I'm curious🤭 about statistics Vs Probability
Here, I have made some tutorials about probability distribution for Machine learning using Scipy Python library https://www.youtube.com/playlist?list=PL0nX4ZoMtjYEl_1ONxAZHu65DPCQcsHmI

Mojo Programming Language - Full Course for Beginners
https://youtu.be/pyfCTxKcDPY

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By transforming our DataFrame into XML, we ensure that our data remains structured and readable across different platforms and applications.

Step by step guide: https://youtu.be/wNKKrt885xY

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In this topic modeling project-based tutorial, I have gone through the following steps:

1. Loads the documents(Generating sample documents)
2. Preprocesses the text by removing stop words and stemming words.
3. Creates a TF-IDF vector representation of the documents.
4. Performs LDA topic modeling with the specified number of topics.
5. Extracts the document-topic weight matrix.
6. Prepares the data for CSV format, including document IDs and topic weights.
7. Saves the results to the specified CSV file. https://youtu.be/uJCB2hRCB60

As a programmer, you must know the concept of object oriented programming. Here you go https://www.youtube.com/watch?v=I7z6i1QTdsw

Data Structures with Projects full tutorial for beginners
https://www.youtube.com/watch?v=lbdKQI8Jsok

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Machine Learning Project

Bag of words: A technique used to extract features from the text. It counts how many times a word appears in a document (corpus), and then transforms that information into a dataset.
https://youtu.be/tn-Tvi8CHmg

INTRODUCTION TO PROBABILITY DISTRIBUTION FOR MACHINE LEARNING WITH PYTHON

1. What is a random variable?
👉🏿 https://youtu.be/TkFipAuH-rY

2. Types of a random variable
👉🏿 https://youtu.be/jBYsKZOxR6k

3. Calculating probability using probability mass function
👉🏿 https://youtu.be/ceSvPxY_uAk

4. Calculating probability over a range
👉🏿 https://youtu.be/_WF9X4RyARA

5. Calculating Probability using the cumulative distribution function
👉🏿 https://youtu.be/tfoGiPlwiys

6. Calculating probability of continuous variable using density function and cumulative distribution function
   👉🏿 https://www.youtube.com/watch?v=ikete4WQaj0

Mathematics for Machine Learning RoadMap

🔗 Link to Linear Regression https://bit.ly/46rqiBu
🔗 Link to Linear Algebra https://bit.ly/45EpfwB
🔗 Link to Probability Distribution https://bit.ly/495L8b5
🔗 Link to Telegram Group https://bit.ly/3IR1lnm

What's new in Python 3.13
https://www.youtube.com/watch?v=AbmmY0VMc3k

Top Python Modules
Tips of Python Programming: https://www.youtube.com/playlist?list=PL0nX4ZoMtjYFd1_d6ycD5Uc-AV4dyQa53

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Python Programming for beginners Roadmap

Basic Python Programming: https://youtu.be/ISv6XIl1hn0

Data Structures with Projects full tutorial for beginners
https://www.youtube.com/watch?v=lbdKQI8Jsok

OOP in Python - beginners Crash Course https://www.youtube.com/watch?v=I7z6i1QTdsw

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Join https://www.tg-me.com/Epython Lab/com.epythonlab for more learning resources

Python Async/Await: Master Asynchronous Programming https://www.youtube.com/watch?v=AyCZ9hC59dI

Learn about how to scrape data, identify and extract internal and external links, detect backlinks from websites through web scraping using Python and get help to obtain data, identify internal linking opportunities, and also help to improve SEO.

1. Scraping price information from ebay website with beautiful soup: https://youtu.be/hsRTxmQRClE

2. Detecting and scraping backlinks from any website: https://youtu.be/2iUUxn4GGhs

3. Scrape internal and external links from any website:
https://youtu.be/h9mSBZgkcCU

4. Scraping table data from webpages https://lnkd.in/dJzB36HQ


#python #webscraping #seo #epythonlab

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Thanks for watching!

🤔 Think of yourself as a Data Scientist and given data to you to clean it. The data might contains unnecessary characters(i.e #*()/?@&$%\;[]{}) and you're required to remove those special characters from your data.

Therefore, in this tutorial, you will be learning about how to remove special characters or punctuations from any data using three different methods.

👉https://youtu.be/CVSMl3RKERk

🙏 Don't forget to subscribe, like, and share

Full-stack web app development Roadmap

Here is a full step by step tutorial on building blog app
https://www.youtube.com/watch?v=tbvZcyTwocA

Here is a GitHub link https://github.com/Epython Lab/com.epythonlab/BlogApp

Join https://www.tg-me.com/Epython Lab/com.epythonlab

Managing class attributes with property decorator is clear and concise than using traditional approach with getter and setter methods. Here is a step by step guide https://www.youtube.com/watch?v=Kj5xFNeBgR8

Join https://www.tg-me.com/Epython Lab/com.epythonlab

In this topic modeling project-based tutorial, I have gone through the following steps:

1. Loads the documents(Generating sample documents)
2. Preprocesses the text by removing stop words and stemming words.
3. Creates a TF-IDF vector representation of the documents.
4. Performs LDA topic modeling with the specified number of topics.
5. Extracts the document-topic weight matrix.
6. Prepares the data for CSV format, including document IDs and topic weights.
7. Saves the results to the specified CSV file. https://youtu.be/uJCB2hRCB60