Machine Learning Blogs

## Why is MySQL so Popular?

Any web development project is incomplete without database. A database is a collection of information that is organized so that it can be easily accessed, managed and updated. Today, there are N number of databases available in the market. Out of which, MySQL is a most popular open source database software backed by Oracle. MySQL is notContinue reading “Why is MySQL so Popular?”

## Edge Analytics! What is it?

According to a recent report by Transparency Market Research (TMR), the edge analytics market is foreseen to project a strong growth with a noticeable CAGR (Compound Annual Growth Rate) of 27.6% within the forecast period. What is Edge Analytics? Edge analytics is the advanced data analysis method that enables users to get access to real-time processingContinue reading “Edge Analytics! What is it?”

## Descriptive and Predictive Analytics

Data analytics is the method of exploring raw data sets in order to find trends and draw conclusions about the information they contain. Across the globe companies are considering various analytic solutions to discover what will allow them to get the most out of their information. Let’s see the two main types of Data Analytics methods; DescriptiveContinue reading “Descriptive and Predictive Analytics”

## Moneyball : The Story Every Data Enthusiast Should Know

Moneyball is the story of general manager Billy Beane of Oakland A’s Baseball team. The story of attempting to create a baseball team on a low budget by employing data analysis to acquire new players. Moneyball Data Science Plot In 2001, Billy Beane’s Oakland A’s team lose to the Yankees( Baseball team) in the playoffsContinue reading “Moneyball : The Story Every Data Enthusiast Should Know”

## Machine Learning In Medicine

The world of medicine is changing due to the implementation of Artificial Intelligence. Fast improvement in computer science and availability of huge amount of data in the field of medicine makes machine learning system to tackle increasingly complex learning tasks, often with unbelievable success. The increasing focus of AI in medicine has led to someContinue reading “Machine Learning In Medicine”

## Regularization in Neural Networks

Regularization in Neural Networks In Deep Learning it is necessary to reduce the complexity of model in order to avoid the problem of overfitting. Also, the model should be able to generalize well. This problem can be solve by using regularization techniques. Technically, overfitting harms the generalization. So, our primary goal is to solve theContinue reading “Regularization in Neural Networks”

## Introduction to K-means Clustering

K-means Clustering is a unsupervised machine learning technique that solve the well known clustering problem, i.e. portioning ‘n’ number of objects into ‘k’ clusters in which each object belongs to the cluster with the nearest mean. Let’s discuss this in detail. What is Unsupervised Machine Learning? This is a term used in Machine learning which people comeContinue reading “Introduction to K-means Clustering”

## YOLO : Real Time Object Detection

We sees an image and instantly know what objects are in the image and where they are unlike machines. The human visual system is fast and accurate. Every AI researcher is struggling to find an efficient method for real time object detection. In 2015 researchers from Allen institute for AI, University of Washington, and FacebookContinue reading “YOLO : Real Time Object Detection”

## A brief Introduction to Probability Distribution for Machine Learning

Probability Distributions are prevalent in many fields, namely, computer science, stock market, astronomy and economics. In this blog we are going to see different probability distribution for Machine Learning and their properties. Also, we will discuss key statistical points for the simple models. Note, all the discussion in this blog is based on the assumptionContinue reading “A brief Introduction to Probability Distribution for Machine Learning”

## Build your own Movie Recommendation Engine using Word Embedding

You may have noticed that services like Netflix, Amazon Prime, YouTube recommends the same kind of video you have watched earlier. So, how does this happen, it is because of Machine Learning based recommendation engine which determines how similar the videos are to other things you like and based on that assumption it serves upContinue reading “Build your own Movie Recommendation Engine using Word Embedding”

## Natural Language Processing with N-Gram Model

Till now we have seen two natural language processing models, Bag of Words and TF-IDF. What we are going to discuss now is totally different from both of them. Also, the applications of N-Gram model are different from that of these previously discussed models. To understand N-gram, it is necessary to know the concept ofContinue reading “Natural Language Processing with N-Gram Model”

