine Learning, Meetings, Online Education, Virtual Event, Webinar

• Machine Learning Experiment Tracking - Jun 4, 2020.
  
  Why is experiment tracking so important for doing real world machine learning?

  Experimentation, Machine Learning, Python

• KDD-2020 – Virtual Only Conference, Aug 23-27 - May 29, 2020.
  
  After much consideration, the General Chairs, Executive Committee and Organizing Committee for KDD 2020 have decided to take the conference fully virtual. Clear your calendar for August 23-27, 2020, and enjoy access to all the virtual content live and on demand the week of the event.

  Data Science, KDD, KDD-2020, Machine Learning, Online Education, Research, Virtual Event

• Model Evaluation Metrics in Machine Learning - May 28, 2020.
  
  A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.

  Classification, Confusion Matrix, Machine Learning, Metrics, Python, Regression

• 5 Machine Learning Papers on Face Recognition - May 28, 2020.
  
  This article will highlight some of that research and introduce five machine learning papers on face recognition.

  Face Recognition, Image Recognition, Machine Learning, Neural Networks

• Faster machine learning on larger graphs with NumPy and Pandas - May 27, 2020.
  
  One of the most exciting features of StellarGraph 1.0 is a new graph data structure — built using NumPy and Pandas — that results in significantly lower memory usage and faster construction times.

  Graphs, Machine Learning, numpy, Pandas

• KDnuggets™ News 20:n21, May 27: The Best NLP with Deep Learning Course is Free; Your First Machine Learning Web App - May 27, 2020.
  
  Also: Python For Everybody: The Free eBook; Complex logic at breakneck speed: Try Julia for data science; An easy guide to choose the right Machine Learning algorithm; Dataset Splitting Best Practices in Python; Appropriately Handling Missing Values for Statistical Modelling and Prediction

  Algorithms, Course, Deep Learning, Free ebook, Julia, Machine Learning, NLP, Python

• Interactive Machine Learning Experiments - May 26, 2020.
  
  Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.

  Convolutional Neural Networks, GitHub, Image Recognition, Jupyter, Machine Learning, Recurrent Neural Networks, Tutorials

• Build and deploy your first machine learning web app - May 22, 2020.
  
  A beginner’s guide to train and deploy machine learning pipelines in Python using PyCaret.

  App, Flask, Heroku, Machine Learning, Modeling, Open Source, Pipeline, PyCaret, Python

• What they do not tell you about machine learning - May 19, 2020.
  
  There's a lot of excitement out there about machine learning jobs. So, it's always good to start off with a healthy dose of reality and proper expectations.

  Advice, Career, Machine Learning, Machine Learning Engineer, SQL

• Linear algebra and optimization and machine learning: A textbook - May 18, 2020.
  
  This book teaches linear algebra and optimization as the primary topics of interest, and solutions to machine learning problems as applications of these methods. Therefore, the book also provides significant exposure to machine learning.

  Book, Charu Aggarwal, Linear Algebra, Machine Learning, Optimization

• Automated Machine Learning: The Free eBook - May 18, 2020.
  
  There is a lot to learn about automated machine learning theory and practice. This free eBook can get you started the right way.

  Automated Machine Learning, AutoML, Free ebook, Machine Learning

• 5 Great New Features in Scikit-learn 0.23 - May 15, 2020.
  
  Check out 5 new features of the latest Scikit-learn release, including the ability to visualize estimators in notebooks, improvements to both k-means and gradient boosting, some new linear model implementations, and sample weight support for a pair of existing regressors.

  Gradient Boosting, Jupyter, K-means, Machine Learning, Python, Regression, scikit-learn

• AI and Machine Learning for Healthcare - May 14, 2020.
  
  Traditional business and technology sectors are not the only fields being impacted by AI. Healthcare is a field that is thought to be highly suitable for the applications of AI tools and techniques.

  AI, Coronavirus, COVID-19, Healthcare, Machine Learning

• I Designed My Own Machine Learning and AI Degree - May 13, 2020.
  
  With so many pioneering online resources for open education, check out this organized collection of courses you can follow to become a well-rounded machine learning and AI engineer.

  Data Science Education, Machine Learning, Online Education

• KDnuggets™ News 20:n19, May 13: Start Your Machine Learning Career in Quarantine; Will Machine Learning Engineers Exist in 10 Years? - May 13, 2020.
  
  Also: The Elements of Statistical Learning: The Free eBook; Explaining "Blackbox" Machine Learning Models: Practical Application of SHAP; What You Need to Know About Deep Reinforcement Learning; 5 Concepts You Should Know About Gradient Descent and Cost Function; Hyperparameter Optimization for Machine Learning Models

  Book, Career, Deep Learning, Explainability, Free ebook, Machine Learning, Machine Learning Engineer, Reinforcement Learning, SHAP

• Machine Learning in Power BI using PyCaret - May 12, 2020.
  
