Machine Learning (2045)

• 5 Data Science Open-source Projects You Should Consider Contributing to - Jun 7, 2021.
  
  As you prepare to interview for a position in data science or are looking to jump to the next level, now is the time to enhance your skills and your resume with by working on rea, open-source projects. Here, we suggest a great selection of projects you can contribute to and help build something awesome, so, all you need to do choose one and tackle it head on.

  Caffe, Data Science, Data Science Skills, GitHub, Google, Machine Learning, Open Source

• PyCaret 101: An introduction for beginners - Jun 7, 2021.
  
  This article is a great overview of how to get started with PyCaret for all your machine learning projects.

  Machine Learning, PyCaret, Python

• Beyond Brainless AI with a Feature Store - Jun 4, 2021.
  
  AI-powered products that are limited to the data available within its application are like jellyfish: its autonomic system makes it functional, but it lacks a brain. However, you can evolve your models with data enriched "brains" through the help of a feature store.

  AI, Data Engineering, Feature Store, Machine Learning

• 10 Deadly Sins of Machine Learning Model Training - Jun 4, 2021.
  
  These mistakes are easy to overlook but costly to redeem.

  Machine Learning, Modeling, Training

• Machine Learning Model Interpretation - Jun 2, 2021.
  
  Read this overview of using Skater to build machine learning visualizations.

  Explainability, Interpretability, Machine Learning, Python

• How I Doubled My Income with Data Science and Machine Learning - Jun 1, 2021.
  
  Many career opportunities exist in the ever-expanding domain of data. Finding your place -- and finding your salary -- is largely up to your dedication, focus, and drive to learn. If you are an aspiring Data Scientist or have already started your professional journey, there are multiple strategies for maximizing your earning potential.

  Career Advice, Data Science, Data Science Skills, Machine Learning, Salary

• Supercharge Your Machine Learning Experiments with PyCaret and Gradio - May 31, 2021.
  
  A step-by-step tutorial to develop and interact with machine learning pipelines rapidly.

  Deployment, Machine Learning, Pipeline, PyCaret, Python

• Where Did You Apply Analytics, Data Science, Machine Learning in 2020/2021? - May 25, 2021.
  
  Take part in the latest KDnuggets survey, and let us know where you have been applying Analytics, Data Science, Machine Learning in 2020/2021.

  Analytics, Data Science, Machine Learning, Poll, Survey

• Write and train your own custom machine learning models using PyCaret - May 25, 2021.
  
  A step-by-step, beginner-friendly tutorial on how to write and train custom machine learning models in PyCaret.

  Machine Learning, Modeling, PyCaret, Python, Training

• Data Validation in Machine Learning is Imperative, Not Optional - May 24, 2021.
  
  Before we reach model training in the pipeline, there are various components like data ingestion, data versioning, data validation, and data pre-processing that need to be executed. In this article, we will discuss data validation, why it is important, its challenges, and more.

  Data Quality, Machine Learning, Production, Validation

• Easy MLOps with PyCaret + MLflow - May 18, 2021.
  
  A beginner-friendly, step-by-step tutorial on integrating MLOps in your Machine Learning experiments using PyCaret.

  Machine Learning, MLflow, MLOps, PyCaret, Python

• Best Python Books for Beginners and Advanced Programmers - May 14, 2021.
  
  Let's take a look at nine of the best Python books for both beginners and advanced programmers, covering topics such as data science, machine learning, deep learning, NLP, and more.

  Analytics, Books, Data Science, Deep Learning, Machine Learning, Python

• The next-generation of AutoML frameworks - May 14, 2021.
  
  AutoML frameworks are getting better every day, and can provide high-performing ML pipelines, unique data insights, and ML explanations. No longer black-boxes, these powerful tools offer self-documenting capabilities and native Python notebook support.

  AutoML, Machine Learning

• DeepMind Wants to Reimagine One of the Most Important Algorithms in Machine Learning - May 14, 2021.
  
  In one of the most important papers this year, DeepMind proposed a multi-agent structure to redefine PCA.

  Algorithms, DeepMind, Game Theory, Machine Learning, PCA

• The Explainable Boosting Machine - May 13, 2021.
  
  As accurate as gradient boosting, as interpretable as linear regression.

  Decision Trees, Explainability, Gradient Boosting, Interpretability, Machine Learning

• KDnuggets™ News 21:n18, May 12: Data Preparation in SQL, with Cheat Sheet!; Rebuilding 7 Python Projects - May 12, 2021.
  
  Data Preparation in SQL, with Cheat Sheet!; Rebuilding My 7 Python Projects; Applying Python’s Explode Function to Pandas DataFrames; Essential Linear Algebra for Data Science and Machine Learning; Similarity Metrics in NLP

  Cheat Sheet, Data Preparation, Data Science, Linear Algebra, Machine Learning, Metrics, NLP, Pandas, Project, Python, SQL

• Must-have Chrome Extensions For Machine Learning Engineers And Data Scientists - May 11, 2021.
  
