BEST BOOK FOR OBJECT ORIENTED PYTHON https://matematika-mipa.unsri.ac.id/wp-content/uploads/2022/05/Object-Oriented-Python-Master-OOP-by-Building-Games-and-GUIs-Irv-Kalb-z-lib.org_.pdf

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

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Sync vs Async programming in Python: https://youtu.be/0UG-pEHZfnc

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How to optimize python code performance with cython: https://www.youtube.com/watch?v=jdgriwblNyw

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Full-stack web app development full tutorial with python and flask framework
https://www.youtube.com/watch?v=tbvZcyTwocA

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

Some projects you can practice
https://www.youtube.com/playlist?list=PL0nX4ZoMtjYHV6n2-0taITMZlrd23hKL1

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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

All Muslim Pythonista, Eid Mubarak

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AI vs ML vs DL
Understanding Artificial Intelligence, Machine Learning, and Deep Learning
https://youtu.be/qSyDFGUXS9M

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How to get started with Python programming from scratch - Beginners Guide https://www.youtube.com/watch?v=4IBGze0CYkk

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Top AI Project Ideas for beginners

How to create xml documents using python https://www.youtube.com/watch?v=OtFqjpEEk_s&t=322s

Decorators in Python is used to ->
-> improve code readability
-> reduce code duplication
-> increase flexibility
Here you can learn how to implement decorators in Python step-by-step: https://www.youtube.com/watch?v=xpNt5qfgK38&list=PL0nX4ZoMtjYFwa6WIlGqs8g3EBTYt1k7y&index=8

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Build your own Deep Learning Model with tensorflow and keras using Google Colab notebook https://www.youtube.com/watch?v=anyJVt5XzfE&list=PL0nX4ZoMtjYEhYVeSJkp2QhW658V0-R4e&index=3

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Build your own Deep Learning Model with tensorflow and keras using Google Colab notebook https://www.youtube.com/watch?v=anyJVt5XzfE&list=PL0nX4ZoMtjYEhYVeSJkp2QhW658V0-R4e&index=3

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Project Idea: Building a spam classifier

Introduction

Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'.

In this mission we will be using the Naive Bayes algorithm to create a model that can classify dataset SMS messages as spam or not spam, based on the training we give to the model. It is important to have some level of intuition as to what a spammy text message might look like.
What are spammy messages?

Usually they have words like 'free', 'win', 'winner', 'cash', 'prize', or similar words in them, as these texts are designed to catch your eye and tempt you to open them. Also, spam messages tend to have words written in all capitals and also tend to use a lot of exclamation marks. To the recipient, it is usually pretty straightforward to identify a spam text and our objective here is to train a model to do that for us!

Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam' and nothing else. Also, this is a supervised learning problem, as we know what are trying to predict. We will be feeding a labelled dataset into the model, that it can learn from, to make future predictions. https://youtu.be/XdxaTc02FYA?si=XUFi1gsjRRmasRwj

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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