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Showing posts from June, 2020

Bite Sized Learning - Speech Synthesis & Recognition

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Text To Speech & Speech To Text in just minutes.... Speech Synthesis: It is a computer-generated simulation of human speech It converts human language text into human-like speech Speech Recognition It   translates spoked language to text One of the classic real life example is "Apple Siri" which recognises the speech and convert into text Refer to my codes here - Click Here

Machine Learning - Introduction

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Machine Learning - Introduction Video Link:

Google Analytics & Studio

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Blog Analytics - Powered by Google Analytics This article is to demonstrate the reporting and analytics feature of google analytics and google studio platform. In todays digital world where online presence is highly critical to run a successful business. Therefore, it becomes very important to know the details of different people who visit a website, what they do, how long they stay and what all pages do they visit on your website. All this help understand if your website is accomplishing its purpose.  Understanding and studying the user behaviour is critical to the success of a e-commerce website or personal/fun/informative blog. This gets easier with Google Analytics. It provides valuable insights that can help you to shape the success strategy of your business.  Below are some reason why you should use google analytics to run a successful online/digital marketing campaign. Free and automated data collection Reporting feature - pre-defined & cu...

Bite Sized Learning: Printing Patterns

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Bite Sized Learning - Prining Patterns This will be handy when you attend interviews or exams Refer the code file for various patterns using numbers, alphabets and characters - Patterns

How to tell stories with data

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Lets make our dataviz tell the story Want to watch more such videos ----> Subscribe here

Bite Sized Learning: Machine Learning - Classification

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Machine Learning  - Classification problem Winter is Coming back...... A classification problem is when the predicted variable is a category. For example "spam" or "not spam" A classification model attempts to draw some conclusion from observed values Given the inputs or features a classification model will try to predict the value of one or more outcomes When the predicted variable has two category ("spam" or "not spam") then it is a binary classfication problem However when the predicted variable has more than two categories then it is multiclass classification Here we will see a simple binary classification model to predict who will die in the famous series "Game of Thrones" Check out this link for the model - Winter is Coming More details on ML classification  - click here

Bite Sized Learning - Handling Text/Characters

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Text Handling using Regular Expressions Most of you might be handling categorical or text data as well while doing text analysis. Cleaning, extracting meaninful insights from text or characters is critical. Here is a simple python library which helps handle characters easily. A Regular Expression (RegEx) is a sequence of characters that defines a search pattern. You can specify the rules for the set of possible strings that you want to match; this set might contain English sentences, or e-mail addresses or anything you like. Click this link for sample codes - RegEx Some common uses of ReGEx: Finding if email ID is valid Remove special characters from a text Extract numbers from a text Check if entered password matches the password criteria