Topic: Machine Learning
This page features most recent and most popular posts on Machine Learning.
Latest posts on Machine Learning
- Zero-Shot Learning: Can you classify an object without seeing it before? - Apr 12, 2021Developing machine learning models that can perform predictive functions on data it has never seen before has become an important research area called zero-shot learning. We tend to be pretty great at recognizing things in the world we never saw before, and zero-shot learning offers a possible path toward mimicking this powerful human capability.
- Why machine learning struggles with causality - Apr 8, 2021If there's one thing people know how to do, and that's guess what caused something else to happen. Usually these guesses are good, especially when making a visual observation of something in the physical world. AI continues to wrestle with such inference of causality, and fundamental challenges must be overcome before we can have "intuitive" machine learning.
- KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable - Apr 7, 2021Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1
- How Noisy Labels Impact Machine Learning Models - Apr 6, 2021Not 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.
- How to Dockerize Any Machine Learning Application - Apr 6, 2021How 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.
Most popular (badge-winning) recent posts on Machine Learning
- 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.
- More Data Science Cheatsheets [Gold 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 [Silver 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.
- 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.
- State of Data Science and Machine Learning 2020: 3 Key Findings [Gold Blog]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.
- Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology [Gold Blog]Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 [Silver Blog]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.
- Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision [Gold Blog]This article compiles the 30 top Python libraries for deep learning, natural language processing & computer vision, as best determined by KDnuggets staff.
- Top 5 Free Machine Learning and Deep Learning eBooks Everyone should read [Gold Blog]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.
- Top Python Libraries for Data Science, Data Visualization & Machine Learning [Platinum Blog]This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
Interpretable Machine Learning: The Free eBook