Machine Learning (2045)

• How to Future-Proof Your Data Science Project - Nov 18, 2020.
  
  This article outlines 5 critical elements of ML model selection & deployment.

  Data Science, Interpretability, Machine Learning, Model Drift, Modeling, Production

• 5 Things You Are Doing Wrong in PyCaret - Nov 16, 2020.
  
  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. Find out 5 ways to improve your usage of the library.

  Machine Learning, PyCaret, Python, Tips

• Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision - Nov 16, 2020.
  
  This article compiles the 30 top Python libraries for deep learning, natural language processing & computer vision, as best determined by KDnuggets staff.

  Computer Vision, Data Science, Deep Learning, Machine Learning, Neural Networks, NLP, Python

• When Machine Learning Knows Too Much About You - Nov 14, 2020.
  
  If machine learning models predict personal information about you, even if it is unintentional, then what sort of ethical dilemma exists in that model? Where does the line need to be drawn? There have already been many such cases, some of which have become overblown folk lore while others are potentially serious overreaches of governments.

  Ethics, Machine Learning, Privacy, Sensitive Data

• tensorflow + dalex = :) , or how to explain a TensorFlow model - Nov 13, 2020.
  
  Having a machine learning model that generates interesting predictions is one thing. Understanding why it makes these predictions is another. For a tensorflow predictive model, it can be straightforward and convenient develop an explainable AI by leveraging the dalex Python package.

  Dalex, Explainability, Explainable AI, Machine Learning, Python, TensorFlow

• Predicting Heart Disease Using Machine Learning? Don’t! - Nov 10, 2020.
  
  I believe the “Predicting Heart Disease using Machine Learning” is a classic example of how not to apply machine learning to a problem, especially where a lot of domain experience is required.

  Advice, Failure, Healthcare, Machine Learning, Medical, Prediction

• Moving from Data Science to Machine Learning Engineering - Nov 10, 2020.
  
  The world of machine learning — and software — is changing. Read this article to find out how, and what you can do to stay ahead of it.

  Career Advice, Data Engineering, Data Science, Machine Learning, Machine Learning Engineer

• Doing the impossible? Machine learning with less than one example - Nov 9, 2020.
  
  Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.

  Algorithms, K-nearest neighbors, Machine Learning, Research

• Change the Background of Any Image with 5 Lines of Code - Nov 9, 2020.
  
  Blur, color, grayscale and change the background of any image with a picture using PixelLib.

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

• Top 5 Free Machine Learning and Deep Learning eBooks Everyone should read - Nov 5, 2020.
  
  There is always so much new to learn in machine learning, and keeping well grounded in the fundamentals will help you stay up-to-date with the latest advancements while acing your career in Data Science.

  Deep Learning, Free ebook, Machine Learning

• Interpretability, Explainability, and Machine Learning – What Data Scientists Need to Know - Nov 4, 2020.
  
  The terms “interpretability,” “explainability” and “black box” are tossed about a lot in the context of machine learning, but what do they really mean, and why do they matter?

  Explainability, Explainable AI, Interpretability, Machine Learning

• Dealing with Imbalanced Data in Machine Learning - Oct 29, 2020.
  
  This article presents tools & techniques for handling data when it's imbalanced.

  Balancing Classes, Machine Learning, Python

• Exploring the Significance of Machine Learning for Algorithmic Trading with Stefan Jansen - Oct 28, 2020.
  
  The immense expansion of digital data has increased the demand for proficiency in trading strategies that use machine learning (ML). Learn more from author Stefan Jansen, and get his latest book on the subject from Packt Publishing.

  Algorithms, Finance, Machine Learning, Packt Publishing

• An Introduction to AI, updated - Oct 28, 2020.
  
  We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.

  AGI, AI, Beginners, Deep Learning, Machine Learning

• DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models - Oct 23, 2020.
  
  Causal Bayesian Networks are used to model the influence of fairness attributes in a dataset.

  Bayesian Networks, Bias, DeepMind, Machine Learning

• Behavior Analysis with Machine Learning and R: The free eBook - Oct 22, 2020.
  
