Adding Text in Turtle Canvas

In this video we’ll be talking about writing text on turtle screen. We’ll learn

• How to write text on canvas
• How to change font color
• How to change fontsize
• How to change font alignment
• How to change font type
• How to underline text

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Hands-on Guide to Pattern - A Python Tool for Effective Text Processing and Data Mining

Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc. which are useful in creating text processing based applications.

Python provides different open-source libraries or modules which are built on top of NLTK and helps in text processing using NLP functions. Different libraries have different functionalities that are used on data to gain meaningful results. One such Library is Pattern.

Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Other than text processing Pattern is used for Data Mining i.e we can extract data from various sources such as Twitter, Google, etc. using the data mining functions provided by Pattern.

In this article, we will try and cover the following points:

• NLP Functionalities of Pattern
• Data Mining Using Pattern

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#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm

Laravel 7 Full Text Search MySQL

Here, I will show you how to create full text search in laravel app. You just follow the below easy steps and create full text search with mysql db in laravel.

Laravel 7 Full Text Search Mysql

Let’s start laravel full-text search implementation in laravel 7, 6 versions:

1. Step 1: Install Laravel New App
2. Step 2: Configuration DB .evn file
3. Step 3: Run Migration
4. Step 4: Install Full Text Search Package
5. Step 5: Add Fake Records in DB
6. Step 6: Add Routes,
7. Step 7: Create Controller
8. Step 8: Create Blade View
9. Step 9: Start Development Server

https://www.tutsmake.com/laravel-full-text-search-tutorial/

#laravel full text search mysql #laravel full text search query #mysql full text search in laravel #full text search in laravel 6 #full text search in laravel 7 #using full text search in laravel

Flutter - How to Add AdMob Real Ads in Flutter App | Flutter AdMob Tutorial

Hello Whats is up Everyone So, Today I am going to show u How to Add Admob Real ads in Flutter apps which are very Easy Implement After watching this video u r going to understand Each & everything

Firebase is one of the best Database storage for Flutter so Firebase is giving us Firebase AdMob for implementing Banner Ads, Interstitial Ads & Rewards Ads.

Github Profile : https://sagarshende23.github.io/

Github Code Link:- https://github.com/sagarshende23/flutter_admob

Part 1 Video
Flutter - How to Add ads to Flutter App:
https://youtu.be/2sLAcHDfbcQ

Check out our Website for more Flutter Tutorials
https://alltechsavvy.com

Flutter - How to Add AdMob Real Ads in Flutter App | Flutter AdMob Tutorial

Code Editor : Visual Studio Code
Device : Vivo V5

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#flutteradmob #flutterads #adsinflutter #flutter

💼Contact: sagarshende631@gmail.com

🎉Don’t forget to take the quizzes 🤓 i.e if you’re Qualified Then you can Apply for top tech companies
https://triplebyte.com/iv/QAlkFsw/cp/header

🎉Don’t forget to take the quizzes 🤓 i.e if you’re Qualified Then you can Apply for top tech companies
https://triplebyte.com/iv/QAlkFsw/cp/header

#flutter ads #ads in flutter #flutter admob ads #how to add ads in flutter app #real ads in flutter

Steps for Preparing Ads for your PPC Campaign using Google Adwords

Read the blog to get aware about the Steps required for Preparing Ads for your PPC Campaign using Google Adwords that will help to make you more money.

#learn google ads #steps to create google ads #google ads strategy #how to create google ads #tips for creating google ads

Text Analysis Within a Full-Text Search Engine

Full-Text Search refers to techniques for searching text content within a document or a collection of documents that hold textual content. A Full-Text search engine examines all the textual content within documents as it tries to match a single search term or several terms, text analysis being a pivotal component.

You’ve probably heard of the most well-known Full-Text Search engine: Lucene with Elasticsearch built on top of it. Couchbase’s Full-Text Search (FTS) Engine is powered by Bleve, and this article will showcase the various ways to analyze text within this engine.

Bleve is an open-sourced text indexing and search library implemented in Go, developed in-house at Couchbase.

Couchbase’s FTS engine supports indexes that subscribe to data residing within a Couchbase Server and indexes data that it ingests from the server. It’s a distributed system – meaning it can partition data across multiple nodes in a cluster and searches involve scattering the request and gathering responses from across all nodes within the cluster before responding to the application.

The FTS engine distributes documents ingested for an index across a configurable number of partitions and these partitions could reside across multiple nodes within a cluster. Each partition follows the same set of rules that the FTS index is configured with – to analyze and index text into the full-text search database.

The text analysis component of a Full-Text search engine is responsible for breaking down the raw text into a list of words – which we’ll refer to as tokens. These tokens are more suitable for indexing in the database and searching.

Couchbase’s FTS Engine handles text indexing for JSON documents. It builds an index for the content that is analyzed and stores into the database – the index along with all the relevant metadata needed to link the tokens generated to the original documents within which they reside.

An Inverted index is the data structure chosen to index the tokens generated from text, to make search queries faster. This index links every token generated to documents that contain the token.

For example, take the following documents …

The inverted index for the tokens generated from the 2 documents above would resemble this…

Here’s a diagram highlighting the components of the full-text search engine …

A Text Analyzer

The components of a text analyzer can broadly be classified into 2 categories:

• Tokenizer
• Filters

Couchbase’s engine further categorizes filters into:

• Character filters
• Token filters

Before we dive into the function of each of these components, here’s an overview of a text analyzer …

Tokenizer

A tokenizer is the first component to which the documents are subjected to. As the name suggests, it breaks the raw text into a list of tokens. This conversion will depend on a rule-set defined for the tokenizer.

Stock tokenizers…

Take this sample text for an example: “_this is my email ID: _abhi123@cb.com”

A couple of configurable tokenizers…

• Exception … This tokenizer allows the user to enter exception patterns (regular expressions) over the stock tokenizers.
• Regexp … This tokenizer extracts text that matches the pattern (a regular expression) as tokens.

For example:

#json #couchbase #search #go #text analysis #full-text search #bleve #full-text #full-text-indexing