Search results for learn R

Found 5516 documents, 5944 searched:

• Math 2.0: The Fundamental Importance of Machine Learning

  Machine learning is not just another way to program computers; it represents a fundamental shift in the way we understand the world. It is Math 2.0.

  https://www.kdnuggets.com/2021/09/math-fundamental-importance-machine-learning.html

• How Machine Learning Leverages Linear Algebra to Solve Data Problems

  Why you should learn the fundamentals of linear algebra.

  https://www.kdnuggets.com/2021/09/machine-learning-leverages-linear-algebra-solve-data-problems.html

• ebook: Learn Data Science with R – free download

  Check out this new book for data science beginners with many practical examples that covers statistics, R, graphing, and machine learning. As a source to learn the full breadth of data science foundations, "Learn Data Science with R" starts at the beginner level and gradually progresses into expert content.

  https://www.kdnuggets.com/2021/09/ebook-learn-data-science-r.html

• How to solve machine learning problems in the real world

  Becoming a machine learning engineer pro is your goal? Sure, online ML courses and Kaggle-style competitions are great resources to learn the basics. However, the daily job of a ML engineer requires an additional layer of skills that you won’t master through these approaches.

  https://www.kdnuggets.com/2021/09/solve-machine-learning-problems-real-world.html

• Best Resources to Learn Natural Language Processing in 2021

  In this article, the author has listed listed all the best resources to learn natural language processing including Online Courses, Tutorials, Books, and YouTube Videos.

  https://www.kdnuggets.com/2021/09/best-resources-learn-natural-language-processing-2021.html

• How is Machine Learning Beneficial in Mobile App Development?

  Mobile app developers have a lot to gain by implementing AI & Machine Learning from the revolutionary changes that these disruptive technologies can offer. This is due to AI and ML's potential to strengthen mobile applications, providing for smoother user experiences capable of leveraging powerful features.

  https://www.kdnuggets.com/2021/09/machine-learning-beneficial-mobile-app-development.html

• Automated Data Labeling with Machine Learning

  Labeling training data is the one step in the data pipeline that has resisted automation. It’s time to change that.

  https://www.kdnuggets.com/2021/08/watchful-automated-data-labeling-machine-learning.html

• Learning Data Science and Machine Learning: First Steps After The Roadmap">Learning Data Science and Machine Learning: First Steps After The Roadmap

  Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.

  https://www.kdnuggets.com/2021/08/learn-data-science-machine-learning.html

• Enhancing Machine Learning Personalization through Variety

  Personalization drives growth and is a touchstone of good customer experience. Personalization driven through machine learning can enable companies to improve this experience while improving ROI for marketing campaigns. However, challenges exist in these techniques for when personalization makes sense and how and when specific options are recommended.

  https://www.kdnuggets.com/2021/08/machine-learning-personalization-variety.html

• Model Drift in Machine Learning – How To Handle It In Big Data

  Rendezvous Architecture helps you run and choose outputs from a Champion model and many Challenger models running in parallel without many overheads. The original approach works well for smaller data sets, so how can this idea adapt to big data pipelines?

  https://www.kdnuggets.com/2021/08/model-drift-machine-learning-big-data.html

• What I Learned From “Women in Data Science” Conferences

  Read the author's perspective after attending 3 "Women in Data Science" conferences.

  https://www.kdnuggets.com/2021/08/learned-women-data-science-conferences.html

• Introduction to Statistical Learning Second Edition

  The second edition of the classic "An Introduction to Statistical Learning, with Applications in R" was published very recently, and is now freely-available via PDF on the book's website.

  https://www.kdnuggets.com/2021/08/introduction-statistical-learning-v2.html

• MLOps And Machine Learning Roadmap

  A 16–20 week roadmap to review machine learning and learn MLOps.

  https://www.kdnuggets.com/2021/08/mlops-machine-learning-roadmap.html

• 2021 State of Production Machine Learning Survey

  We invite you to take the 2021 State of Production Machine Learning survey and help shed light on the latest trends in the adoption of machine learning (ML) in the industry. 

  https://www.kdnuggets.com/2021/08/anyscale-2021-state-production-machine-learning-survey.html

• How My Learning Path Changed After Becoming a Data Scientist

  I keep learning but in a different way.