  Check out this new free ebook to learn how to leverage the power of machine learning to analyze behavioral patterns from sensor data and electronic records using R.

  Behavioral Analytics, Free ebook, Machine Learning, R

• 5 Must-Read Data Science Papers (and How to Use Them) - Oct 20, 2020.
  
  Keeping ahead of the latest developments in a field is key to advancing your skills and your career. Five foundational ideas from recent data science papers are highlighted here with tips on how to leverage these advancements in your work, and keep you on top of the machine learning game.

  Data Science, Machine Learning, P-value, Research, Software, Technical Debt, Transformer

• Feature Ranking with Recursive Feature Elimination in Scikit-Learn - Oct 19, 2020.
  
  This article covers using scikit-learn to obtain the optimal number of features for your machine learning project.

  Feature Selection, Machine Learning, Python, scikit-learn

• How to Explain Key Machine Learning Algorithms at an Interview - Oct 19, 2020.
  
  While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.

  Algorithms, Decision Trees, Interview Questions, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM

• DOE SMART Visualization Platform 1.5M Prize Challenge - Oct 16, 2020.
  
  The U.S. Department of Energy’s (DOE) Office of Fossil Energy (FE) will award up to $1.5 million to winning innovators in a prize challenge to support FE’s SMART initiative. Registration deadline to participate in the challenge is 11:59 p.m. EDT Friday, Jan 22, 2021.

  Competition, Data Visualization, Machine Learning

• Fast Gradient Boosting with CatBoost - Oct 16, 2020.
  
  In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.

  CatBoost, Gradient Boosting, Machine Learning, Python

• Machine Learning’s Greatest Omission: Business Leadership - Oct 15, 2020.
  
  Eric Siegel's business-oriented, vendor-neutral machine learning course is designed to fulfill vital unmet learner needs, delivering material critical for both techies and business leaders.

  Business, Data Leadership, Eric Siegel, Machine Learning

• Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform - Oct 13, 2020.
  
  The new release makes Ludwig one of the most complete open source AutoML stacks in the market.

  Automated Machine Learning, AutoML, Machine Learning, Open Source, Uber

• 5 Best Practices for Putting Machine Learning Models Into Production - Oct 12, 2020.
  
  Our focus for this piece is to establish the best practices that make an ML project successful.

  Best Practices, Machine Learning, Production

• Exploring The Brute Force K-Nearest Neighbors Algorithm - Oct 12, 2020.
  
  This article discusses a simple approach to increasing the accuracy of k-nearest neighbors models in a particular subset of cases.

  Algorithms, K-nearest neighbors, Machine Learning, Python

• Annotated Machine Learning Research Papers - Oct 9, 2020.
  
  Check out this collection of annotated machine learning research papers, and no longer fear their reading.

  Machine Learning, Research

• How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems - Oct 8, 2020.
  
  LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations.

  LinkedIn, Machine Learning, Recommendation Engine, Recommender Systems, Recruitment

• Free Introductory Machine Learning Course From Amazon - Oct 7, 2020.
  
  Amazon's Machine Learning University offers an introductory course titled Accelerated Machine Learning, which is a good starting place for those looking for a foundation in generalized practical ML.

  Amazon, Courses, Machine Learning, MOOC

• 5 Challenges to Scaling Machine Learning Models - Oct 7, 2020.
  
  ML models are hard to be translated into active business gains. In order to understand the common pitfalls in productionizing ML models, let’s dive into the top 5 challenges that organizations face.

  Deployment, Machine Learning, Scalability

• KDnuggets™ News 20:n38, Oct 7: 10 Essential Skills You Need to Know to Start Doing Data Science; The Best Free Data Science eBooks: 2020 Update - Oct 7, 2020.
  
  Also: Comparing the Top Business Intelligence Tools: Power BI vs Tableau vs Qlik vs Domo; 5 Concepts Every Data Scientist Should Know; Understanding Transformers, the Data Science Way; 10 Best Machine Learning Courses in 2020

  Courses, Data Analysis, Data Science, Data Science Skills, Data Visualization, Free ebook, Machine Learning, Transformer

• 10 Best Machine Learning Courses in 2020 - Oct 6, 2020.
  
