Tech Scouter

Introduction Machine Learning has become the hottest topic in Data Industry with increasing demand for professionals who can work in this domain. There is large amount of textual data present in internet and giant servers around the world. Just for some facts 1,209,600 new data producing social media users each day. 656 million tweets per day! More than 4 million hours of content uploaded to Youtube every day, with users watching 5.97 billion hours of Youtube videos each day. 67,305,600 Instagram posts uploaded each day There are over 2 billion monthly active Facebook users, compared to 1.44 billion at the start of 2015 and 1.65 at the start of 2016. Facebook has 1.32 billion daily active users on average as of June 2017 4.3 BILLION Facebook messages posted daily! 5.75 BILLION Facebook likes every day. 22 billion texts sent every day. 5.2 BILLION daily Google Searches in 2017. Need for Vectorization The amount of textual data is massive, and the problem with textual dat

This one took long due to the Analysis work I was doing for this post.There is a lot of work going on in the subject of Sentiment analysis so I decided to compare the accuracy of the products. Let's start with some basics... NLP: Natural Language Processing Natural Language Processing is a very interesting topic and a subject of debate when it comes to accuracy of the NLP. Natural Language is very ambiguous as same sentences can have different meanings like "I saw a man on a hill with a telescope. " It seems like a simple statement until you begin to unpack the many alternate meanings: There’s a man on a hill, and I’m watching him with my telescope. There’s a man on a hill, who I’m seeing, and he has a telescope. There’s a man, and he’s on a hill that also has a telescope on it. I’m on a hill, and I saw a man using a telescope. There’s a man on a hill, and I’m seeing him with a telescope. Sarcasm is that component of the language that is diffi

The article will be divided into different sections as follows: Introduction to Machine Learning Types of Solutions Classification using Naive Bayes A brief about Machine Learning According to the definition by Wikipedia,  Machine learning  is the subfield of  computer science  that, according to  Arthur Samuel  in 1959, gives "computers the ability to learn without being explicitly programmed."  Machine Learning defines a set of problems that have to be evolved through the data by implying some algorithm. One factor that has to be kept in mind while defining a solution through ML is accuracy. Accuracy is very critical in case you are developing a solution in medical domain(cancer detection).There should be a threshold set for every solution which can be based on risk %age that is acceptable. A useful cheatsheet from Microsoft's site to sum up the use of different ML algorithms for the different type of problems. Types of solution Machine Lear