  https://www.kdnuggets.com/2021/08/learning-path-changed-becoming-data-scientist.html

• Artificial Intelligence vs Machine Learning in Cybersecurity

  Artificial Intelligence and Machine Learning are the next-gen technology used in various fields. With the rise in online threats, it has become essential to include these technologies in cybersecurity. In this post, we will know what roles do AI and ML play in cybersecurity.

  https://www.kdnuggets.com/2021/08/artificial-intelligence-machine-learning-cybersecurity.html

• 30 Most Asked Machine Learning Questions Answered

  There is always a lot to learn in machine learning. Whether you are new to the field or a seasoned practitioner and ready for a refresher, understanding these key concepts will keep your skills honed in the right direction.

  https://www.kdnuggets.com/2021/08/30-machine-learning-questions-answered.html

• GPU-Powered Data Science (NOT Deep Learning) with RAPIDS">GPU-Powered Data Science (NOT Deep Learning) with RAPIDS

  How to utilize the power of your GPU for regular data science and machine learning even if you do not do a lot of deep learning work.

  https://www.kdnuggets.com/2021/08/gpu-powered-data-science-deep-learning-rapids.html

• 10 Machine Learning Model Training Mistakes

  These common ML model training mistakes are easy to overlook but costly to redeem.

  https://www.kdnuggets.com/2021/07/10-machine-learning-model-training-mistakes.html

• Building Machine Learning Pipelines using Snowflake and Dask

  In this post, I want to share some of the tools that I have been exploring recently and show you how I use them and how they helped improve the efficiency of my workflow. The two I will talk about in particular are Snowflake and Dask. Two very different tools but ones that complement each other well especially as part of the ML Lifecycle.

  https://www.kdnuggets.com/2021/07/building-machine-learning-pipelines-snowflake-dask.html

• Machine Learning Skills – Update Yours This Summer

  The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.

  https://www.kdnuggets.com/2021/07/update-your-machine-learning-skills.html

• Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics">Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics

  Modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Can we leverage the power of GPU and distributed computing for regular data processing jobs too?

  https://www.kdnuggets.com/2021/07/deep-learning-gpu-accelerate-data-science-data-analytics.html

• Why and how should you learn “Productive Data Science”?">Why and how should you learn “Productive Data Science”?

  What is Productive Data Science and what are some of its components?

  https://www.kdnuggets.com/2021/07/learn-productive-data-science.html

• Full cross-validation and generating learning curves for time-series models

  Standard cross-validation on time series data is not possible because the data model is sequential, which does not lend well to splitting the data into statistically useful training and validation sets. However, a new approach called Reconstructive Cross-validation may pave the way toward performing this type of important analysis for predictive models with temporal datasets.

  https://www.kdnuggets.com/2021/07/full-cross-validation-learning-curves-time-series.html

• ColabCode: Deploying Machine Learning Models From Google Colab

  New to ColabCode? Learn how to use it to start a VS Code Server, Jupyter Lab, or FastAPI.

  https://www.kdnuggets.com/2021/07/colabcode-deploying-machine-learning-models-google-colab.html

• Design patterns in machine learning">Design patterns in machine learning

  Can we abstract best practices to real design patterns yet?

  https://www.kdnuggets.com/2021/07/design-patterns-machine-learning.html

• When to Retrain an Machine Learning Model? Run these 5 checks to decide on the schedule

  Machine learning models degrade with time, and need to be regularly updated. In the article, we suggest how to approach retraining and plan for it in advance.

  https://www.kdnuggets.com/2021/07/retrain-machine-learning-model-5-checks-decide-schedule.html

• How Much Memory is your Machine Learning Code Consuming?