  If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel.

  Courses, DataCamp, Deep Learning, fast.ai, Machine Learning, Online Education, Python, Stanford

• New U. of Chicago Machine Learning for Cybersecurity Certificate Gives Professionals Tools to Detect and Prevent Attacks - Oct 5, 2020.
  
  Machine learning has become an essential tool for IT security professionals seeking to detect and prevent attacks and vulnerabilities. The Center for Data and Computing (CDAC) convened a trio of University of Chicago computer science faculty to produce an innovative new remote Machine Learning for Cybersecurity certificate that will be offered for the first time this autumn.

  Machine Learning, Security, U. of Chicago

• Key Machine Learning Technique: Nested Cross-Validation, Why and How, with Python code - Oct 5, 2020.
  
  Selecting the best performing machine learning model with optimal hyperparameters can sometimes still end up with a poorer performance once in production. This phenomenon might be the result of tuning the model and evaluating its performance on the same sets of train and test data. So, validating your model more rigorously can be key to a successful outcome.

  Cross-validation, Machine Learning, Python

• Machine Learning Model Deployment - Sep 30, 2020.
  
  Read this article on machine learning model deployment using serverless deployment. Serverless compute abstracts away provisioning, managing severs and configuring software, simplifying model deployment.

  Cloud, Deployment, Machine Learning, Modeling, Workflow

• Missing Value Imputation – A Review - Sep 29, 2020.
  
  Detecting and handling missing values in the correct way is important, as they can impact the results of the analysis, and there are algorithms that can’t handle them. So what is the correct way?

  Data Preprocessing, Knime, Machine Learning, Missing Values

• International alternatives to Kaggle for Data Science / Machine Learning competitions - Sep 29, 2020.
  
  While Kaggle might be the most well-known, go-to data science competition platform to test your skills at model building and performance, additional regional platforms are available around the world that offer even more opportunities to learn... and win.

  Competition, Data Science, Kaggle, Machine Learning

• LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at Scale - Sep 23, 2020.
  
  The reference architecture is powering mission critical machine learning workflows within LinkedIn.

  Best Practices, LinkedIn, Machine Learning, Scalability

• How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy - Sep 23, 2020.
  
  Data science work typically requires a big lift near the end to increase the accuracy of any model developed. These five recommendations will help improve your machine learning models and help your projects reach their target goals.

  Accuracy, Ensemble Methods, Feature Engineering, Feature Selection, Hyperparameter, Machine Learning, Missing Values, Tips

• KDnuggets™ News 20:n36, Sep 23: New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project - Sep 23, 2020.
  
  New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project; Autograd: The Best Machine Learning Library You're Not Using?; Implementing a Deep Learning Library from Scratch in Python; Online Certificates/Courses in AI, Data Science, Machine Learning; Can Neural Networks Show Imagination?

  Automation, Certificate, Courses, Data Science, Deep Learning, DeepMind, Machine Learning, Neural Networks, Python

• Machine Learning from Scratch: Free Online Textbook - Sep 22, 2020.
  
  If you are looking for a machine learning starter that gets right to the core of the concepts and the implementation, then this new free textbook will help you dive in to ML engineering with ease. By focusing on the basics of the underlying algorithms, you will be quickly up and running with code you construct yourself.

  Beginners, Free ebook, Machine Learning, Online Education

• What an Argentine Writer and a Hungarian Mathematician Can Teach Us About Machine Learning Overfitting - Sep 21, 2020.
  
  This article presents some beautiful ideas about intelligence and how they related to modern machine learning.

  Machine Learning, Mathematics, Overfitting

• Coursera’s Machine Learning for Everyone Fulfills Unmet Training Needs - Sep 17, 2020.
  
  Coursera's Machine Learning for Everyone (free access) fulfills two different kinds of unmet learner needs, for both the technology side and the business side, covering state-of-the-art techniques, business leadership best practices, and a wide range of common pitfalls and how to avoid them.

  Coursera, Courses, Eric Siegel, Machine Learning

• Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities - Sep 16, 2020.
  
  We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.

