Here are the open dataset repositories for your AI or Machine Learning Project https://youtu.be/15dD6kNAhx4


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What are magic methods and functions in Python
https://www.youtube.com/watch?v=fjcx0b2ckl4

In this video, you will learn everything you need to know about regular expressions, from beginner to advanced. I will cover the basics of regular expressions, as well as more advanced topics with practical examples. By the end of this video, you will be able to use regular expressions to solve a variety of problems. https://www.youtube.com/watch?v=lR7xQUx5_Og

Unleashing the Power of NumPy: Solving Real-world Problems
https://youtu.be/6Ci7BbksEC8

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This is a simple CRUD application developed using Python, Bootstrap, and Flask as a framework. https://github.com/epythonlab/BlogApp

Watch full tutorial: https://www.youtube.com/playlist?list=PL0nX4ZoMtjYGzAtRxyP0szpmv3Yaub-0o

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It's 01/01/2023

What are the concepts behind list, tuple, and dictionary?

This tutorial will give you an insight about them
https://youtu.be/YYzOGQCBUjo

Which is better for Deep Learning https://www.youtube.com/watch?v=ZIN6WmY-EY0

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#09 Scalar Array

Learn more about numpy array here https://youtu.be/G7FjapQvJV8

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What type of array?

Learn more about numpy array here https://youtu.be/G7FjapQvJV8

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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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A comprehensive guide to matrix multiplication with numpy python library
https://www.youtube.com/watch?v=-hu53V03-1I

Compilers and interpreters are programs that help convert the high level language (Source Code) into machine codes to be understood by the computers. Computer programs are usually written on high level languages. A high level language is one that can be understood by humans.

However, computers cannot understand high level languages as we humans do. They can only understand the programs that are developed in binary systems known as a machine code. To start with, a computer program is usually written in high level language described as a source code. These source codes must be converted into machine language and here comes the role of compilers and interpreters.

Differences between Interpreter and Compiler

!. Interpreter translates just one statement of the program at a time into machine code where as Compiler scans the entire program and translates the whole of it into machine code at once.

2. An interpreter takes very less time to analyze the source code. However, the overall time to execute the process is much slower. A compiler takes a lot of time to analyze the source code. However, the overall time taken to execute the process is much faster.

3. An interpreter does not generate an intermediary code. Hence, an interpreter is highly efficient in terms of its memory. A compiler always generates an intermediary object code. It will need further linking. Hence more memory is needed.

4. Keeps translating the program continuously till the first error is confronted. If any error is spotted, it stops working and hence debugging becomes easy. A compiler generates the error message only after it scans the complete program and hence debugging is relatively harder while working with a compiler.

5. Interpreters are used by programming languages like Ruby and Python for example. Compliers are used by programming languages like C and C++ for example.

Keynote on Tensorflow vs PyTorch
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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INTRODUCTION TO PROBABILITY DISTRIBUTION FOR MACHINE LEARNING
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

Understanding Artificial Intelligence, Machine Learning, and Deep Learning
https://youtu.be/qSyDFGUXS9M

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How to Fix Pandas KeyError: Python KeyError https://youtu.be/AC1DnZeXCu4


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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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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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Pandas vs SQL: Data Manipulation Showdown
https://www.youtube.com/watch?v=MIHFVsu7QNc

Top 10 highly paid remote tech jobs https://youtu.be/RBPAvQA8wZ8

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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