  Learn how to quickly check the memory footprint of your machine learning function/module with one line of command. Generate a nice report too.

  https://www.kdnuggets.com/2021/07/memory-machine-learning-code-consuming.html

• Advice for Learning Data Science from Google’s Director of Research">Advice for Learning Data Science from Google’s Director of Research

  Surfing the professional career wave in data science is a hot prospect for many looking to get their start in the world. The digital revolution continues to create many exciting new opportunities. But, jumping in too fast without fully establishing your foundational skills can be detrimental to your success, as is suggested by this advice for data science newbies from Peter Norvig, the Director of Research at Google.

  https://www.kdnuggets.com/2021/07/google-advice-learning-data-science.html

• How to Create Unbiased Machine Learning Models

  In this post we discuss the concepts of bias and fairness in the Machine Learning world, and show how ML biases often reflect existing biases in society. Additionally, We discuss various methods for testing and enforcing fairness in ML models.

  https://www.kdnuggets.com/2021/07/create-unbiased-machine-learning-models.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 5

  Training efficient deep learning models with any software tool is nothing without an infrastructure of robust and performant compute power. Here, current software and hardware ecosystems are reviewed that you might consider in your development when the highest performance possible is needed.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part5.html

• Pushing No-Code Machine Learning to the Edge

  Discover the power of no-code machine learning, and what it can accomplish when pushed to edge devices.

  https://www.kdnuggets.com/2021/07/pushing-no-code-machine-learning-edge.html

• 7 Open Source Libraries for Deep Learning Graphs

  In this article we’ll go through 7 up-and-coming open source libraries for graph deep learning, ranked in order of increasing popularity.

  https://www.kdnuggets.com/2021/07/7-open-source-libraries-deep-learning-graphs.html

• Geometric foundations of Deep Learning">Geometric foundations of Deep Learning

  Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

  https://www.kdnuggets.com/2021/07/geometric-foundations-deep-learning.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4

  With the right software, hardware, and techniques at your fingertips, your capability to effectively develop high-performing models now hinges on leveraging automation to expedite the experimental process and building with the most efficient model architectures for your data.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part4.html

• A Learning Path To Becoming a Data Scientist">A Learning Path To Becoming a Data Scientist

  Becoming a professional data scientist may not be as easy as "1... 2... 3...", but these 10 steps can be your self-learning roadmap to kickstarting your future in the exciting and ever-expanding field of data science.

  https://www.kdnuggets.com/2021/07/learning-path-data-scientist.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3

  Now that you are ready to efficiently build advanced deep learning models with the right software and hardware tools, the techniques involved in implementing such efforts must be explored to improve model quality and obtain the performance that your organization desires.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part3.html

• Learning Data Science Through Social Media

  Want your social media algorithms to show you actual algorithms? Spare a moment during your social media scrolling to learn a bit of data science. Here are suggestions for at-a-glance access to good ideas and tips on your favorite platforms.

  https://www.kdnuggets.com/2021/07/learning-data-science-through-social-media.html

• Computational Complexity of Deep Learning: Solution Approaches

  Why has deep learning been so successful? What is the fundamental reason that deep learning can learn from big data? Why cannot traditional ML learn from the large data sets that are now available for different tasks as efficiently as deep learning can?

  https://www.kdnuggets.com/2021/06/computational-complexity-deep-learning-solution-approaches.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 2

  As your organization begins to consider building advanced deep learning models with efficiency in mind to improve the power delivered through your solutions, the software and hardware tools required for these implementations are foundational to achieving high-performance.

  https://www.kdnuggets.com/2021/06/high-performance-deep-learning-part2.html

• In-Warehouse Machine Learning and the Modern Data Science Stack

  As your organization matures its data science portfolio and capabilities, establishing a modern data stack is vital to enabling such growth. Here, we overview various in-data warehouse machine learning services, and discuss each of their benefits and requirements.

  https://www.kdnuggets.com/2021/06/in-warehouse-machine-learning-modern-data-science-stack.html

• Amazing Low-Code Machine Learning Capabilities with New Ludwig Update

  Integration with Ray, MLflow and TabNet are among the top features of this release.

  https://www.kdnuggets.com/2021/06/ludwig-update-includes-low-code-machine-learning-capabilities.html

• Major changes: Where Analytics, Data Science, Machine Learning were applied in 2020/21

  Our latest poll shows major change in where AI, Data Science, Machine Learning are being applied, with decline in interest in traditional fields like CRM/Consumer Analytics, and growth in applications to Computer Vision, COVID, Agriculture, and Education.

  https://www.kdnuggets.com/2021/06/poll-where-analytics-data-science-ml-applied.html

• High Performance Deep Learning, Part 1

  Advancing deep learning techniques continue to demonstrate incredible potential to deliver exciting new AI-enhanced software and systems. But, training the most powerful models is expensive--financially, computationally, and environmentally. Increasing the efficiency of such models will have profound impacts in many ways, so developing future models with this intension in mind will only help to further expand the reach, applicability, and value of what deep learning has to offer.

  https://www.kdnuggets.com/2021/06/efficiency-deep-learning-part1.html

• The Best Way to Learn Practical NLP?

