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. Buy ads: https://telega.io/c/epythonlab
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6 213
Combine Date and Time object
πΎ Trick?
πMore Tips and Tricks: https://bit.ly/Pythontoptips
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Learn #python with #epythonlab
6 213
Data Science vs Machine Learning: Understanding the differences with realworld examples
https://youtu.be/bjwJrRVzBUU
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6 213
INTRODUCTION TO PROBABILITY DISTRIBUTION FOR MACHINE LEARNING WITH PYTHON
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1. What is a random variable?
ππΏ https://youtu.be/TkFipAuH-rY
2. Types of a random variable
ππΏ https://youtu.be/jBYsKZOxR6k
3. Calculating probability using probability mass function
ππΏ https://youtu.be/ceSvPxY_uAk
4. Calculating probability over a range
ππΏ https://youtu.be/_WF9X4RyARA
5. Calculating Probability using the cumulative distribution function
ππΏ https://youtu.be/tfoGiPlwiys
6. Calculating probability of continuous variable using density function and cumulative distribution function
ππΏ https://www.youtube.com/watch?v=ikete4WQaj0
6 213
Top 10 highly paid remote tech jobs https://youtu.be/RBPAvQA8wZ8
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6 213
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
πJoin Telegram https://t.me/epythonlab/
Learn #python with #epythonlab
6 213
How to Fix Pandas KeyError: Python KeyError https://youtu.be/AC1DnZeXCu4
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6 213
Understanding Artificial Intelligence, Machine Learning, and Deep Learning
https://youtu.be/qSyDFGUXS9M
Join #epythonlab https://t.me/epythonlab
6 213
INTRODUCTION TO PROBABILITY DISTRIBUTION FOR MACHINE LEARNING
1. What is a random variable?
ππΏ https://youtu.be/TkFipAuH-rY
2. Types of a random variable
ππΏ https://youtu.be/jBYsKZOxR6k
3. Calculating probability using probability mass function
ππΏ https://youtu.be/ceSvPxY_uAk
4. Calculating probability over a range
ππΏ https://youtu.be/_WF9X4RyARA
5. Calculating Probability using the cumulative distribution function
ππΏ https://youtu.be/tfoGiPlwiys
6. Calculating probability of continuous variable using density function and cumulative distribution function
ππΏ https://www.youtube.com/watch?v=ikete4WQaj0
6 213
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
Join #epythonlab https://t.me/epythonlab
6 213
Repost from Epython Lab
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.
6 213
Build your own Deep Learning Model with tensorflow and keras using Google Colab notebook https://www.youtube.com/playlist?list=PL0nX4ZoMtjYEhYVeSJkp2QhW658V0-R4e
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6 213
#09 Scalar Array
Learn more about numpy array here https://youtu.be/G7FjapQvJV8
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6 213
Which is better for Deep Learning https://www.youtube.com/watch?v=ZIN6WmY-EY0
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6 213
Repost from Epython Lab
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
6 213
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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