  Browser extensions are a productivity secret weapon for hackers and developers. Many machine learning practitioners use Chrome, and this list features must-have Chrome extensions for machine learning engineers and data scientists that you should check out today.

  arXiv, Data Science Tools, Deployment, Google Colab, Machine Learning, Research, Tools

• A Comprehensive Guide to Ensemble Learning – Exactly What You Need to Know - May 6, 2021.
  
  This article covers ensemble learning methods, and exactly what you need to know in order to understand and implement them.

  CatBoost, Ensemble Methods, Machine Learning, Python, random forests algorithm, scikit-learn, XGBoost

• Feature stores – how to avoid feeling that every day is Groundhog Day - May 6, 2021.
  
  Feature stores stop the duplication of each task in the ML lifecycle. You can reuse features and pipelines for different models, monitor models consistently, and sidestep data leakage with this MLOps technology that everyone is talking about.

  Data Preparation, Feature Store, Machine Learning, MLOps

• What makes a winning entry in a Machine Learning competition? - May 5, 2021.
  
  So you want to show your grit in a Kaggle-style competition? Many, many others have the same idea, including domain experts and non-experts, and academic and corporate teams. What does it take for your bright ideas and skills to come out on top of thousands of competitors?

  Challenge, Competition, Kaggle, Machine Learning, PyTorch, TensorFlow

• The Machine Learning Research Championed by the Biggest AI Labs in the World - May 5, 2021.
  
  How Google, Microsoft, Facebook, DeepMind, OpenAI, Amazon and IBM think about the future of AI.

  AI, Deep Learning, Machine Learning, Research

• Disentangling AI, Machine Learning, and Deep Learning - May 4, 2021.
  
  The field of Artificial Intelligence is extremely broad and captures a winding history through the evolution of various sub-fields that experienced many ups and downs over the years. Appreciating AI within its historical contexts will enhance your communication with the public, colleagues, and potential hiring managers, as well as guide your thinking as you progress in the application and study of state-of-the-art techniques.

  AI, Deep Learning, History, Machine Learning

• XGBoost Explained: DIY XGBoost Library in Less Than 200 Lines of Python - May 3, 2021.
  
  Understand how XGBoost work with a simple 200 lines codes that implement gradient boosting for decision trees.

  Algorithms, Machine Learning, Python, XGBoost

• Gradient Boosted Decision Trees – A Conceptual Explanation - Apr 30, 2021.
  
  Gradient boosted decision trees involves implementing several models and aggregating their results. These boosted models have become popular thanks to their performance in machine learning competitions on Kaggle. In this article, we’ll see what gradient boosted decision trees are all about.

  CatBoost, Decision Trees, Gradient Boosting, Machine Learning, Python, scikit-learn, XGBoost

• FluDemic – using AI and Machine Learning to get ahead of disease - Apr 30, 2021.
  
  We are amidst a healthcare data explosion. AI/ML will be more vital than ever in the prevention and handling of future pandemics. Here, we walk you through the different facets of modeling infectious diseases, focusing on influenza and COVID-19.

  AI, COVID-19, Healthcare, Machine Learning

• Feature Engineering of DateTime Variables for Data Science, Machine Learning - Apr 29, 2021.
  
  Learn how to make more meaningful features from DateTime type variables to be used by Machine Learning Models.

  Data Science, Feature Engineering, Machine Learning, Python

• Best Podcasts for Machine Learning - Apr 28, 2021.
  
  Podcasts, especially those featuring interviews, are great for learning about the subfields and tools of AI, as well as the rock stars and superheroes of the AI world. Here, we highlight some of the best podcasts today that are perfect for both those learning about machine learning and seasoned practitioners.

  AI, Data Science, Machine Learning, Podcast

• Multiple Time Series Forecasting with PyCaret - Apr 27, 2021.
  
  A step-by-step tutorial to forecast multiple time series with PyCaret.

  Forecasting, Machine Learning, PyCaret, Python, Time Series

• Improving model performance through human participation - Apr 23, 2021.
  
  Certain industries, such as medicine and finance, are sensitive to false positives. Using human input in the model inference loop can increase the final precision and recall. Here, we describe how to incorporate human feedback at inference time, so that Machines + Humans = Higher Precision & Recall.

  Data Science Platform, Humans, Machine Learning, Model Performance, Precision, Recall

• Data Science Books You Should Start Reading in 2021 - Apr 23, 2021.
  
  Check out this curated list of the best data science books for any level.

  Books, Data Science, Data Scientist, Deep Learning, Machine Learning

• The Three Edge Case Culprits: Bias, Variance, and Unpredictability - Apr 22, 2021.
  
  Edge cases occur for three basic reasons: Bias – the ML system is too ‘simple’; Variance – the ML system is too ‘inexperienced’; Unpredictability – the ML system operates in an environment full of surprises. How do we recognize these edge cases situations, and what can we do about them?

  Bias, iMerit, Machine Learning, Variance

• Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1 - Apr 22, 2021.
  
