Topic: Machine Learning
This page features most recent and most popular posts on Machine Learning.
Latest posts on Machine Learning
- How to calculate confidence intervals for performance metrics in Machine Learning using an automatic bootstrap method - Oct 15, 2021Are your model performance measurements very precise due to a “large” test set, or very uncertain due to a “small” or imbalanced test set?
- Deploying Your First Machine Learning API - Oct 14, 2021Effortless way to develop and deploy your machine learning API using FastAPI and Deta.
- The 20 Python Packages You Need For Machine Learning and Data Science - Oct 14, 2021Do you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.
- Building Multimodal Models: Using the widedeep Pytorch package - Oct 13, 2021This article gets you started on the open-source widedeep PyTorch framework developed by Javier Rodriguez Zaurin.
- Dealing with Data Leakage - Oct 8, 2021Target leakage and data leakage represent challenging problems in machine learning. Be prepared to recognize and avoid these potentially messy problems.
Most popular (badge-winning) recent posts on Machine Learning
- 20 Machine Learning Projects That Will Get You Hired [Silver Blog]If you want to break into the machine learning and data science job market, then you will need to demonstrate the proficiency of your skills, especially if you are self-taught through online courses and bootcamps. A project portfolio is a great way to practice your new craft and offer convincing evidence that an employee should hire you over the competition.
- Nine Tools I Wish I Mastered Before My PhD in Machine Learning [Gold Blog]Whether you are building a start up or making scientific breakthroughs these tools will bring your ML pipeline to the next level.
- How to Find Weaknesses in your Machine Learning Models [Gold Blog]FreaAI: a new method from researchers at IBM.
- The Machine & Deep Learning Compendium Open Book [Gold Blog]After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management.
- Top 18 Low-Code and No-Code Machine Learning Platforms [Silver Blog]Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI.
- Learning Data Science and Machine Learning: First Steps After The Roadmap [Silver Blog]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.
- 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks [Gold Blog]While there may always seem to be something new, cool, and shiny in the field of AI/ML, classic statistical methods that leverage machine learning techniques remain powerful and practical for solving many real-world business problems.
- Design patterns in machine learning [Silver Blog]Can we abstract best practices to real design patterns yet?
- Advice for Learning Data Science from Google’s Director of Research [Silver Blog]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.
- 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.