  Hugging Face has just released a course on using its libraries and ecosystem for practical NLP, and it appears to be very comprehensive. Have a look for yourself.

  https://www.kdnuggets.com/2021/06/best-way-learn-practical-nlp.html

• Facebook Launches One of the Toughest Reinforcement Learning Challenges in History

  The FAIR team just launched the NetHack Challenge as part of the upcoming NeurIPS 2021 competition. The objective is to test new RL ideas using a one of the toughest game environments in the world.

  https://www.kdnuggets.com/2021/06/facebook-launches-toughest-reinforcement-learning-challenges.html

• How to speed up a Deep Learning Language model by almost 50X at half the cost

  In this blog post, we show how to accelerate fine-tuning the ALBERT language model while also reducing costs by using Determined’s built-in support for distributed training with AWS spot instances.

  https://www.kdnuggets.com/2021/06/determined-ai-speed-up-deep-learning-language-model.html

• Machine Learning Model Interpretation

  Read this overview of using Skater to build machine learning visualizations.

  https://www.kdnuggets.com/2021/06/machine-learning-model-interpretation.html

• How I Doubled My Income with Data Science and Machine Learning">How I Doubled My Income with Data Science and Machine Learning

  Many career opportunities exist in the ever-expanding domain of data. Finding your place -- and finding your salary -- is largely up to your dedication, focus, and drive to learn. If you are an aspiring Data Scientist or have already started your professional journey, there are multiple strategies for maximizing your earning potential.

  https://www.kdnuggets.com/2021/06/double-income-data-science-machine-learning.html

• Supercharge Your Machine Learning Experiments with PyCaret and Gradio

  A step-by-step tutorial to develop and interact with machine learning pipelines rapidly.

  https://www.kdnuggets.com/2021/05/supercharge-machine-learning-experiments-pycaret-gradio.html

• Where Did You Apply Analytics, Data Science, Machine Learning in 2020/2021?

  Take part in the latest KDnuggets survey, and let us know where you have been applying Analytics, Data Science, Machine Learning in 2020/2021.

  https://www.kdnuggets.com/2021/05/poll-did-apply-analytics-data-science-machine-learning-2020-2021.html

• Write and train your own custom machine learning models using PyCaret

  A step-by-step, beginner-friendly tutorial on how to write and train custom machine learning models in PyCaret.

  https://www.kdnuggets.com/2021/05/pycaret-write-train-custom-machine-learning-models.html

• Data Validation in Machine Learning is Imperative, Not Optional

  Before we reach model training in the pipeline, there are various components like data ingestion, data versioning, data validation, and data pre-processing that need to be executed. In this article, we will discuss data validation, why it is important, its challenges, and more.

  https://www.kdnuggets.com/2021/05/data-validation-machine-learning-imperative.html

• How to Determine if Your Machine Learning Model is Overtrained">How to Determine if Your Machine Learning Model is Overtrained

  WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems.

  https://www.kdnuggets.com/2021/05/how-determine-machine-learning-model-overtrained.html

• Binary Classification with Automated Machine Learning

  Check out how to use the open-source MLJAR auto-ML to build accurate models faster.

  https://www.kdnuggets.com/2021/05/binary-classification-automated-machine-learning.html

• Make Connections With SAS Live Web Learning

  Through a year of uncertainty, the demand for analytics skills and the desire to continue skills development remained consistent. Take this opportunity to join SAS expert instructors and learn the latest skills in a Live Web class.

  https://www.kdnuggets.com/2021/05/sas-live-web-learning.html

• Essential Linear Algebra for Data Science and Machine Learning">Essential Linear Algebra for Data Science and Machine Learning

  Linear algebra is foundational in data science and machine learning. Beginners starting out along their learning journey in data science--as well as established practitioners--must develop a strong familiarity with the essential concepts in linear algebra.

  https://www.kdnuggets.com/2021/05/essential-linear-algebra-data-science-machine-learning.html

• A Comprehensive Guide to Ensemble Learning – Exactly What You Need to Know

  This article covers ensemble learning methods, and exactly what you need to know in order to understand and implement them.

  https://www.kdnuggets.com/2021/05/comprehensive-guide-ensemble-learning.html

• What makes a winning entry in a Machine Learning competition?