  New to data science? Interested in the must-know machine learning algorithms in the field? Check out the first part of our list and introductory descriptions of the top 10 algorithms for data scientists to know.

  Algorithms, Bagging, Data Science, Data Scientist, Decision Trees, Linear Regression, Machine Learning, SVM, Top 10

• How Uber manages Machine Learning Experiments with Comet.ml - Apr 21, 2021.
  
  At Uber, where ML is fundamental to most products, a mechanism to manage offline experiments easily is needed to improve developer velocity. To solve for this, Uber AI was looking for a solution that will potentially complement and extend its in-house experiment management and collaboration capabilities.

  Comet.ml, Experimentation, Machine Learning, Scalability, Uber

• Time Series Forecasting with PyCaret Regression Module - Apr 21, 2021.
  
  PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use PyCaret's Regression Module for Time Series Forecasting.

  Machine Learning, PyCaret, Python, Regression, Time Series

• Free From Stanford: Machine Learning with Graphs - Apr 19, 2021.
  
  Check out the freely-available Stanford course Machine Learning with Graphs, taught by Jure Leskovec, and see how a world renowned researcher teaches their topic of expertise. Accessible materials include slides, videos, and more.

  Courses, Free, Graphs, Jure Leskovec, Machine Learning, Stanford

• 6 Mistakes To Avoid While Training Your Machine Learning Model - Apr 15, 2021.
  
  While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.

  Computer Vision, Data Labeling, Machine Learning, Mistakes

• Continuous Training for Machine Learning – a Framework for a Successful Strategy - Apr 14, 2021.
  
  A basic appreciation by anyone who builds machine learning models is that the model is not useful without useful data. This doesn't change after a model is deployed to production. Effectively monitoring and retraining models with updated data is key to maintaining valuable ML solutions, and can be accomplished with effective approaches to production-level continuous training that is guided by the data.

  Machine Learning, MLOps, Model Performance, Production, Real-time, Training Data

• KDnuggets™ News 21:n14, Apr 14: A/B Testing: Common Questions and Answers in Data Science Interviews; Interpretable Machine Learning: The Free eBook - Apr 14, 2021.
  
  Common Questions and Answers on A/B testing in Data Science Interviews; Interpretable Machine Learning: The Free eBook; Why machine learning struggles with causality; Deep Learning Recommendation Models: A Deep Dive; and more.

  A/B Testing, Causality, Free ebook, Interpretability, Machine Learning

• Automated Anomaly Detection Using PyCaret - Apr 13, 2021.
  
  Learn to automate anomaly detection using the open source machine learning library PyCaret.

  Anomaly Detection, Machine Learning, PyCaret, Python

• 7 Must-Haves in your Data Science CV - Apr 13, 2021.
  
  If you are looking for a new role as a Data Scientist -- either as a first job fresh out of school, a career change, or a shift to another organization -- then check off as many of these critical points as possible to stand out in the crowd and pass the hiring manager's initial CV screen.

  Business, Career Advice, Data Scientist, Machine Learning

• Why machine learning struggles with causality - Apr 8, 2021.
  
  If there's one thing people know how to do, and that's guess what caused something else to happen. Usually these guesses are good, especially when making a visual observation of something in the physical world. AI continues to wrestle with such inference of causality, and fundamental challenges must be overcome before we can have "intuitive" machine learning.

  Causality, Inference, Machine Learning

• KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable - Apr 7, 2021.
  
  Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1

  Automated Machine Learning, AutoML, Data Science, Data Scientist, Explainable AI, Interpretability, Machine Learning, MLOps, Python

• How Noisy Labels Impact Machine Learning Models - Apr 6, 2021.
  
  Not all training data labeling errors have the same impact on the performance of the Machine Learning system. The structure of the labeling errors make a difference. Read iMerit’s latest blog to learn how to minimize the impact of labeling errors.

  Data Labeling, Data Preparation, iMerit, Machine Learning

• How to Dockerize Any Machine Learning Application - Apr 6, 2021.
  
  How can you -- an awesome Data Scientist -- also be known as an awesome software engineer? Docker. And these 3 simple steps to use it for your solutions over and over again.

  Advice, Applications, Containers, Deployment, Docker, Machine Learning

• The Best Machine Learning Frameworks & Extensions for TensorFlow - Apr 5, 2021.
  
  Check out this curated list of useful frameworks and extensions for TensorFlow.

  Machine Learning, Python, TensorFlow

• How to deploy Machine Learning/Deep Learning models to the web - Apr 5, 2021.
  
  The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.

  Deep Learning, Deployment, Machine Learning, RESTful API

• Awesome Tricks And Best Practices From Kaggle - Apr 5, 2021.
  
  Easily learn what is only learned by hours of search and exploration.

  Data Science, Kaggle, Machine Learning, Tips

• Shapash: Making Machine Learning Models Understandable - Apr 2, 2021.
  
  Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.

  Explainability, Machine Learning, Python, SHAP

• Easy AutoML in Python - Apr 1, 2021.
  
