This page features most recent and most popular posts on Machine Learning.
- 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 Python Libraries for Data Science, Data Visualization & Machine Learning [Gold Blog]
This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
- An Introduction to AI, updated [Silver Blog]
We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.
- How to Explain Key Machine Learning Algorithms at an Interview [Gold Blog]
While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.
- Annotated Machine Learning Research Papers [Silver Blog]
Check out this collection of annotated machine learning research papers, and no longer fear their reading.
- How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems [Silver Blog]
LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations.
- Free Introductory Machine Learning Course From Amazon [Silver Blog]
Amazon's Machine Learning University offers an introductory course titled Accelerated Machine Learning, which is a good starting place for those looking for a foundation in generalized practical ML.
- 10 Best Machine Learning Courses in 2020 [Gold Blog]
If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel.
- How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy [Silver Blog]
Data science work typically requires a big lift near the end to increase the accuracy of any model developed. These five recommendations will help improve your machine learning models and help your projects reach their target goals.
- Machine Learning from Scratch: Free Online Textbook [Gold Blog]
If you are looking for a machine learning starter that gets right to the core of the concepts and the implementation, then this new free textbook will help you dive in to ML engineering with ease. By focusing on the basics of the underlying algorithms, you will be quickly up and running with code you construct yourself.
- Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities [Silver Blog]
We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.
- 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out [Silver Blog]
If you are just starting down a path toward a career in Data Science, or you are already a seasoned practitioner, then keeping active to advance your experience through side projects is invaluable to take you to the next professional level. These eight interesting project ideas with source code and reference articles will jump start you to thinking outside of the box.
- How to Evaluate the Performance of Your Machine Learning Model [Silver Blog]
You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.
- 4 ways to improve your TensorFlow model – key regularization techniques you need to know [Gold Blog]
Regularization techniques are crucial for preventing your models from overfitting and enables them perform better on your validation and test sets. This guide provides a thorough overview with code of four key approaches you can use for regularization in TensorFlow.
- Top Google AI, Machine Learning Tools for Everyone [Silver Blog]
Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.
- Going Beyond Superficial: Data Science MOOCs with Substance [Silver Blog]
Data science MOOCs are superficial. At least, a lot of them are. What are your options when looking for something more substantive?
- Setting Up Your Data Science & Machine Learning Capability in Python [Silver Blog]
With the rich and dynamic ecosystem of Python continuing to be a leading programming language for data science and machine learning, establishing and maintaining a cost-effective development environment is crucial to your business impact. So, do you rent or buy? This overview considers the hidden and obvious factors involved in selecting and implementing your Python platform.
- Awesome Machine Learning and AI Courses [Gold Blog]
Check out this list of awesome, free machine learning and artificial intelligence courses with video lectures.
- Essential Resources to Learn Bayesian Statistics [Silver Blog]
If you are interesting in becoming better at statistics and machine learning, then some time should be invested in diving deeper into Bayesian Statistics. While the topic is more advanced, applying these fundamentals to your work will advance your understanding and success as an ML expert.
- Wrapping Machine Learning Techniques Within AI-JACK Library in R [Silver Blog]
The article shows an approach to solving problem of selecting best technique in machine learning. This can be done in R using just one library called AI-JACK and the article shows how to use this tool.
- The Bitter Lesson of Machine Learning [Gold Blog]
Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.