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

  https://www.kdnuggets.com/2021/05/winning-machine-learning-competition.html

• FluDemic – using AI and Machine Learning to get ahead of disease

  We are amidst a healthcare data explosion. AI/ML will be more vital than ever in the prevention and handling of future pandemics. Here, we walk you through the different facets of modeling infectious diseases, focusing on influenza and COVID-19.

  https://www.kdnuggets.com/2021/04/fludemic-ai-machine-learning-disease.html

• Learn Neural Networks for Natural Language Processing Now

  Still haven't come across enough quality contemporary natural language processing resources? Here is yet another freely-accessible offering from a top-notch university that might help quench your thirst for learning materials.

  https://www.kdnuggets.com/2021/04/learn-neural-networks-natural-language-processing-now.html

• Feature Engineering of DateTime Variables for Data Science, Machine Learning

  Learn how to make more meaningful features from DateTime type variables to be used by Machine Learning Models.

  https://www.kdnuggets.com/2021/04/feature-engineering-datetime-variables-data-science-machine-learning.html

• Best Podcasts for Machine Learning

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

  https://www.kdnuggets.com/2021/04/best-podcasts-machine-learning.html

• Getting Started with Reinforcement Learning

  Demystifying some of the main concepts and terminologies associated with Reinforcement Learning and their association with other fields of AI.

  https://www.kdnuggets.com/2021/04/getting-started-reinforcement-learning.html

• Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1

  New to data science? Interested in the must-know machine learning algorithms in the field? Check out the first part of our list and introductory descriptions of the top 10 algorithms for data scientists to know.

  https://www.kdnuggets.com/2021/04/top-10-must-know-machine-learning-algorithms-data-scientists-1.html

• Production-Ready Machine Learning NLP API with FastAPI and spaCy

  Learn how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER), and see why the author used FastAPI to quickly build a fast and robust machine learning API.

  https://www.kdnuggets.com/2021/04/production-ready-machine-learning-nlp-api-fastapi-spacy.html

• Free From Stanford: Machine Learning with Graphs

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

  https://www.kdnuggets.com/2021/04/free-stanford-machine-learning-graphs.html

• 6 Mistakes To Avoid While Training Your Machine Learning Model

  While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.

  https://www.kdnuggets.com/2021/04/cogitotech-6-mistakes-avoid-training-machine-learning.html

• Continuous Training for Machine Learning – a Framework for a Successful Strategy

  A basic appreciation by anyone who builds machine learning models is that the model is not useful without useful data. This doesn't change after a model is deployed to production. Effectively monitoring and retraining models with updated data is key to maintaining valuable ML solutions, and can be accomplished with effective approaches to production-level continuous training that is guided by the data.

  https://www.kdnuggets.com/2021/04/continuous-training-machine-learning.html

• Top March Stories: Are You Still Using Pandas to Process Big Data in 2021? Here are two better options; How To Overcome The Fear of Math and Learn Math For Data Science

  Also: Top YouTube Channels for Data Science; More Data Science Cheatsheets; Top 10 Python Libraries Data Scientists should know in 2021.

  https://www.kdnuggets.com/2021/04/top-stories-2021-mar.html

• Interpretable Machine Learning: The Free eBook">Interpretable Machine Learning: The Free eBook

  Interested in learning more about interpretability in machine learning? Check out this free eBook to learn about the basics, simple interpretable models, and strategies for interpreting more complex black box models.

  https://www.kdnuggets.com/2021/04/interpretable-machine-learning-free-ebook.html

• Deep Learning Recommendation Models (DLRM): A Deep Dive

  The currency in the 21st century is no longer just data. It's the attention of people. This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendation model, DLRM, which was open-sourced by Facebook in March 2019.

  https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html

• How Noisy Labels Impact Machine Learning Models

  Not all training data labeling errors have the same impact on the performance of the Machine Learning system. The structure of the labeling errors make a difference. Read iMerit’s latest blog to learn how to minimize the impact of labeling errors.

