Epython Lab – Telegram
Epython Lab
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Welcome to Epython Lab, where you can get resources to learn, one-on-one trainings on machine learning, business analytics, and Python, and solutions for business problems.

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Develop Quantum Application in Python and qiskit framework full tutorial https://youtu.be/b728XehreTQ

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This is a Python for data science, machine learning or Artificial Intelligence tutorial for beginners. In this tutorial you will have a solid understanding of the following basic Python topics:
Chapters:
0:00 Introduction to Python programming - Python basics
35:33 Data object types  and Type conversion
48:17 Operators and Expressions
1:14:15 Exception Handling
1:34:36 String Methods for Manipulating String Data
2:13:25 Functions
2:28:38 Function Scope
2:38:38 Function Arguments
2:47:54 Conditional Statements and Loops
3:14:41 Essential Built-in Modules
3:26:26 Develop a Simple Game Program

https://youtu.be/ISv6XIl1hn0

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Choose your favorite chapter and learn Python basics - Python Basic Full tutorial https://youtu.be/ISv6XIl1hn0


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Learn the Mojo programming language fundamentals
https://youtu.be/pyfCTxKcDPY


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🍾 Python Join Trick?
👉More Tips and Tricks: https://bit.ly/Pythontoptips

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Learn #python with #epythonlab
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Introduction to Python Programming Language - Complete Guide: https://youtu.be/ISv6XIl1hn0


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In this coding interview videos, I have solved many interview questions and you will practice your own and improve your coding skills: https://youtu.be/XlgANCFTuxA

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Filtering Rows and Columns in  Pandas DataFrame
https://youtu.be/8a3Y-HT09sQ

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Forwarded from Epython Lab
Here, I have build and published my first demo library on PyPI.
you can test it https://pypi.org/project/coolmath1/

Here is a step by step tutorial on how to build and publish your own python library https://youtu.be/ZQlDrNvQn6Y
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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
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