Machine Learning tutorial

Apriori Associative Learning Algorithm

Apriori is a associative learning algorithm which is generally used in data mining. It follows the principle that people who bought this will also buy this.

machine learning

Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is used as a topic modelling technique that is it can classify text in a document to a particular topic. It uses Dirichlet distribution to find topics for each document model

machine learning

Topic Modelling Techniques in NLP

Topic modelling is an algorithm for extracting the topic or topics for a collection of documents. We explored different techniques like LDA, NMF, LSA, PLDA and PAM.

machine learning

Zero shot learning

In zero-shot learning, the machine is capable of describing what class an unlabeled sample belongs to when it does not fall into the category of any of the trained categories.

machine learning

Implement Document Clustering using K Means in Python

In this article, we discuss the implementation of concepts like TF IDF, document similarity and K Means and created a demo of document clustering in Python

machine learning

TextRank for Text Summarization

TextRank is a text summarization technique which is used in Natural Language Processing to generate Document Summaries. It uses an extractive approach and is an unsupervised graph-based text summarization technique based on PageRank.

software engineering

Create a Web App using Flask to present a Machine Learning model

We will see how to deploy a Machine Learning model by building a simple Web Application using Flask. It will run the Machine Learning model in the server as inference.

machine learning

Demystifying Voting Classifier

Voting classifier is one of the most powerful methods of ensemble methods which we have explored in depth in this article.

machine learning

Text classification using CNN

In this article, we are going to do text classification on IMDB data-set using Convolutional Neural Networks(CNN). We will go through the basics of Convolutional Neural Networks and how it can be used with text for classification.

machine learning

Types of Neural Networks

Today, there are over 10 types of Neural Networks and each have a different central idea which makes them unique. We have explored all types in this article

machine learning

Text Generation using LSTM

LSTM are a perfect fit for text generation as text are a sequence of words and can predict the next word. We have explained the basic idea and developed a demo for it

software engineering

Approaches for implementing Autocomplete Feature

The various approaches for Text Auto-completion are Linear Search (Brute Force), Binary Search Approach, TRIE Data Structure, Ternary Search Tree Approach, Fuzzy Search, Burkhard Keller Tree Approach and Machine Learning Approach.

machine learning

K-medoids Clustering

K-medoids Clustering is an Unsupervised Clustering algorithm that cluster objects in unlabelled data. It is an improvement to K Means clustering which is sensitive to outliers.

machine learning

Linear vs Logistic Regression

We have explored the differences between Linear and Logistic regression in depth. We looking into the applications of Linear and Logistic regression along with a basic background.

machine learning

Text classification using K Nearest Neighbors (KNN)

In this article, we will demonstrate how we can use K-Nearest Neighbors (KNN) algorithm for classifying input text into different categories. We used 20 news groups for a demo.

machine learning

PageRank

PageRank is an algorithm to assign weights to nodes on a graph based on the graph structure and is largely used in Google Search Engine being developed by Larry Page

machine learning

Language Identification Techniques

In this article, we will understand the different techniques for language identification which involves two steps namely language modelling and classification

machine learning

Text classification using Naive Bayes classifier

In this article, we have explored how we can classify text into different categories using Naive Bayes classifier. We have used the News20 dataset and developed the demo in Python.

machine learning

Gradient Boosting

Gradient Boosting is a machine learning algorithms used to predict variable (dependent variable). It is used in regression and classification problem.

machine learning

Grid Search

Grid Search is a machine learning tool which is used to tune the hyperparametres of various machine learning algorithms like decision tree and SVM

machine learning

LexRank method for Text Summarization

LexRank method for text summarization is another child method to PageRank method similar to TextRank. It uses a graph based approach for text summarization

machine learning

Applications of Recurrent Neural Networks (RNNs)

In this article, we explored the different applications of RNNs like generating image descriptions, Music composition, Machine translation and more.

machine learning

Graph based approach for Text summarization (Reduction)

In this article we will understand Graph based approach for text summarization (also known as Graph Reduction). It uses techniques to reducing graph size such as predicate-argument mapping and normalization.

machine learning

Image Captioning using Keras (in Python)

Image Captioning is the process of generating a textual description of an image based on the objects and actions in it. We have build a model using Keras library (Python) and trained it to make predictions.

machine learning

Major ideas in Creating Realistic Talking Head Models from Photo

The only issue in creating artificial videos is that they require a large amount of training data, however, we might want it to learn from only a few image views of a person. This is the concern of this paper.