Topic: Machine Learning
This page features most recent and most popular posts on Machine Learning.
Latest posts on Machine Learning
- An introduction to Explainable AI (XAI) and Explainable Boosting Machines (EBM) - Jun 16, 2021Understanding why your AI-based models make the decisions they do is crucial for deploying practical solutions in the real-world. Here, we review some techniques in the field of Explainable AI (XAI), why explainability is important, example models of explainable AI using LIME and SHAP, and demonstrate how Explainable Boosting Machines (EBMs) can make explainability even easier.
- 9 Deadly Sins of Machine Learning Dataset Selection - Jun 11, 2021Avoid endless pain in model debugging by focusing on datasets upfront.
- Feature Selection – All You Ever Wanted To Know - Jun 10, 2021Although your data set may contain a lot of information about many different features, selecting only the "best" of these to be considered by a machine learning model can mean the difference between a model that performs well--with better performance, higher accuracy, and more computational efficiency--and one that falls flat. The process of feature selection guides you toward working with only the data that may be the most meaningful, and to accomplish this, a variety of feature selection types, methodologies, and techniques exist for you to explore.
- KDnuggets™ News 21:n21, Jun 9: 5 Tasks To Automate With Python; How I Doubled My Income with Data Science and Machine Learning - Jun 9, 20215 Tasks To Automate With Python; How I Doubled My Income with Data Science and Machine Learning; Will There Be a Shortage of Data Science Jobs in the Next 5 Years?; How to Make Python Code Run Incredibly Fast; Stop (and Start) Hiring Data Scientists
- The only Jupyter Notebooks extension you truly need - Jun 8, 2021Now you don’t need to restart the kernel after editing the code in your custom imports.
Most popular (badge-winning) recent posts on Machine Learning
- How I Doubled My Income with Data Science and Machine Learning [Gold Blog]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.
- Data Scientist, Data Engineer & Other Data Careers, Explained [Platinum Blog]In this article, we will have a look at five distinct data careers, and hopefully provide some advice on how to get one's feet wet in this convoluted field.
- DeepMind Wants to Reimagine One of the Most Important Algorithms in Machine Learning [Silver Blog]In one of the most important papers this year, DeepMind proposed a multi-agent structure to redefine PCA.
- Data Science Books You Should Start Reading in 2021 [Gold Blog]Check out this curated list of the best data science books for any level.
- How to deploy Machine Learning/Deep Learning models to the web [Gold Blog]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.
- Awesome Tricks And Best Practices From Kaggle [Gold Blog]Easily learn what is only learned by hours of search and exploration.
- Shapash: Making Machine Learning Models Understandable [Gold Blog]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.
- The Best Machine Learning Frameworks & Extensions for Scikit-learn [Silver Blog]Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.
- More Data Science Cheatsheets [Platinum Blog]It's time again to look at some data science cheatsheets. Here you can find a short selection of such resources which can cater to different existing levels of knowledge and breadth of topics of interest.
- 10 Amazing Machine Learning Projects of 2020 [Silver Blog]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.
- A Machine Learning Model Monitoring Checklist: 7 Things to Track [Gold Blog]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.
- 4 Machine Learning Concepts I Wish I Knew When I Built My First Model [Silver Blog]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.
- Machine Learning Systems Design: A Free Stanford Course [Gold Blog]This freely-available course from Stanford should give you a toolkit for designing machine learning systems.
- Approaching (Almost) Any Machine Learning Problem [Silver Blog]This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.
- Want to Be a Data Scientist? Don’t Start With Machine Learning [Gold Blog]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.
- The Ultimate Scikit-Learn Machine Learning Cheatsheet [Gold Blog]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.
- Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants [Gold Blog]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.
- Popular Machine Learning Interview Questions [Silver Blog]Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.
- K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines [Gold Blog]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.
- All Machine Learning Algorithms You Should Know in 2021 [Platinum Blog]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.
- 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 [Platinum Blog]We present a curated list of 15 free eBooks compiled in a single location to close out the year.
