Many articles have been written about the top machine learning algorithms: click here and here for instance. Most of them seem to define top as oldest, and thus most used, ignoring modern, efficient algorithms fit for big data, such as indexation, attribution modeling, collaborative filtering, or recommendation engines used by companies such as Amazon, Google, or Facebook. I received this morning and advertisement for a (self-published) book called Master Machine Learning Algorithms, and I could not resist to post the author's list of top 10 machine learning algorithms:: Some of these techniques such as Naive Bayes (variables are almost never uncorrelated), Linear Discriminant Analysis (clusters are almost never separated by hyperplanes), or Linear Regression (numerous model assumptions - including linearity - are almost always violated in real data) have been so abused that I would hesitate teaching them. This is not a criticism of the book; most textbooks mention pretty much the same algorithms, and in this case, even skipping all graph-related algorithms. Even k Nearest Neighbors have modern, fast implementations not covered in traditional books - we are indeed working on this topic and expect to have an article published shortly about it.

Many articles have been written about the top machine learning algorithms: click here and here for instance. Most of them seem to define top as oldest, and thus most used, ignoring modern, efficient algorithms fit for big data, such as indexation, attribution modeling, collaborative filtering, or recommendation engines used by companies such as Amazon, Google, or Facebook.

Many articles have been written about the top machine learning algorithms: click here and here for instance. I received this morning and advertisement for a (self-published) book called Master Machine Learning Algorithms, and I could not resist to post the author's list of top 10 machine learning algorithms:: You can check the book here. You might have to attend classes taught by real practitioners (people who worked for big data solutions vendors) to learn modern tools that will give you a competitive edge on the job market. My upcoming book Data Science 2.0 will cover much of the topic, and my previous Wiley book is a good starting point.

Machine learning is an intimidating topic to tackle for the first time. The term encompasses so many fields, research topics and business use cases, that it can be difficult to even know where to start. To combat this, it's often a good idea to turn to textbooks that will introduce you to the basic principles of your new field of research. This holds true for AI and machine learning, especially if you have a background in statistics or programming. When used alongside more focused online articles like our introduction to training data, they can be an essential part of a powerful toolkit with which to learn and grow.