The prospect of incorporating Computer Vision with Machine Learning gives me chills! It’s really fascinating how we can build and train models to make machines identify between images, such as a picture of a dog or a cat, with phenomenal precision. The potential is endless. In this article I am going to explain how you can build an image classifier yourself with the help of TensorFlow for Poets, created by Google, to recognize just about anything in the world!
Before we dive into the “How”, let’s turn our attention to the “Why”.
We are living in a data driven world. What makes companies valuable is the volume, uniqueness and quality of data they have accumulated through years of services. The insights squeezed from data gives companies leverage over their competitors. At the same time, more people than ever in the history of the world now have the luxury to be online and be a consumer of a plethora of online services. Evidently so, the volume of data has grown exponentially, and it will only continue to grow indefinitely.
Companies nowadays are in constant need of more qualified people who can work with these massive collection of data properly and help solve real problems for the companies and help them continue to improve their products and services. …
Suppose you just sat for your GRE exam (which is a standard test accepted by graduate and business schools worldwide) and you get a score of 310 out of a total 340. You feel pretty confident but you would like to find out your chances or probability of getting into a tier A university with that score.
One of the ways to accurately describe Machine Learning is it’s the domain of figuring out mathematical optimization for real-world problems. Yes, it’s all maths down the road! You pick a problem that you deeply care about solving, you find suitable data that you think will help you get insights on that problem, you pick the most favorable model for the situation and then hop on to the later stages. …
Machine Learning is the hottest thing of this decade. Everybody wants to get on the bandwagon and start deploying machine learning models in their businesses. At the heart of this intricate process is data. Your machine learning tools are as good as the quality of your data. Sophisticated algorithms will not make up for poor data. Just like how precious stones found while digging go through several steps of cleaning process, data needs to also go through a few before it is ready for further use.
In this article I will try to simplify the exercise of data preprocessing, or in other words, the rituals programmers usually follow before it is ready to be used for machine learning models into 6 simple steps. …
Well, a sentiment analysis is formally defined as
a process of computationally identifying and categorizing emotions and opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral.
Some of the real-world applications of sentiment analysis are Social Media monitoring (how are people reacting to certain things on social media), Marketing (if people are liking your product or not by analyzing product reviews), Political analysis (which politician is more popular among the majority of people), among many others.
Twitter is a great source of collecting and analyzing thousands of diverse opinions and emotions expressed by real people all over the world on diverse range of topics every single second of every single day. Besides being a great repository for gathering data published by real people, a tweet is ideal for sentiment analysis for two other reasons —
i) tweets are easy to collect and categorize
ii) tweets are smaller in length(140 characters), so will exhaust the memory relatively less. …
Prologue: If you have a basic understanding of programming and are eager to get into the domain of Artificial Intelligence, Machine Learning and Data Science, among many other, but is baffled by where to start, then this article is absolutely for you.
Why use Python?
“ Python is a widely used high-level, general-purpose, interpreted, dynamic programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java. …