Topic: Machine Learning
This page features most recent and most popular posts on Machine Learning.
Latest posts on Machine Learning
- Mastering Clustering with a Segmentation Problem - Aug 3, 2021The one stop shop for implementing the most widely used models in Python for unsupervised clustering.
- 30 Most Asked Machine Learning Questions Answered - Aug 3, 2021There 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.
- 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks - Aug 2, 2021While 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.
- 10 Machine Learning Model Training Mistakes - Jul 30, 2021These common ML model training mistakes are easy to overlook but costly to redeem.
- Building Machine Learning Pipelines using Snowflake and Dask - Jul 28, 2021In 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.
Most popular (badge-winning) recent posts on Machine Learning
- 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.
- 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.