Boosting Algorithms explained in detail
In the post, Random Forests Explained in detail we discussed Random Forest which uses the technique of Bagging to create an ensemble of the decision tree. In this post, we will be discussing Boosting techniques and will look at a few popular algorithms: Adaptive Boosting or AdaBoost Gradient Boosting which uses boosting techniques to create an ensemble. ... Read More
Distributional Semantics: Techniques to represent words as vectors
The distributional hypothesis states that the context words of the given ambiguous word determine the correct meaning of the word. Which in simple terms means that the meaning of the given word can be determined in the context (or neighboring words) in which the word is used. ... Read More
Multivariate Linear Regression detailed explanation
In the previous post, Simple Linear Regression detailed Explanation we understand how to apply Linear Regression to the problem statement where we have only one independent variable. However, in the real-time scenario, there will be many independent variables that will contribute to predicting the target variable. Here, I will be demonstrating using the Boston dataset from the sklearn library. ... Read More
Importing data using Python
In today's world, there is a lot of data being generated from various devices. The format of data varies from flat files to tabular structure. In this post, we will be looking into python packages, for importing data using python. We will be looking at techniques to import following file types using python packages: Flat files - .txt, .csv files Pickled file Excel files SAS files STATA file HDF5 files mat file Relational database Reading data from web Let's get started with importing data from various file formats. ... Read More
Improving the Supervised Learning Model using Python
Typically in Machine Learning, we generate a Machine Learning model and once the model is generated we use multiple techniques to measure the accuracy of the model. But, the question that might be coming to your mind is: "The accuracy of the model doesn't match the expectations. What can I do to improve the accuracy of the model?" Valid thought. In this post, we will be looking at a few best practices to generate Models. In other words, we will be looking at techniques to tune the model. ... Read More
Hypothesis Testing explained using practical example
An in-depth explanation of hypothesis testing using real-world examples. The best guide to follow for understanding hypothesis testing in detail. The article is inspired by the book "Naked Statistics - Stripping the dread from the data" by Charles Wheelan. ... Read More
Simple Linear Regression detailed Explanation
Linear regression is one of the simple and popular used machine learning algorithms which focus on finding the best straight-line fit between the dependent variable or target variable and one or more independent variables. To learn more about algorithm working refer to the post. ... Read More
Mastering Smart Contract Development: Writing and Deploying Solidity Contracts using Solidity
Learn about how to develop a smart contract using solidity as a programming language on the online remix IDE. At the same time, this post also explains how to test the smart contract on a sample blockchain before deploying the smart contract on the real blockchain. ... Read More
Unlocking the Power of Natural Language Processing: An Introduction and Pipeline Framework for Solving NLP Tasks
Natural Language Processing (NLP) is the ability of a computer program to understand human language as it is spoken. Here in this post we discussed about NLTK and spaCy package which is used to perform NLP operations and it's pipeline. ... Read More