  https://www.kdnuggets.com/2021/04/imerit-noisy-labels-impact-machine-learning.html

• How to Dockerize Any Machine Learning Application

  How can you -- an awesome Data Scientist -- also be known as an awesome software engineer? Docker. And these 3 simple steps to use it for your solutions over and over again.

  https://www.kdnuggets.com/2021/04/dockerize-any-machine-learning-application.html

• How to deploy Machine Learning/Deep Learning models to the web">How to deploy Machine Learning/Deep Learning models to the web

  The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.

  https://www.kdnuggets.com/2021/04/deploy-machine-learning-models-to-web.html

• Shapash: Making Machine Learning Models Understandable">Shapash: Making Machine Learning Models Understandable

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

  https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html

• 15 Habits I Learned from Highly Effective Data Scientists

  I’m using these habits in 2021 to become a more effective future data scientist.

  https://www.kdnuggets.com/2021/03/15-habits-learned-from-highly-effective-data-scientists.html

• Top YouTube Machine Learning Channels

  These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.

  https://www.kdnuggets.com/2021/03/top-youtube-machine-learning-channels.html

• The Best Machine Learning Frameworks & Extensions for Scikit-learn">The Best Machine Learning Frameworks & Extensions for Scikit-learn

  Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.

  https://www.kdnuggets.com/2021/03/best-machine-learning-frameworks-extensions-scikit-learn.html

• Learning from machine learning mistakes

  Read this article and discover how to find weak spots of a regression model.

  https://www.kdnuggets.com/2021/03/learning-from-machine-learning-mistakes.html

• Data Validation and Data Verification – From Dictionary to Machine Learning

  In this article, we will understand the difference between data verification and data validation, two terms which are often used interchangeably when we talk about data quality. However, these two terms are distinct.

  https://www.kdnuggets.com/2021/03/data-validation-data-verification-dictionary-machine-learning.html

• 10 Amazing Machine Learning Projects of 2020">10 Amazing Machine Learning Projects of 2020

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

  https://www.kdnuggets.com/2021/03/10-amazing-machine-learning-projects-2020.html

• A Machine Learning Model Monitoring Checklist: 7 Things to Track">A Machine Learning Model Monitoring Checklist: 7 Things to Track

  Once you deploy a machine learning model in production, you need to make sure it performs. In the article, we suggest how to monitor your models and open-source tools to use.

  https://www.kdnuggets.com/2021/03/machine-learning-model-monitoring-checklist.html

• 4 Machine Learning Concepts I Wish I Knew When I Built My First Model">4 Machine Learning Concepts I Wish I Knew When I Built My First Model

  Diving into building your first machine learning model will be an adventure -- one in which you will learn many important lessons the hard way. However, by following these four tips, your first and subsequent models will be put on a path toward excellence.

  https://www.kdnuggets.com/2021/03/4-machine-learning-concepts.html

• Is It Too Late to Learn AI?

  Have you missed the train on learning AI?

  https://www.kdnuggets.com/2021/03/too-late-learn-ai.html

• Speeding up Scikit-Learn Model Training

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

  https://www.kdnuggets.com/2021/03/speed-up-scikit-learn-model-training.html

• Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret

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

  https://www.kdnuggets.com/2021/03/bayesian-hyperparameter-optimization-tune-sklearn-pycaret.html

• Getting Started with Distributed Machine Learning with PyTorch and Ray

  Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.

  https://www.kdnuggets.com/2021/03/getting-started-distributed-machine-learning-pytorch-ray.html

• Data Science Learning Roadmap for 2021">Data Science Learning Roadmap for 2021

  Venturing into the world of Data Science is an exciting, interesting, and rewarding path to consider. There is a great deal to master, and this self-learning recommendation plan will guide you toward establishing a solid understanding of all that is foundational to data science as well as a solid portfolio to showcase your developed expertise.

  https://www.kdnuggets.com/2021/02/data-science-learning-roadmap-2021.html

• Machine Learning Systems Design: A Free Stanford Course">Machine Learning Systems Design: A Free Stanford Course

  This freely-available course from Stanford should give you a toolkit for designing machine learning systems.

  https://www.kdnuggets.com/2021/02/machine-learning-systems-design-free-stanford-course.html

• Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall">Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall

  This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated, and how they relate to evaluating deep learning models.