  We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.

  Automated Machine Learning, AutoML, Machine Learning, Open Source, Python

• Overview of MLOps - Mar 26, 2021.
  
  Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.

  Data Science, Deployment, Machine Learning, MLOps, Monitoring

• Data Science Curriculum for Professionals - Mar 25, 2021.
  
  If you are looking to expand or transition your current professional career that is buried in spreadsheet analysis into one powered by data science, then you are in for an exciting but complex journey with much to explore and master. To begin your adventure, following this complete road map to guide you from a gnome in the forest of spreadsheets to an AI wizard known far and wide throughout the kingdom.

  Cloud Computing, Data Science Education, Data Visualization, Machine Learning, Python, R, Roadmap, Statistics

• KDnuggets™ News 21:n12, Mar 24: More Data Science Cheat sheets; Top YouTube Channels for Machine Learning - Mar 24, 2021.
  
  Happy with your job or not? Either way, vote in KDnuggets Poll on Data Job Satisfaction to help us understand the current situation.
  In this issue, More data science cheatsheets; How to create your data science portfolio; The best machine learning frameworks and extensions for scikit-learn; Top youtube channels for machine learning; dbt, the ETL and ELT disrupter;

  Cheat Sheet, Machine Learning, Portfolio, scikit-learn, Youtube

• Top YouTube Machine Learning Channels - Mar 23, 2021.
  
  These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.

  Machine Learning, Youtube

• The Best Machine Learning Frameworks & Extensions for Scikit-learn - Mar 22, 2021.
  
  Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.

  Machine Learning, Python, scikit-learn

• Learning from machine learning mistakes - Mar 19, 2021.
  
  Read this article and discover how to find weak spots of a regression model.

  Machine Learning, Mistakes, Modeling, Regression

• Automating Machine Learning Model Optimization - Mar 17, 2021.
  
  This articles presents an overview of using Bayesian Tuning and Bandits for machine learning.

  Bayesian, Hyperparameter, Machine Learning, Optimization

• Data Validation and Data Verification – From Dictionary to Machine Learning - Mar 16, 2021.
  
  In this article, we will understand the difference between data verification and data validation, two terms which are often used interchangeably when we talk about data quality. However, these two terms are distinct.

  Data Quality, Machine Learning, Validation

• 10 Amazing Machine Learning Projects of 2020 - Mar 15, 2021.
  
  So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.

  Chatbot, Deep Learning, Image Processing, Machine Learning, Project, Trends

• A Beginner’s Guide to the CLIP Model - Mar 11, 2021.
  
  CLIP is a bridge between computer vision and natural language processing. I'm here to break CLIP down for you in an accessible and fun read! In this post, I'll cover what CLIP is, how CLIP works, and why CLIP is cool.

  CLIP, Computer Vision, Machine Learning, NLP

• A Machine Learning Model Monitoring Checklist: 7 Things to Track - Mar 11, 2021.
  
  Once you deploy a machine learning model in production, you need to make sure it performs. In the article, we suggest how to monitor your models and open-source tools to use.

  Checklist, Data Science, Deployment, Machine Learning, MLOps, Monitoring

• KDnuggets™ News 21:n10, Mar 10: More Resources for Women in AI, Data Science, and Machine Learning; Speeding up Scikit-Learn Model Training - Mar 10, 2021.
  
  More Resources for Women in AI, Data Science, and Machine Learning; Speeding up Scikit-Learn Model Training; Dask and Pandas: No Such Thing as Too Much Data; 9 Skills You Need to Become a Data Engineer; 8 Women in AI Who Are Striving to Humanize the World

  AI, Dask, Data Engineer, Data Science, Machine Learning, Modeling, Pandas, scikit-learn, Training, Women

• 4 Machine Learning Concepts I Wish I Knew When I Built My First Model - Mar 9, 2021.
  
  Diving into building your first machine learning model will be an adventure -- one in which you will learn many important lessons the hard way. However, by following these four tips, your first and subsequent models will be put on a path toward excellence.

  Feature Selection, Gradio, Hyperparameter, Machine Learning, Metrics, Python

• More Resources for Women in AI, Data Science, and Machine Learning - Mar 8, 2021.
  
  Useful resources to help more women enter and succeed in AI, Data Science, and Machine Learning fields.

  Advice, AI, Data Science, Diversity, Machine Learning, Women

• Speeding up Scikit-Learn Model Training - Mar 5, 2021.
  
  If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization, you can build the best scikit-learn model possible in the least amount of time.

  Distributed Computing, Machine Learning, Optimization, scikit-learn

• Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret - Mar 5, 2021.
  
  PyCaret, a low code Python ML library, offers several ways to tune the hyper-parameters of a created model. In this post, I'd like to show how Ray Tune is integrated with PyCaret, and how easy it is to leverage its algorithms and distributed computing to achieve results superior to default random search method.