  Check out this step-by-step tutorial for implementing machine learning in Power BI within minutes.

  Clustering, K-means, Machine Learning, Microsoft, Power BI, PyCaret, Python

• Start Your Machine Learning Career in Quarantine - May 11, 2020.
  
  While this quarantine can last two months, make the most of it by starting your career in Machine Learning with this 60-day learning plan.

  Career, Data Science Education, Machine Learning, Online Education

• The Architecture Used at LinkedIn to Improve Feature Management in Machine Learning Models - May 11, 2020.
  
  The new typed feature schema streamlined the reusability of features across thousands of machine learning models.

  Feature Engineering, Feature Selection, LinkedIn, Machine Learning

• 5 Concepts You Should Know About Gradient Descent and Cost Function - May 7, 2020.
  
  Why is Gradient Descent so important in Machine Learning? Learn more about this iterative optimization algorithm and how it is used to minimize a loss function.

  Cost Function, Gradient Descent, Machine Learning, Neural Networks, Optimization

• Hyperparameter Optimization for Machine Learning Models - May 7, 2020.
  
  Check out this comprehensive guide to model optimization techniques.

  Hyperparameter, Machine Learning, Modeling, Optimization, Python

• Beginners Learning Path for Machine Learning - May 5, 2020.
  
  So, you are interested in machine learning? Here is your complete learning path to start your career in the field.

  Beginners, Learning Path, Machine Learning

• Getting Started with Spectral Clustering - May 5, 2020.
  
  This post will unravel a practical example to illustrate and motivate the intuition behind each step of the spectral clustering algorithm.

  Clustering, Machine Learning, Python

• Optimize Response Time of your Machine Learning API In Production - May 1, 2020.
  
  This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

  API, Machine Learning, Optimization, Production, Python

• KDnuggets™ News 20:n17, Apr 29: The Super Duper NLP Repo; Free Machine Learning & Data Science Books & Courses for Quarantine - Apr 29, 2020.
  
  Also: Should Data Scientists Model COVID19 and other Biological Events; Learning during a crisis (Data Science 90-day learning challenge); Data Transformation: Standardization vs Normalization; DBSCAN Clustering Algorithm in Machine Learning; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

  Courses, COVID-19, Data Science, Free ebook, Machine Learning, Modeling, NLP, Normalization, Standardization

• 10 Best Machine Learning Textbooks that All Data Scientists Should Read - Apr 28, 2020.
  
  Check out these 10 books that can help data scientists and aspiring data scientists learn machine learning today.

  Books, Data Scientist, Machine Learning

• 3 Reasons Why We Are Far From Achieving Artificial General Intelligence - Apr 23, 2020.
  
  How far we are from achieving Artificial General Intelligence? We answer this through the study of three limitations of current machine learning.

  AGI, AI, Machine Learning

• Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition - Apr 22, 2020.
  
  If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.

  Books, Courses, Data Science, Free ebook, Machine Learning, MOOC

• KDnuggets™ News 20:n16, Apr 22: Scaling Pandas with Dask for Big Data; Dive Into Deep Learning: The Free eBook - Apr 22, 2020.
  
  4 Steps to ensure your AI/Machine Learning system survives COVID-19; State of the Machine Learning and AI Industry; A Key Missing Part of the Machine Learning Stack; 5 Papers on CNNs Every Data Scientist Should Read

  AI, Big Data, Coronavirus, COVID-19, Dask, Deep Learning, Free ebook, Machine Learning, Pandas

• Announcing PyCaret 1.0.0 - Apr 21, 2020.
  
  An open source low-code machine learning library in Python. PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient.

  Machine Learning, Modeling, Open Source, PyCaret, Python

• A Key Missing Part of the Machine Learning Stack - Apr 20, 2020.
  
  With many organizations having machine learning models running in production, some are discovering that inefficiencies exists in the first step of the process: feature definition and extraction. Robust feature management is now being realized as a key missing part of the ML stack, and improving it by applying standard software development practices is gaining attention.

  Feature Engineering, Feature Extraction, Feature Store, Machine Learning

• 4 Steps to ensure your AI/Machine Learning system survives COVID-19 - Apr 17, 2020.
  
  Many AI models rely on historical data to make predictions on future behavior. So, what happens when consumer behavior across the planet makes a 180 degree flip? Companies are quickly seeing less value from some AI systems as training data is no longer relevant when user behaviors and preferences change so drastically. Those who are flexible can make it through this crisis in data, and these four techniques will help you stay in front of the competition.

