One of the ways physicians can gain some forewarning of impending heart failure is through the detection of excess fluid in the lungs, and MIT researchers have developed a new machine learning tool that could offer them a helping hand. The algorithm is able to detect severe cases of this condition with a high level of accuracy, and the researchers behind it are hopeful it could be adapted to assist with the management of other conditions, too. Learn more here: https://bit.ly/2HQaKR4

Dear colleagues,I publlished an article with the above title in the online newsletter of the Society of Petroleum Engineers. This is the latest in the series of such short pieces to explain machine learning foundational principles to petroleum engineers and to encourage its application.I thought it might equally be of benefit to someone here.The link to the article is here: https://pubs.spe.org/en/twa/twa-article-detail/?art=7313I will appreciate your feedback.

That's a good article and very well explained.I was using EML without realizing it... in form of Random Forest Algorithm.

While working on various dataset to train a Machine Learning model. What is it, that you look for? What is the most important part of the EDA phase? There are certain things which, if they are not done in the EDA phase, can affect further statistical / Machine Learning modelling. One of the answers is to, find “Outliers”.

One more thing which i missed in the article, that presence of outliers affects the distribution of the data. Let's examine what can happen to a data set with outliers. For the sample data set: 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4 We find the following: mean, median, mode, and standard deviation Mean = 2.58 Median = 2.5 Mode = 2 Standard Deviation = 1.08 If we add an outlier to the data set: 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 400 The new values of our statistics are: Mean = 35.38 Median = 2.5 Mode = 2 Standard Deviation = 114.74 It can be seen that having outliers often has a significant effect on the mean and standard deviation and hence affecting the distribution. We must take steps to remove outliers from our data sets. One of the easiest way to handle this is log transformation.Hope this information will help you.

Machine learning developers often encounter models that perform well on a data set, but dont live up to expectations when used on unseen or test data. This problem is called Overfitting. If you would like to learn more about Overfitting and how to avoid it, read on.

Very nice explanation of a very common problem. Examples are given in such a way that any one can understand easily. Also remedies for under fitting and over fitting are given in a structured manner.keep it up.

Decision Trees, the popular and time-tested method of applying logic to complex problems, where the variables are many and the options specific and dependent, have an important role to play within Machine Learning. We will dedicate this paper to understanding why this reasonably humble technique has become such an important tool for data scientists.

very nice paper for getting kick start. also choice of data set (iris data set) for explanation is also good as it is a well known bench mark data set and easy to understand as well. keep it up

Do you remember those afternoons in school or college when the professor was trying to teach you about things you never thought you would use in adult life? I bet regression and statistical algorithms are on that list. Well, it turns out that, with the realization of the importance of extracting knowledge from data, these techniques play an important role in Machine Learning and Data Science.

This white paper clearly explained about different regression concept with different real time examples.

This article is on where to start with Machine Learning.

Thank you. Very good noticeable point. Artificial intelligence is the prerequisite of machine learning.

Before getting into the definition, let’s first understand why we need Machine Learning and how it came into existence.

Great work. Liked its flow and thanks for writing this down.Like I said The only difference is that earlier was used and known to very few people and it was very time consuming. I

Machine Learning Algorithm used most commonly and the Types of ML. Just a short-notes on these.

How many algorithms are really put in use? What are the best practices? a great note and points to reflect

Thinking about using a Machine Learning model? Get your hands dirty with a practical lesson on SKLEARN.

you'll need a problem statement, data to process and enough time to try different approaches without deadlines pressing on you. - Great point