  Bayesian, Hyperparameter, Machine Learning, Optimization, PyCaret, Python, scikit-learn

• Reducing the High Cost of Training NLP Models With SRU++ - Mar 4, 2021.
  
  The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.

  Deep Learning, Machine Learning, Neural Networks, NLP

• Getting Started with Distributed Machine Learning with PyTorch and Ray - Mar 3, 2021.
  
  Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.

  Distributed Systems, Machine Learning, Python, PyTorch

• Machine Learning Systems Design: A Free Stanford Course - Feb 26, 2021.
  
  This freely-available course from Stanford should give you a toolkit for designing machine learning systems.

  Courses, Deployment, Design, Machine Learning, Maintenance, Stanford

• Why Do Machine Learning Projects Fail? - Feb 24, 2021.
  
  At the beginning of any data science project, many challenges could arise that lead to its eventual collapse. Making sure you look ahead -- early in the planning -- toward putting your resulting model into production can help increase the chance of delivering long-term value with your developed machine learning system.

  A/B Testing, Failure, Machine Learning, Model Drift, Production, Project Fail

• KDnuggets™ News 21:n08, Feb 24: Powerful Exploratory Data Analysis in just two lines of code; Cartoon: Data Scientist vs Data Engineer - Feb 24, 2021.
  
  Powerful Exploratory Data Analysis in just two lines of code; Cartoon: Data Scientist vs Data Engineer; Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall; Feature Store as a Foundation for Machine Learning; Approaching (Almost) Any Machine Learning Problem

  Cartoon, Data Analysis, Data Engineering, Data Science, Deep Learning, Machine Learning, Metrics, Python

• Feature Store as a Foundation for Machine Learning - Feb 19, 2021.
  
  With so many organizations now taking the leap into building production-level machine learning models, many lessons learned are coming to light about the supporting infrastructure. For a variety of important types of use cases, maintaining a centralized feature store is essential for higher ROI and faster delivery to market. In this review, the current feature store landscape is described, and you can learn how to architect one into your MLOps pipeline.

  Data Engineering, Data Infrastructure, Data Lake, Feature Engineering, Feature Store, Machine Learning, Metadata, MLOps, Pipeline

• Approaching (Almost) Any Machine Learning Problem - Feb 18, 2021.
  
  This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.

  Deep Learning, Free ebook, Machine Learning, Python

• Distributed and Scalable Machine Learning [Webinar] - Feb 17, 2021.
  
  Mike McCarty and Gil Forsyth work at the Capital One Center for Machine Learning, where they are building internal PyData libraries that scale with Dask and RAPIDS. For this webinar, Feb 23 @ 2 pm PST, 5pm EST, they’ll join Hugo Bowne-Anderson and Matthew Rocklin to discuss their journey to scale data science and machine learning in Python.

  Capital One, Dask, Distributed, Machine Learning, Python, scikit-learn, XGBoost

• Machine Learning for Cybersecurity Certificate at U. of Chicago - Feb 16, 2021.
  
  Hands-On Machine Learning Training from UChicago: 5-week remote Machine Learning for Cybersecurity certificate, Mar 30 - Apr 27. Learn from & network with leading faculty/industry leaders, learn data-driven prevention strategies. Group discounts, tuition support.

  Certificate, Cybersecurity, Machine Learning, U. of Chicago

• Easy, Open-Source AutoML in Python with EvalML - Feb 16, 2021.
  
  We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.

  Automated Machine Learning, AutoML, Machine Learning, Open Source, Python

• How to Speed up Scikit-Learn Model Training - Feb 11, 2021.
  
  Scikit-Learn is an easy to use a Python library for machine learning. However, sometimes scikit-learn models can take a long time to train. The question becomes, how do you create the best scikit-learn model in the least amount of time?

  Distributed Systems, Hyperparameter, Machine Learning, Optimization, Parallelism, Python, scikit-learn, Training

• Machine Learning – it’s all about assumptions - Feb 11, 2021.
  
  Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.

  Algorithms, Decision Trees, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, SVM, XGBoost

• A Critical Comparison of Machine Learning Platforms in an Evolving Market - Feb 11, 2021.
  
  There’s a clear inclination towards the MLaaS model across industries, given the fact that companies today have an option to select from a wide range of solutions that can cater to diverse business needs. Here is a look at 3 of the top ML platforms for data excellence.

  Google Cloud, IBM Watson, Machine Learning, Microsoft Azure, Platform

• My machine learning model does not learn. What should I do? - Feb 10, 2021.
  
  This article presents 7 hints on how to get out of the quicksand.

  Algorithms, Business Context, Data Quality, Hyperparameter, Machine Learning, Modeling, Tips

• Microsoft Explores Three Key Mysteries of Ensemble Learning - Feb 8, 2021.
  
  A new paper studies three key puzzling characteristics of deep learning ensembles and some potential explanations.

  Ensemble Methods, Machine Learning, Microsoft

• Saving and loading models in TensorFlow — why it is important and how to do it - Feb 3, 2021.
  