  AI, Coronavirus, COVID-19, Deployment, Machine Learning

• State of the Machine Learning and AI Industry - Apr 16, 2020.
  
  Enterprises are struggling to launch machine learning models that encapsulate the optimization of business processes. These are now the essential components of data-driven applications and AI services that can improve legacy rule-based business processes, increase productivity, and deliver results. In the current state of the industry, many companies are turning to off-the-shelf platforms to increase expectations for success in applying machine learning.

  AI, AutoML, Data Science Platform, Industry, Machine Learning

• Better notebooks through CI: automatically testing documentation for graph machine learning - Apr 16, 2020.
  
  In this article, we’ll walk through the detailed and helpful continuous integration (CI) that supports us in keeping StellarGraph’s demos current and informative.

  Graphs, Integration, Jupyter, Machine Learning, Python, Software Engineering

• Federated Learning: An Introduction - Apr 15, 2020.
  
  Improving machine learning models and making them more secure by training on decentralized data.

  Federated Learning, Learning, Machine Learning, Privacy, Security

• KDnuggets™ News 20:n15, Apr 15: How to Do Hyperparameter Tuning on Any Python Script; 10 Must-read Machine Learning Articles - Apr 15, 2020.
  
  Learn how to do hyperparameter tuning on python ML scripts; Read 10 must-read Machine Learning Articles; Understand the process for Data Science project review; see how data science is used to understand COVID-19; and stay safe and healthy!

  Hyperparameter, Machine Learning, Python, Research

• Can Java Be Used for Machine Learning and Data Science? - Apr 14, 2020.
  
  While Python and R have become favorites for building these programs, many organizations are turning to Java application development to meet their needs. Read on to see how, and why.

  Data Science, Java, Machine Learning, Programming Languages

• 10 Must-read Machine Learning Articles (March 2020) - Apr 9, 2020.
  
  This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.

  AI, API, Cloud, Data Analytics, Datasets, fast.ai, Machine Learning, Neural Networks, Social Media

• How to Do Hyperparameter Tuning on Any Python Script in 3 Easy Steps - Apr 8, 2020.
  
  With your machine learning model in Python just working, it's time to optimize it for performance. Follow this guide to setup automated tuning using any optimization library in three steps.

  Hyperparameter, Machine Learning, Optimization, Python

• 3 Reasons to Use Random Forest® Over a Neural Network: Comparing Machine Learning versus Deep Learning - Apr 8, 2020.
  
  Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?

  Machine Learning, Neural Networks, random forests algorithm

• 2 Things You Need to Know about Reinforcement Learning – Computational Efficiency and Sample Efficiency - Apr 7, 2020.
  
  Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.

  Efficiency, Machine Learning, Reinforcement Learning

• Mathematics for Machine Learning: The Free eBook - Apr 6, 2020.
  
  Check out this free ebook covering the fundamentals of mathematics for machine learning, as well as its companion website of exercises and Jupyter notebooks.

  Book, Free ebook, Machine Learning, Mathematics

• More Performance Evaluation Metrics for Classification Problems You Should Know - Apr 3, 2020.
  
  When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. However, this metric alone is never the entire story, as it can still offer misleading results. That's where these additional performance evaluations come into play to help tease out more meaning from your model.

  Classification, Confusion Matrix, Machine Learning, Metrics, Precision, Recall, ROC-AUC

• Introduction to the K-nearest Neighbour Algorithm Using Examples - Apr 1, 2020.
  
  Read this concise summary of KNN, a supervised and pattern classification learning algorithm which helps us find which class the new input belongs to when k nearest neighbours are chosen and distance is calculated between them.

  Algorithms, K-nearest neighbors, Machine Learning, Python, scikit-learn

• Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs - Apr 1, 2020.
  
  From network security to financial fraud, anomaly detection helps protect businesses, individuals, and online communities. To help improve anomaly detection, researchers have developed a new approach called MIDAS.

  Anomaly Detection, Graph, Machine Learning

• KDnuggets™ News 20:n13, Apr 1: Effective visualizations for pandemic storytelling; Machine learning for time series forecasting - Apr 1, 2020.
  
  This week, read about the power of effective visualizations for pandemic storytelling; see how (not) to use machine learning for time series forecasting; learn about a deep learning breakthrough: a sub-linear deep learning algorithm that does not need a GPU?; familiarize yourself with how to painlessly analyze your time series; check out what can we learn from the latest coronavirus trends; and... KDnuggets topics?!? Also, much more.