  https://www.kdnuggets.com/2021/02/evaluating-deep-learning-models-confusion-matrix-accuracy-precision-recall.html

• Feature Store as a Foundation for Machine Learning

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

  https://www.kdnuggets.com/2021/02/feature-store-foundation-machine-learning.html

• Approaching (Almost) Any Machine Learning Problem">Approaching (Almost) Any Machine Learning Problem

  This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.

  https://www.kdnuggets.com/2021/02/approaching-almost-any-machine-learning-problem.html

• Distributed and Scalable Machine Learning [Webinar]

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

  https://www.kdnuggets.com/2021/02/coiled-distributed-machine-learning.html

• Deep Learning-based Real-time Video Processing

  In this article, we explore how to build a pipeline and process real-time video with Deep Learning to apply this approach to business use cases overviewed in our research.

  https://www.kdnuggets.com/2021/02/deep-learning-based-real-time-video-processing.html

• IBM Uses Continual Learning to Avoid The Amnesia Problem in Neural Networks

  Using continual learning might avoid the famous catastrophic forgetting problem in neural networks.

  https://www.kdnuggets.com/2021/02/ibm-continual-learning-avoid-amnesia-problem-neural-networks.html

• How to Speed up Scikit-Learn Model Training

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

  https://www.kdnuggets.com/2021/02/speed-up-scikit-learn-model-training.html

• Machine Learning – it’s all about assumptions

  Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.

  https://www.kdnuggets.com/2021/02/machine-learning-assumptions.html

• A Critical Comparison of Machine Learning Platforms in an Evolving Market

  There’s a clear inclination towards the MLaaS model across industries, given the fact that companies today have an option to select from a wide range of solutions that can cater to diverse business needs. Here is a look at 3 of the top ML platforms for data excellence.

  https://www.kdnuggets.com/2021/02/critical-comparison-machine-learning-platforms-evolving-market.html

• My machine learning model does not learn. What should I do?

  This article presents 7 hints on how to get out of the quicksand.

  https://www.kdnuggets.com/2021/02/machine-learning-model-not-learn.html

• 7 Most Recommended Skills to Learn to be a Data Scientist

  The Data Scientist professional has emerged as a true interdisciplinary role that spans a variety of skills, theoretical and practical. For the core, day-to-day activities, many critical requirements that enable the delivery of real business value reach well outside the realm of machine learning, and should be mastered by those aspiring to the field.

  https://www.kdnuggets.com/2021/02/7-most-recommended-skills-data-scientist.html

• Microsoft Explores Three Key Mysteries of Ensemble Learning

  A new paper studies three key puzzling characteristics of deep learning ensembles and some potential explanations.

  https://www.kdnuggets.com/2021/02/microsoft-explores-three-key-mysteries-ensemble-learning.html

• Deep learning doesn’t need to be a black box">Deep learning doesn’t need to be a black box

  The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this "black box" after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network's architecture -- before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.

  https://www.kdnuggets.com/2021/02/deep-learning-not-black-box.html

• Past 2021 Meetings / Online Events on AI, Analytics, Big Data, Data Science, and Machine Learning

  Past | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec Read more »

  https://www.kdnuggets.com/meetings/past-meetings-2021.html

• Machine learning adversarial attacks are a ticking time bomb

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

  https://www.kdnuggets.com/2021/01/machine-learning-adversarial-attacks.html

• Top 5 Reasons Why Machine Learning Projects Fail

  The rise in machine learning project implementation is coming, as is the the number of failures, due to several implementation and maintenance challenges. The first step of closing this gap lies in understanding the reasons for the failure.

  https://www.kdnuggets.com/2021/01/top-5-reasons-why-machine-learning-projects-fail.html

• Machine learning is going real-time

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

  https://www.kdnuggets.com/2021/01/machine-learning-real-time.html

• Popular Machine Learning Interview Questions, part 2

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions-part2.html

• Want to Be a Data Scientist? Don’t Start With Machine Learning">Want to Be a Data Scientist? Don’t Start With Machine Learning

  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.

  https://www.kdnuggets.com/2021/01/data-scientist-dont-start-machine-learning.html

• Deep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI

  Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to Craig Smith about his work In 2020 and what he sees on the horizon for AI.

