• Big Data

    SOLR Search – COOK BOOK

    February 27, 2021 - By 

    Solr is the popular, blazing-fast, open-source enterprise search platform built on Apache Lucene™. Here is a example of how Solr might be integrated into an application This blog has a curated list of SOLR packages and resources. It starts with how to install and then show some basic implementation and usage. Installing Solr  Typically in order to install on my Mac, I always use Homebrew first update your brew:  brew update     Updated Homebrew from 37714b5ce to 373a454ac. then install solr: brew install solr  However this time I am going to show step by step installation on mac as explained in…

  • Big Data

    ELASTIC Search – COOKBOOK

    February 27, 2021 - By 

    Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java. Following an open-core business model, parts of the software are licensed under various open-source licenses (mostly the Apache License),[2] while other parts fall under the proprietary (source-available) Elastic License. Official clients are available in Java, .NET (C#), PHP, Python, Apache Groovy, Ruby and many other languages. According to the DB-Engines ranking, Elasticsearch is the most popular enterprise search engine followed by Apache Solr, also based on Lucene. Original author Shay Banon talking about Elasticsearch at Berlin Buzzwords 2010 Initial release 8 February 2010 Written in Java License Various (open-core model), e.g. Apache License 2.0(partially; open source), Elastic License (proprietary; source-available) Website…

  • AI, ML & Data Science - Deep Learning - Machine Learning

    AI – Machine Learning & Deep Learning

    January 9, 2019 - By 

    Getting Started with Machine Learning (ML) Machine learning projects typically follow a series of steps: data collection, data preprocessing, model selection, training, and evaluation. Here’s a breakdown of essential concepts and project ideas to help you get started. 1. Data Collection and Preprocessing Data is the foundation of any ML project. Collecting relevant, high-quality data ensures models have the information needed to identify patterns. Preprocessing steps—such as cleaning, normalization, and handling missing values—prepare raw data for analysis. Project Example: Predicting House Prices Using the famous Boston housing dataset, you can start by cleaning data and then normalizing it to improve…

  • SCALA - AI, ML & Data Science

    Scala Basics

    October 2, 2018 - By 

    Originally posted October 2, 2018 by Kinshuk Dutta Table of Contents What is Scala? Comparison Between Scala and Java Installing Scala on macOS Setting Up Your Development Environment Scala Basics with REPL Data Types, Variables, and Immutability Next Steps in Scala Learning What is Scala? Scala is a general-purpose programming language that blends object-oriented and functional programming, providing powerful support for concurrency and a strong static type system. It’s designed to be concise and expressive, particularly in comparison to Java. Scala’s compatibility with Java makes it a popular choice in Big Data applications, notably with frameworks like Apache Spark. Comparison…

  • Python
    AI, ML & Data Science

    Python Basics

    February 11, 2013 - By 

    Python Basics (Python v3.2.5) This blog is a comprehensive introduction to Python, covering what Python is, how to install and use it, along with practical scenarios, sample projects, and valuable tips. The goal is to give readers a hands-on understanding and prepare them to tackle real-world Python tasks confidently. What is Python? Python is an interpreted, high-level, general-purpose programming language. Known for its readable syntax and versatility, Python is used across various domains, from web development to scientific computing. Created by Guido van Rossum and first released in 1991, Python’s design philosophy emphasizes simplicity and readability, with significant whitespace usage.…