  Coronavirus, Data Visualization, Deep Learning, Distributed, Machine Learning, Python, Time Series

• How (not) to use Machine Learning for time series forecasting: The sequel - Mar 30, 2020.
  
  Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late.

  Forecasting, Machine Learning, Mistakes, Time Series

• 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.

  Cloud, Kubeflow, Kubernetes, Machine Learning

• 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.

  Algorithms, Deep Learning, GPU, Machine Learning

• Making sense of ensemble learning techniques - Mar 26, 2020.
  
  This article breaks down ensemble learning and how it can be used for problem solving.

  Algorithms, Data Science, Ensemble Methods, Machine Learning

• 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.

  Data Preparation, Data Science, Dimensionality Reduction, Feature Engineering, Machine Learning

• 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.

  Career Advice, Coronavirus, Free ebook, Machine Learning, SVM, Time Series

• 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.

  GitHub, Kaggle, Machine Learning, Research

• 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.

  Google, Machine Learning, Quantum Computing, TensorFlow

• 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.

  Books, Free ebook, Machine Learning

• 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.

  Algorithms, Explained, Linear Algebra, Machine Learning, Support Vector Machines, SVM

• 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.

  AutoML, Coronavirus, Machine Learning, Team

• 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.

  Data Science Team, Machine Learning, Team

• 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.

  Applications, GitHub, Machine Learning, Postgres, PyCharm, Tools

• 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.

  Decision Boundaries, Machine Learning, Modeling, R

• 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.

  Image Classification, Learning, Machine Learning

• 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.

  Ethics, Google, Machine Learning, Optimization, TensorFlow

• 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.

  API, Deployment, Machine Learning, MLOps, Software Engineering

• 21 Machine Learning Projects – Datasets Included - 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.

  Chatbot, Datasets, Google Trends, Machine Learning, Project, Uber

• 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.

  Game Theory, Machine Learning

• 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.

  AI, Data Science, Diversity, Machine Learning, Women

• 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.

  Cybersecurity, Machine Learning, Security

• 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.

  Machine Learning, Security, Trends

• 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.

  Classification, Classifier, Interpretability, Machine Learning, Metrics, ROC-AUC

• 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.

  Data Science, Data Scientist, Image Classification, Machine Learning

• 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.

  AI, Data Science, Explainability, Geospatial, GPT-2, Key Terms, Machine Learning, Natural Language Generation, Reinforcement Learning, Transformer

• 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.

  Machine Learning, Scalability, Uber

• 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.

  Beginners, Decision Trees, Machine Learning

• 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.

  Algorithms, AutoML, Data Science, Data Scientist, Gartner, Machine Learning, Magic Quadrant, Mathematics, R

• 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.

  Courses, Data Science, Machine Learning, Mathematics, MOOC

• 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.

  Alteryx, Data Science Platform, Databricks, Dataiku, DataRobot, Domino, Gartner, Google, H2O, IBM, Knime, Machine Learning, Magic Quadrant, MathWorks, Microsoft Azure, RapidMiner, SAS, TIBCO

• 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.

  Data Science, Machine Learning, Programming, R

• 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!

  AI, API, Data Science, Data Scientist, Health, Key Terms, Machine Learning, Mathematics, Neural Networks, Python

• 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.

  AI, Data Science, Key Terms, Machine Learning

• 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.

  Africa, AI, Computer Vision, Machine Learning

• 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?

  Chat, Chatbot, Google, Machine Learning, NLU

• 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.

  Deployment, Machine Learning, MLOps

• 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.

  Adversarial, Kaggle, Machine Learning, Python, Validation

• 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.

  Automated Machine Learning, AutoML, Deep Learning, Hyperparameter, Machine Learning, Optimization, Python, scikit-learn

• 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.

  API, Deep Learning, Machine Learning, Open Source, Python

• 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.

  AI, Fraud Detection, Gmail, Machine Learning, Search, Social Media, Travel

• 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.

  AI, Big Data, Data Mining, Data Science, Deep Learning, Machine Learning

• 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.

  AI, Future, Machine Learning, Machine Learning Engineer

• 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.

  AI, Beginners, Linear Regression, Machine Learning, Mathematics

• 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.

  Kalman Filters, Machine Learning, Probability

• 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.

  Cloud, Machine Learning, R, Twitter

• 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.

  Deployment, Kaggle, Lukas Biewald, Machine Learning, Project Fail, Training Data

• 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.

  App, Challenge, DevOps, Machine Learning, Streamlit

• 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.

  Cross-validation, Data Science, Machine Learning

• 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.

  Automated Machine Learning, AutoML, Deep Learning, Machine Learning

• 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.

  Cosmology, Machine Learning, Python