Machine Learning

MachineX: Unfolding Mystery Behind NAIVE BAYES CLASSIFIER

Reading Time: 4 minutes In machine learning, Naive Bayes classifiers are a family of simple “probabilistic classifiers “based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. The Naive Bayes Classifier technique is based on the so-called Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods.   In simple terms, a Naive Bayes classifier assumes that Continue Reading

MachineX: Layman guide to Association Rule Learning

Reading Time: 6 minutes Association rule learning is one of the most common techniques in data mining and as well as machine learning. The most common use, which I’m sure you all must be aware of, is the recommendation systems used by various e-shops like Amazon, Flipkart, etc. Association rule learning is a technique to uncover the relationship between various items, elements, or more generally, various variables in a Continue Reading

MachineX: The second dimensionality reduction method

Reading Time: 5 minutes In the previous blog we have gone through how more data or to be precise more dimensions in the data creates different problems like overfitting in classification and regression algorithms. This is known as “curse of dimensionality”. Then we have gone through the solutions to the problem i.e. dimensionality reduction. We were mainly focused on one of the dimensionality reduction method called feature selection. In this Continue Reading

MachineX: When data is a curse to learning

Reading Time: 4 minutes Data and learning are like best friends, perhaps learning is too dependent on data to be called as friends. When data overwhelms, learning acts pricey, so it feels more like a girlfriend-boyfriend sort of a relationship. Well don’t get confused or bothered on how I am comparing the data and learning, it is just my depiction of something called Dimensionality reduction in machine learning. On Continue Reading

MachineX: Simplifying Logistic Regression

Reading Time: 3 minutes Logistic regression is one of the most popular machine learning algorithms for binary classification. This is because it is a simple algorithm that performs very well on a wide range of problems. It is used when you know that the data is linearly separable/classifiable and the outcome is Binary or Dichotomous but it can be extended when the dependent has more than 2 categories. It Continue Reading

Cool Breeze of Scala for Easy Computation: Introduction to Breeze Library

Reading Time: 4 minutes Mathematics is a core part of machine learning and to dive deep into machine learning one should possess basic knowledge of mathematics concepts but when you start developing algorithms, mathematics can be a real pain. Thankfully we have some awesome libraries that reduce some of our pain and also allows us to focus more on our basic requirement rather than focussing more on manipulation techniques.While Continue Reading

Artificial Intelligence vs Machine Learning vs Deep Learning

Reading Time: 3 minutes The world as we know it is moving towards machines big time. But we can not fully utilize the working of any machine without a lot of human interaction. So in order to do that, we needed some kind of intelligence for the machines. Here comes the place for Artificial Intelligence. It is the concept of machines being smart to carry out numerous tasks without Continue Reading

Getting started with TensorFlow: A Brief Introduction

Reading Time: 3 minutes TensorFlow is an open source software library, provided by Google, mainly for deep learning, machine learning and numerical computation using data flow graphs. Looking at their website, the first definition they have written for TensorFlow goes something like this – TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges Continue Reading

Machine Learning with Random Forests

Reading Time: 5 minutes Machines, working on our commands, for each step, they need guidance where to go what to do. This pattern is like a child who doesn’t understand the surrounding facts to make decisions for a situation. The grown ups usually do it for children. Same goes for machines. The developer writes the commands for the machine to be executed. But here in Machine Learning, we talk about Continue Reading

Concept Learning: The stepping stone towards Machine Learning with Find-S

Reading Time: 7 minutes From our previous blog, we came across what awesome stuff a machine can do with machine learning and what all math stuff is required before you take a deep dive into machine learning. Now we all know the prerequisites for machine learning, so let’s start the journey towards machine learning with small but effective steps towards awesomeness. Most of us always wonder how machines can Continue Reading

First step Towards Machine Learning

Reading Time: 3 minutes The human pace is getting lazy day by day and wants an ease in their life. For that, we created machines, machines that are capable enough to take commands and perform tasks for us. But what if machines can think and take decisions on their own……Sounds rubbish!!! In this modern era of machines and technology, this thing is actually taking place. Big companies like Google Continue Reading

Introduction to Machine Learning

Reading Time: 2 minutes Before jumping directly into what is Machine Learning lets starts with the meaning of individual words i.e. What is Machine and What is Learning. A machine is a tool containing one or more parts that transform energy. Machines are usually powered by chemical, thermal, or electrical means, and are often motorized. Learning is the ability to improve behaviour based on Experience. What is Machine Learning? Continue Reading

SMILE because Regression is easy with Scala

Reading Time: 4 minutes When we think about Regression in Machine Learning, what usually comes in mind are these two techniques: Linear and Logistic regressions. These forms of Regression are considered most used and that’s why they became the most popular. But the truth is, there are many other Regression techniques and all have their significant use in Machine Learning depending on the situation. So in this blog we Continue Reading