## Term Frequency-Inverse Document Frequency (Tf-idf)

Term Frequency-Inverse Document Frequency is a natural language processing model use for converting textual data into numerical form. Principle is, as number of times a word increases in given data, the Tf-idf value increases correspondingly. Or, importance of a word is proportional to the number of times it appears in the data but is offsetContinue reading “Term Frequency-Inverse Document Frequency (Tf-idf)”

## What is Bag of Words and How to Code it in Python

We all know that Machines can only read numbers and mathematical calculations and cannot read textual data like us. Bag of word helps to convert textual data into mathematical form that can be applied to any machine learning algorithm. Creating Bag of Words Let’s make the bag-of-words model concrete with the following example. paragraph =Continue reading “What is Bag of Words and How to Code it in Python”

## Brief Introduction to Viola-Jones Algorithm

Real time object detection Viola-Jones algorithm is proposed by two scientist Paul Viola and Micheal Jones in 2001. It is mainly use for face detection, goal is to detect face from non face. The merits of this algorithm is its speed and its accuracy. Steps 1. Selection of Haar-like features If we look at theContinue reading “Brief Introduction to Viola-Jones Algorithm”

## What is Transfer Learning in Deep Learning?

Convolutional Neural Network process an image and come up with detecting low-level features in images like edges, corners, etc. These layers are successful in capturing the spatial and temporal dependencies in an image. This is done with the help of filters. But to train the model using simple CNN, we required large amount of data.Continue reading “What is Transfer Learning in Deep Learning?”

## Implementation of Convolutional Neural Network

Convolutional Neural Network (CNN) is a class of Deep Learning, mainly use for Computer Vision. It is similar to artificial neural network, only difference is it uses convolutional mathematical linear operation instead of simple matrix multiplication in at-least one of its layer. Building a Convolutional Neural Network is nothing but building a human eye, howContinue reading “Implementation of Convolutional Neural Network”

## Introduction to Artificial Neural Network

Inspired from brain, Artificial Neural Network is a tool use by Deep Learning. We can say Artificial Neural Network is recreation of brain into machine. But the question is how can we recreate that in a machine? The Neuron Neuron is the basic building block of artificial neural network, neuron has quite an interesting structure.Continue reading “Introduction to Artificial Neural Network”

## Evaluation Metric in Machine Learning

It is very important to check efficiency of the Machine Learning model. To check this, you have to evaluate the model based on some metric. But more important is a choice of metric to evaluate your machine learning model. Confusion Metric Confusion Metric is use to check the accuracy or correctness of your model. ItContinue reading “Evaluation Metric in Machine Learning”

## Decision Tree ; Explained

Decision tree is a non-parametric supervised machine learning algorithm, which can used for categorical as well as regression problem. It uses a tree structure to represent a number of possible decision paths and an outcome for each path. At each node, a question is asked and based on the answer of the question the pathContinue reading “Decision Tree ; Explained”

## A Complete guide to Linear Regression

Linear Regression is a supervised type of Machine Learning algorithm. It helps for predicting the continuous value. What is Regression ? When your output is not a categorical (like yes/no or 0/1) neither is cluster (like c0, c1, etc.), but instead is a continuous value (like temperature, height, etc.) is a Regression. Linear Regression algorithm helpsContinue reading “A Complete guide to Linear Regression”

## Logistic Regression in Brief

Logistic Regression is a Supervised Machine Learning Algorithm used for the classification problem. Now, you might wonder how regression can be used for classification problem. It works on the principle of Simple Linear Regression. Logistic Regression is taken from the field of statistics, where it is used to categorize the classes into true or false,Continue reading “Logistic Regression in Brief”

## Machine Learning Blogs Word Cloud

analytics Artificial Intelligence Bernoulli Binomial Cancer Computer Vision Data Enthusiast Deep Learning Distribution Gaussian Image Processing K – means clustering Machine Learning Marketing Medicine Moneyball Neural Network Poisson Probability Regularization Stat Statistics Unsupervised Machine Learning

### Kaggle Data Analysis

© Copyright 2020 Capable Machine

Created by Sarang Deshmukh