  AI, Business Analytics, Certificate, Columbia, Coursera, Courses, edX, Harvard, Machine Learning, MIT, Online Education, Stanford, Toronto, UC Berkeley, UCLA

• The Maslow’s hierarchy your data science team needs - Sep 15, 2020.
  
  Domino Data Lab was announced as a leader for the second year in a row in the recently released “Forrester Wave™: Notebook-based Predictive Analytics and Machine Learning (PAML), Q3 2020” analyst report. True to our data science roots, we’ve built a Maslow’s hierarchy of data science team needs.

  Data Science Team, Domino, Forrester, Machine Learning, Predictive Analytics

• Seven Reasons to Take This Course Before You Go Hands-On with Machine Learning - Sep 9, 2020.
  
  Eric Siegel's new course series on Coursera, Machine Learning for Everyone, is for any learner who wishes to participate in the business deployment of machine learning. This end-to-end, three-course series is accessible to business-level learners and yet vital to techies as well. It covers both the state-of-the-art techniques and the business-side best practices.

  Course, Coursera, Machine Learning, Online Education

• 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out - Sep 9, 2020.
  
  If you are just starting down a path toward a career in Data Science, or you are already a seasoned practitioner, then keeping active to advance your experience through side projects is invaluable to take you to the next professional level. These eight interesting project ideas with source code and reference articles will jump start you to thinking outside of the box.

  AI, Career, Face Recognition, Machine Learning, Music, Natural Language Generation, Portfolio, Sentiment Analysis, Text Summarization

• KDnuggets™ News 20:n34, Sep 9: Top Online Data Science Masters Degrees; Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills - Sep 9, 2020.
  
  Also: Creating Powerful Animated Visualizations in Tableau; PyCaret 2.1 is here: What's new?; How To Decide What Data Skills To Learn; How to Evaluate the Performance of Your Machine Learning Model

  Data Science, Data Science Skills, Data Visualization, Machine Learning, Master of Science, Modeling, Online Education, PyCaret, Tableau

• How to Evaluate the Performance of Your Machine Learning Model - Sep 3, 2020.
  
  You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.

  Accuracy, Confusion Matrix, Machine Learning, Precision, Predictive Modeling, Recall, ROC-AUC

• 10 Things You Didn’t Know About Scikit-Learn - Sep 3, 2020.
  
  Check out these 10 things you didn’t know about Scikit-Learn... until now.

  Machine Learning, Python, scikit-learn

• PyCaret 2.1 is here: What’s new? - Sep 1, 2020.
  
  PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up the machine learning experiment cycle and makes you 10x more productive. Read about what's new in PyCaret 2.1.

  Machine Learning, PyCaret, Python

• Microsoft’s DoWhy is a Cool Framework for Causal Inference - Aug 28, 2020.
  
  Inspired by Judea Pearl’s do-calculus for causal inference, the open source framework provides a programmatic interface for popular causal inference methods.

  Causality, Inference, Machine Learning, Microsoft

• 4 ways to improve your TensorFlow model – key regularization techniques you need to know - Aug 27, 2020.
  
  Regularization techniques are crucial for preventing your models from overfitting and enables them perform better on your validation and test sets. This guide provides a thorough overview with code of four key approaches you can use for regularization in TensorFlow.

  Machine Learning, Overfitting, Regularization, TensorFlow

• DeepMind’s Three Pillars for Building Robust Machine Learning Systems - Aug 24, 2020.
  
  Specification Testing, Robust Training and Formal Verification are three elements that the AI powerhouse believe hold the essence of robust machine learning models.

  AI, DeepMind, Machine Learning

• Rapid Python Model Deployment with FICO Xpress Insight - Aug 20, 2020.
  
  The biggest hurdle in the use of data to create business value, is indeed the ability to operationalize analytics throughout the organization. Xpress Insight is geared to reduce the burden on IT and address their critical requirements while empowering business users to take ownership of decisions and change management.

  AI, Deployment, FICO, Machine Learning, Optimization, Python

• Autotuning for Multi-Objective Optimization on LinkedIn’s Feed Ranking - Aug 19, 2020.
  
  In this post, the authors share their experience coming up with an automated system to tune one of the main parameters in their machine learning model that recommends content on LinkedIn’s Feed, which is just one piece of the community-focused architecture.