  So much time and effort can go into training your machine learning models. But, shut down the notebook or system, and all those trained weights and more vanish with the memory flush. Saving your models to maximize reusability is key for efficient productivity.

  Deep Learning, Machine Learning, TensorFlow

• Machine learning adversarial attacks are a ticking time bomb - Jan 29, 2021.
  
  Software developers and cyber security experts have long fought the good fight against vulnerabilities in code to defend against hackers. A new, subtle approach to maliciously targeting machine learning models has been a recent hot topic in research, but its statistical nature makes it difficult to find and patch these so-called adversarial attacks. Such threats in the real-world are becoming imminent as the adoption of machine learning spreads, and a systematic defense must be implemented.

  Adversarial, Generative Adversarial Network, Machine Learning

• Top 5 Reasons Why Machine Learning Projects Fail - Jan 28, 2021.
  
  The rise in machine learning project implementation is coming, as is the the number of failures, due to several implementation and maintenance challenges. The first step of closing this gap lies in understanding the reasons for the failure.

  Data Preparation, Data Science, Failure, Implementation, Machine Learning

• Machine learning is going real-time - Jan 28, 2021.
  
  Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?

  China, Machine Learning, MLOps, Real-time, Stream Processing

• Popular Machine Learning Interview Questions, part 2 - Jan 27, 2021.
  
  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.

  Convolutional Neural Networks, Interview Questions, Linear Regression, Logistic Regression, Machine Learning, Regularization, Transfer Learning, Unbalanced

• Support Vector Machine for Hand Written Alphabet Recognition in R - Jan 27, 2021.
  
  We attempt to break down a problem of hand written alphabet image recognition into a simple process rather than using heavy packages. This is an attempt to create the data and then build a model using Support Vector Machines for Classification.

  Classification, Image Recognition, Machine Learning, R, Support Vector Machines

• KDnuggets™ News 21:n04, Jan 27: The Ultimate Scikit-Learn Machine Learning Cheatsheet; Building a Deep Learning Based Reverse Image Search - Jan 27, 2021.
  
  The Ultimate Scikit-Learn Machine Learning Cheatsheet; Building a Deep Learning Based Reverse Image Search; Data Engineering — the Cousin of Data Science, is Troublesome; Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning; Popular Machine Learning Interview Questions

  Cheat Sheet, Data Engineering, Data Science, Deep Learning, GitHub, Image Recognition, Machine Learning, scikit-learn, Search

• Want to Be a Data Scientist? Don’t Start With Machine Learning - Jan 26, 2021.
  
  Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data.

  Career Advice, Data Scientist, Machine Learning, Statistics

• The Ultimate Scikit-Learn Machine Learning Cheatsheet - Jan 25, 2021.
  
  With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.

  Cheat Sheet, Machine Learning, scikit-learn

• Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants - Jan 22, 2021.
  
  Kaggle’s survey of ‘State of Data Science and Machine Learning 2020’ covers a lot of diverse topics. In this post, we are going to look at the popularity of cloud computing platforms and products among the data science and ML professionals participated in the survey.

  AWS, Cloud Computing, Data Science, Google Cloud, Kaggle, Machine Learning, Microsoft Azure, Trends

• How to Use MLOps for an Effective AI Strategy - Jan 21, 2021.
  
  The need to deal with the challenges and other smaller nuances of deploying machine learning models has given rise to the relatively new concept of MLOps. – a set of best practices aimed at automating the ML lifecycle, bringing together the ML system development and ML system operations.

  AI, DevOps, Machine Learning, MLOps, Strategy

• Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning - Jan 21, 2021.
  
  Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?

  AI, Career Advice, GitHub, Machine Learning

• Popular Machine Learning Interview Questions - Jan 20, 2021.
  
  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.

  Bias, Confusion Matrix, Interview Questions, Machine Learning, Overfitting, Variance

• K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines - Jan 15, 2021.
  
  K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.

  Algorithms, K-means, Machine Learning, scikit-learn

• KDnuggets™ News 21:n02, Jan 13: Best Python IDEs and Code Editors; 10 Underappreciated Python Packages for Machine Learning Practitioners - Jan 13, 2021.
  
  Best Python IDEs and Code Editors You Should Know; 10 Underappreciated Python Packages for Machine Learning Practitioners; Top 10 Computer Vision Papers 2020; CatalyzeX: A must-have browser extension for machine learning engineers and researchers

  Computer Vision, Data Science, DevOps, IDE, Machine Learning, MLOps, Python, Research

• 5 Tools for Effortless Data Science - Jan 11, 2021.
  
  The sixth tool is coffee.

  Data Science, Data Science Tools, Keras, Machine Learning, MLflow, PyCaret, Python

• CatalyzeX: A must-have browser extension for machine learning engineers and researchers - Jan 6, 2021.
  
  CatalyzeX is a free browser extension that finds code implementations for ML/AI papers anywhere on the internet (Google, Arxiv, Twitter, Scholar, and other sites).

  Implementation, Machine Learning, Programming, Research

• MLOps: Model Monitoring 101 - Jan 6, 2021.
  
  Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.

  AI, Data Science, DevOps, Machine Learning, MLOps, Modeling

• All Machine Learning Algorithms You Should Know in 2021 - Jan 4, 2021.
  
  Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.

  Algorithms, Decision Trees, Explained, Gradient Boosting, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM

• 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 - Dec 31, 2020.
  
  We present a curated list of 15 free eBooks compiled in a single location to close out the year.

  Automated Machine Learning, Data Science, Deep Learning, Free ebook, Machine Learning, NLP, Python, R, Statistics

• How to easily check if your Machine Learning model is fair? - Dec 24, 2020.
  
  Machine learning models deployed today -- as will many more in the future -- impact people and society directly. With that power and influence resting in the hands of Data Scientists and machine learning engineers, taking the time to evaluate and understand if model results are fair will become the linchpin for the future success of AI/ML solutions. These are critical considerations, and using a recently developed fairness module in the dalex Python package is a unified and accessible way to ensure your models remain fair.

  Bias, Dalex, Ethics, Machine Learning

• Can you trust AutoML? - Dec 23, 2020.
  
  Automated Machine Learning, or AutoML, tries hundreds or even thousands of different ML pipelines to deliver models that often beat the experts and win competitions. But, is this the ultimate goal? Can a model developed with this approach be trusted without guarantees of predictive performance? The issue of overfitting must be closely considered because these methods can lead to overestimation -- and the Winner's Curse.

  Accuracy, AutoML, Cross-validation, Machine Learning, Model Performance, Overfitting

• 5 strategies for enterprise machine learning for 2021 - Dec 22, 2020.
  
  While it is important for enterprises to continue solving the past challenges in a machine learning pipeline (manage, monitor, track experiments and models) in 2021 enterprises should focus on strategies to achieve scalability, elasticity and operationalization of machine learning.

  Enterprise, Machine Learning, Strategy

• Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance - Dec 21, 2020.
  
  A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.

  AI, Deployment, Explainable AI, Machine Learning, Modeling, Outliers, Production, Python

• MLOps Is Changing How Machine Learning Models Are Developed - Dec 21, 2020.
  
  Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence.

  Deployment, Machine Learning, MLOps

• ebook: Fundamentals for Efficient ML Monitoring - Dec 17, 2020.
  
  We've gathered best practices for data science and engineering teams to create an efficient framework to monitor ML models. This ebook provides a framework for anyone who has an interest in building, testing, and implementing a robust monitoring strategy in their organization or elsewhere.

  ebook, Machine Learning, Monitoring

• How to use Machine Learning for Anomaly Detection and Conditional Monitoring - Dec 16, 2020.
  
  This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.

  Anomaly Detection, Machine Learning, Python, scikit-learn, Unsupervised Learning

• KDnuggets™ News 20:n47, Dec 16: A Rising Library Beating Pandas in Performance; R or Python? Why Not Both? - Dec 16, 2020.
  
  Also: 10 Python Skills They Don't Teach in Bootcamp; Data Science Volunteering: Ways to Help; A Journey from Software to Machine Learning Engineer; Data Science and Machine Learning: The Free eBook

  Data Science, Free ebook, IDE, Machine Learning, Machine Learning Engineer, Pandas, Python, R

• Data Science and Machine Learning: The Free eBook - Dec 15, 2020.
  
  Check out the newest addition to our free eBook collection, Data Science and Machine Learning: Mathematical and Statistical Methods, and start building your statistical learning foundation today.

  Data Science, Free ebook, Machine Learning, Python

• State of Data Science and Machine Learning 2020: 3 Key Findings - Dec 15, 2020.
  
  Kaggle recently released its State of Data Science and Machine Learning report for 2020, based on compiled results of its annual survey. Read about 3 key findings in the report here.

  Data Science, Kaggle, Machine Learning, Survey

• Implementing the AdaBoost Algorithm From Scratch - Dec 10, 2020.
  
  AdaBoost technique follows a decision tree model with a depth equal to one. AdaBoost is nothing but the forest of stumps rather than trees. AdaBoost works by putting more weight on difficult to classify instances and less on those already handled well. AdaBoost algorithm is developed to solve both classification and regression problem. Learn to build the algorithm from scratch here.

  Adaboost, Algorithms, Ensemble Methods, Machine Learning, Python

• A Journey from Software to Machine Learning Engineer - Dec 10, 2020.
  
  In this blog post, the author explains his journey from Software Engineer to Machine Learning Engineer. The focus of the blog post is on the areas that the author wished he'd have focused on during his learning journey, and what should you look for outside of books and courses when pursuing your Machine Learning career.

  Career Advice, Machine Learning, Machine Learning Engineer, Software Engineer

• Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology - Dec 9, 2020.
  
  Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.

  2021 Predictions, AI, AutoML, Bill Schmarzo, Carla Gentry, COVID-19, Doug Laney, GPT-3, Kirk D. Borne, Machine Learning, MLOps, Predictions, Ronald van Loon, Tom Davenport, Trends

• AI registers: finally, a tool to increase transparency in AI/ML - Dec 9, 2020.
  
  Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why they are important and what you need to do, no tools have existed until now.

  AI, Bias, Ethics, Explainability, Helsinki, Machine Learning, Trust

• Machine Learning: Cutting Edge Tech with Deep Roots in Other Fields - Dec 8, 2020.
  
  Join INFORMS community of data, analytics, operations research, and statistics professionals and tackle the future together. With nearly 13,000 members around the world, INFORMS is the largest international association for data science professionals.

  INFORMS, Machine Learning, Operations Research

• Change the Background of Any Video with 5 Lines of Code - Dec 7, 2020.
  
  Learn to blur, color, grayscale and create a virtual background for a video with PixelLib.

  Computer Vision, Image Processing, Machine Learning, Python, Segmentation, Video

• Pruning Machine Learning Models in TensorFlow - Dec 4, 2020.
  
  Read this overview to learn how to make your models smaller via pruning.

  Machine Learning, Modeling, Python, TensorFlow

• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 - Dec 3, 2020.
  
  2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.

  2021 Predictions, AI, Ajit Jaokar, Analytics, Brandon Rohrer, Daniel Tunkelang, Data Science, Deep Learning, Machine Learning, Pedro Domingos, Predictions, Research, Rosaria Silipo

• Is Your Machine Learning Model Likely to Fail? - Nov 27, 2020.
  
  Read about these 5 missteps to avoid in your planning process.

  Agile, Diversity, Machine Learning, Production

• How to Know if a Neural Network is Right for Your Machine Learning Initiative - Nov 26, 2020.
  
  It is important to remember that there must be a business reason for even considering neural nets and it should not be because the C-Suite is feeling a bad case of FOMO.

  Algorithms, Machine Learning, Neural Networks

• Better data apps with Streamlit’s new layout options - Nov 26, 2020.
  
  Introducing new layout primitives - including columns, containers and expanders!

  App, Data Science, Machine Learning, Streamlit

• Essential Math for Data Science: Integrals And Area Under The Curve - Nov 25, 2020.
  
  In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.

  Machine Learning, Mathematics, Metrics, numpy, Python, Unbalanced

• How to Incorporate Tabular Data with HuggingFace Transformers - Nov 25, 2020.
  
  In real-world scenarios, we often encounter data that includes text and tabular features. Leveraging the latest advances for transformers, effectively handling situations with both data structures can increase performance in your models.

  Data Preparation, Deep Learning, Machine Learning, NLP, Python, Transformer

• Fraud through the eyes of a machine - Nov 24, 2020.
  
  Data structured as a network of relationships can be modeled as a graph, which can then help extract insights into the data through machine learning and rule-based approaches. While these graph representations provide a natural interface to transactional data for humans to appreciate, caution and context must be applied when leveraging machine-based interpretations of these connections.

  Fraud, Fraud Detection, Graph Analytics, Machine Learning

• Know-How to Learn Machine Learning Algorithms Effectively - Nov 23, 2020.
  
  The takeaway from the story is that machine learning is way beyond a simple fit and predict methods. The author shares their approach to actually learning these algorithms beyond the surface.

  Algorithms, Complexity, Interpretability, Machine Learning

• How Machine Learning Works for Social Good - Nov 21, 2020.
  
  We often discuss applying data science and machine learning techniques in term so of how they help your organization or business goals. But, these algorithms aren't limited to only increasing the bottom line. Developing new applications that leverage the predictive power of AI to benefit society and those communities in need is an equally valuable endeavor for Data Scientists that will further expand the positive impact of machine learning to the world.

  Advice, Chicago, Machine Learning, Social Good

• Cellular Automata in Stream Learning - Nov 20, 2020.
  
  In this post, we will start presenting CA as pattern recognition methods for stream learning. Finally, we will briefly mention two recent CA-based solutions for stream learning. Both are highly interpretable as their cellular structure represents directly the mapping between the feature space and the labels to be predicted.

  Algorithms, Machine Learning, Streaming Analytics, Tom Fawcett

• Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for AI - Nov 19, 2020.
  
  "It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.

  AI, AlphaGo, Machine Learning, OpenAI, Richard Sutton, Scalability, Trends

• Primer on TensorFlow and how PerceptiLabs Makes it Easier - Nov 18, 2020.
  
  With PerceptiLabs, beginners can get started building a model more quickly, and those with more experience can still dive into the code. Given that PerceptiLabs runs TensorFlow behind the scenes, we thought we'd walk through the framework so you can understand its basics, and how it is utilized by PerceptiLabs.

  AutoML, Deep Learning, Machine Learning, MLOps, PerceptiLabs, TensorFlow

• 5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use - Nov 18, 2020.
  
  If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.

  Data Science Tools, Data Scientist, GitHub, Heroku, Machine Learning, Postgres, PyCharm, PyTorch, scikit-learn, Streamlit