Machine Learning (1326)

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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