  Automated Machine Learning, AutoML, LinkedIn, Machine Learning, Optimization

• Accelerated Natural Language Processing: A Free Course From Amazon - Aug 19, 2020.
  
  Amazon's Machine Learning University is making its online courses available to the public, starting with this Accelerated Natural Language Processing offering.

  Amazon, Courses, Free, Machine Learning, NLP

• KDD-2020 (virtual), the leading conference on Data Science and Knowledge Discovery, Aug 23-27 – register now - Aug 18, 2020.
  
  Using an interactive VR platform, KDD-2020 brings you the latest research in AI, Data Science, Deep Learning, and Machine Learning with tutorials to improve your skills, keynotes from top experts, workshops on state-of-the-art topics and over 200 research presentations.

  ACM SIGKDD, COVID-19, Data Science, Deep Learning, KDD, KDD-2020, Machine Learning, Meetings, Research

• Top Google AI, Machine Learning Tools for Everyone - Aug 18, 2020.
  
  Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.

  AI, AutoML, Bias, Data Science Platforms, Datasets, Google, Google Cloud, Google Colab, Machine Learning, TensorFlow

• Are Computer Vision Models Vulnerable to Weight Poisoning Attacks? - Aug 17, 2020.
  
  A recent paper has explored the possibility of influencing the predictions of a freshly trained Natural Language Processing (NLP) model by tweaking the weights re-used in its training. his result is especially interesting if it proves to transfer also to the context of Computer Vision (CV) since there, the usage of pre-trained weights is widespread.

  Adversarial, AI, Computer Vision, Machine Learning, NLP

• Going Beyond Superficial: Data Science MOOCs with Substance - Aug 13, 2020.
  
  Data science MOOCs are superficial. At least, a lot of them are. What are your options when looking for something more substantive?

  Courses, Data Science, Machine Learning, MOOC

• Will Reinforcement Learning Pave the Way for Accessible True Artificial Intelligence? - Aug 11, 2020.
  
  Python Machine Learning, Third Edition covers the essential concepts of reinforcement learning, starting from its foundations, and how RL can support decision making in complex environments. Read more on the topic from the book's author Sebastian Raschka.

  Machine Learning, Packt Publishing, Python, Reinforcement Learning, Sebastian Raschka

• 10 Use Cases for Privacy-Preserving Synthetic Data - Aug 11, 2020.
  
  This article presents 10 use-cases for synthetic data, showing how enterprises today can use this artificially generated information to train machine learning models or share data externally without violating individuals' privacy.

  Compliance, Machine Learning, Privacy, Synthetic Data

• Facebook Uses Bayesian Optimization to Conduct Better Experiments in Machine Learning Models - Aug 10, 2020.
  
  A research from Facebook proposes a Beyasian optimization method to run A/B tests in machine learning models.

  Bayesian, Facebook, Machine Learning, Modeling, Optimization

• Essential Data Science Tips: How to Use One-Vs-Rest and One-Vs-One for Multi-Class Classification - Aug 6, 2020.
  
  Classification, as a predictive model, involves aligning each class label to examples. Algorithms designed for binary classification cannot be applied to multi-class classification problems. For such situations, heuristic methods come in handy.

  Classification, Machine Learning

• Word Embedding Fairness Evaluation - Aug 5, 2020.
  
  With word embeddings being such a crucial component of NLP, the reported social biases resulting from the training corpora could limit their application. The framework introduced here intends to measure the fairness in word embeddings to better understand these potential biases.

  Bias, Ethics, Machine Learning, Word Embeddings

• KDnuggets™ News 20:n30, Aug 5: What Employers are Expecting of Data Scientist Role; I have a joke about… - Aug 5, 2020.
  
  Know What Employers are Expecting for a Data Scientist Role in 2020; I have a joke about …; First Steps of a Data Science Project; Why You Should Get Google's New Machine Learning Certificate; Awesome Machine Learning and AI Courses

  AI, Career Advice, Certificate, Courses, Data Science, Data Scientist, Google, Humor, Machine Learning, Trends

• 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

• 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