  https://www.kdnuggets.com/2021/01/deep-learning-pioneer-geoff-hinton-research-future-ai.html

• The Ultimate Scikit-Learn Machine Learning Cheatsheet">The Ultimate Scikit-Learn Machine Learning Cheatsheet

  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.

  https://www.kdnuggets.com/2021/01/ultimate-scikit-learn-machine-learning-cheatsheet.html

• Building a Deep Learning Based Reverse Image Search">Building a Deep Learning Based Reverse Image Search

  Following the journey from unstructured data to content based image retrieval.

  https://www.kdnuggets.com/2021/01/deep-learning-based-reverse-image-search.html

• Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning

  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?

  https://www.kdnuggets.com/2021/01/github-career-growth-ai-machine-learning.html

• Popular Machine Learning Interview Questions">Popular Machine Learning Interview Questions

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions.html

• Graph Representation Learning: The Free eBook

  This free eBook can show you what you need to know to leverage graph representation in data science, machine learning, and neural network models.

  https://www.kdnuggets.com/2021/01/graph-representation-learning-book-free-ebook.html

• K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines">K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines

  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.

  https://www.kdnuggets.com/2021/01/k-means-faster-lower-error-scikit-learn.html

• 8 New Tools I Learned as a Data Scientist in 2020

  The author shares the data science tools learned while making the move from Docker to Live Deployments.

  https://www.kdnuggets.com/2021/01/8-new-tools-learned-data-scientist-2020.html

• Unsupervised Learning for Predictive Maintenance using Auto-Encoders

  This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; ​​​​Approaches for machine diagnosis; Machine diagnosis using machine learning

  https://www.kdnuggets.com/2021/01/unsupervised-learning-predictive-maintenance-auto-encoders.html

• My Data Science Learning Journey So Far">My Data Science Learning Journey So Far

  These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author.

  https://www.kdnuggets.com/2021/01/data-science-learning-journey.html

• Attention mechanism in Deep Learning, Explained

  Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.

  https://www.kdnuggets.com/2021/01/attention-mechanism-deep-learning-explained.html

• 10 Underappreciated Python Packages for Machine Learning Practitioners">10 Underappreciated Python Packages for Machine Learning Practitioners

  Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.

  https://www.kdnuggets.com/2021/01/10-underappreciated-python-packages-machine-learning-practitioners.html

• CatalyzeX: A must-have browser extension for machine learning engineers and researchers

  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).

  https://www.kdnuggets.com/2021/01/catalyzex-browser-extension-machine-learning.html

• Learn Data Science for free in 2021">Learn Data Science for free in 2021

  If you are considering starting a career path in machine learning and data science, then there is a great deal to learn theoretically, along with gaining practical skills in applying a broad range of techniques. This comprehensive learning plan will guide you to start on this path, and it is all available for free.

  https://www.kdnuggets.com/2021/01/learn-data-science-free-2021.html

• DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created">DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created

  MuZero takes a unique approach to solve the problem of planning in deep learning models.

  https://www.kdnuggets.com/2021/01/deepmind-muzero-important-deep-learning-systems.html

• All Machine Learning Algorithms You Should Know in 2021">All Machine Learning Algorithms You Should Know in 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.

  https://www.kdnuggets.com/2021/01/machine-learning-algorithms-2021.html

• 15 Free Data Science, Machine Learning & Statistics eBooks for 2021">15 Free Data Science, Machine Learning & Statistics eBooks for 2021

  We present a curated list of 15 free eBooks compiled in a single location to close out the year.

  https://www.kdnuggets.com/2020/12/15-free-data-science-machine-learning-statistics-ebooks-2021.html

• How to easily check if your Machine Learning model is fair?

  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.

  https://www.kdnuggets.com/2020/12/machine-learning-model-fair.html

• Top 9 Data Science Courses to Learn Online

  Learn Data Science from these top courses. Details like cost and course duration are included.

  https://www.kdnuggets.com/2020/12/simplilearn-top-9-data-science-courses-online.html

• Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance

  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.

  https://www.kdnuggets.com/2020/12/production-machine-learning-monitoring-outliers-drift-explainers-statistical-performance.html

• MLOps Is Changing How Machine Learning Models Are Developed

  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.

  https://www.kdnuggets.com/2020/12/mlops-changing-machine-learning-developed.html