Introduction

The goal of this post is to briefly explain popular (and unpopular) concepts in Machine Learning, the idea for which stemmed from my travails for finding good quality explanations of various Machine Learning concepts on the web. Unlike similar posts on the web, here you’ll also find links to good quality resources and to related concepts for more holistic understanding. Hopefully, this post would be helpful to the people who are just starting in Machine Learning as well as to the people who need a quick refresher on some concepts.

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Introduction

[Update] Check out refined version of this post on TowardsDataScience. Online shopping is a trend these days as it provides many benefits like convenience, large selection of products, great prices and so on. Trailing on this trend, the online fashion industry has also seen tremendous growth. However, shopping for clothes online is still tricky because it is hard to gauge their fit given the wide size variations across different clothing brands. Thus, automatically providing accurate and personalized fit guidance is critical for improving the online shopping experience and reducing product return rates.

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Introduction

In the previous post, we learned about scenarios where Expectation Maximization (EM) algorithm could be useful and a basic outline of the algorithm for inferring the model parameters. If you haven’t already, I would encourage you to read that first so that you have the necessary context. In this post, we would dive deeper into understanding the algorithm. First, we would try to understand how EM algorithm optimizes the log-likelihood at every step. Although, a bit mathematical, this would in-turn help us in understanding how we can use various approximation methods for inference when the E-step (calculating posterior of hidden variables given observed variables and parameters) is not tractable. Disclaimer: This post is a bit Mathematical.

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Introduction

[Update: Refined version of this post is available at Towards Data Science]. To solve any data science problem, first we obtain a dataset, do exploration on it and then, guided by the findings, we try to come up with a model to tackle the problem. Once all of that is done, our next task is to find a way to estimate the parameters of the model based on the dataset we have, so that we can make predictions on unseen data. In this post, we will learn about how we can learn the parameters of the model using Maximum Likelihood approach which has a very simple premise: find parameters that maximize the likelihood of the observed data. Through that, I would motivate the Expectation-Maximization (EM) algorithm which is considered to be an important tool in statistical analysis. This post would assume familiarity with Logistic Regression.

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Introduction

In this post, you will learn about the basics of Support Vector Machines (SVM), which is a well-regarded supervised machine learning algorithm. This technique needs to be in everyone’s tool-bag especially people who aspire to be a data scientist one day. Since there’s a lot to learn about, I’ll introduce SVM to you across two posts so that you can have a coffee break in between :)

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Introduction

These days, one of the common features of a good keyboard application is the prediction of upcoming words. These predictions get better and better as you use the application, thus saving users’ effort. Another application for text prediction is in Search Engines. Predictive search saves effort and guides visitors to results, rather than having them type searches that are slightly off and don’t return a large number of results. As a consumer of these applications, I am sure you would have wondered “How exactly does this prediction works?” at least once. Well, wonder no more because, in this article, I will give you some insight into what goes behind the scenes of producing predictions. So, let’s get started.

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