Just a try about Article Machine Learning

Introduction

The next post at the  of the year 2018 on our list of best-curated articles on – “Machine Learning”. These curated articles will be a one stop solution for people who are getting started with Machine Learning or who already have. This article contains all the best articles of 2018 which gathered the interest of the Machine Learning community.

Similar to the previous article on -“Best Deep Learning articles in 2018”, I have added the used tool and the level of difficulty for each article to facilitate you with the choice. If you wish to include any other learning resource/article here, please mention them in the comments.

A large amount of unstructured data present today is in the form of text, for example : Medical documents, legal agreements, tweets, blogs, newspapers, chat conversions etc. These text informations are the storehouse of new innovative products that can revolutionise the way we interact with the technology and live our lives. A few of the examples are:

1. An automated system which can go through your medical records to suggest which kind of food you should avoid.
2. An automated system which can go through a legal document to check its validity.
3. A chatbot which can help you in buying groceries to booking a cab just by typing over a phone.

This is just the tip of the iceberg for what is possible if Natural Language is exploited.

This article explains the basic concepts behind Natural Language Processing such as Text Processing, Feature Extraction from text etc. along with their codes in Python.

This is a must read article for someone getting started into the field of Natural Language Processing.

Machine Learning has been with us since a long time ago, but it picked up pace about a decade back, part in thanks to the advancements in the hardware and in part to the Algorithms.

This article is about one such Algorithm which is extremely popular in the field of Machine Learning – Gradient Descent. This article explains in detail about how Gradient Descent works, the problems in the original Gradient Descent and the variants of Gradient Descent for overcoming the problem along with the implementation