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Discover powerful insights with Python, Machine Learning, Coding, and Rโ€”your essential toolkit for data-driven solutions, smart alg

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๐Ÿ’  All free Kaggle courses for data science
๐Ÿ“ Along with the course completion certificate

โœ… Python โฌ…๏ธ link

โœ… An introduction to machine learning โฌ…๏ธ link

โœ… Pandas โฌ…๏ธ link

โœ… Medium machine learning โฌ…๏ธ link

โœ… Data visualization โฌ…๏ธ link

โœ… Feature engineering โฌ…๏ธ link

โœ… An introduction to the SQL language โฌ…๏ธ link

โœ… Advanced SQL language โฌ…๏ธ link

โœ… An introduction to deep learning โฌ…๏ธ link

โœ… Computer vision โฌ…๏ธ link

โœ… Time series โฌ…๏ธ link

โœ… Data cleanup โฌ…๏ธ link

โœ… Geographical analysis โฌ…๏ธ link

โœ… Explainability of machine learning โฌ…๏ธ link

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

http://t.me/codeprogrammer โญ๏ธ
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Python List Methods clearly Explained

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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๐Ÿฑ 7 of the best GitHub repos
โœ… To enter the world of data analysis and data science


1โƒฃ 100 Days of ML Code repo

โœ๏ธ A hundred-day program for learning and practicing machine learning coding.


๐Ÿ”ข Awesome Data Science repo

โœ๏ธ A curated list of great data science resources such as books, software, and tools.


๐Ÿ”ข Data Science for Beginners repo

โœ๏ธ A repository from Microsoft that has a 10-week course with 20 lessons for beginners. Each lesson includes videos, quizzes, challenges and more.


๐Ÿ”ข Data Science Interviews Repo

โœ๏ธ A repository of questions and answers for science job interviews.


๐Ÿ”ข ML Technical Interviews repo

โœ๏ธ A good guide for machine learning and artificial intelligence technical interviews.


๐Ÿ”ข ML Interviews repo

โœ๏ธ A repository containing machine learning interview questions from basic topics to complex topics such as neural networks and reinforcement learning.


๐Ÿ”ข Data Science Python Notebooks repo

โœ๏ธ A collection of notebooks in various fields of data science such as deep learning, machine learning, data analysis and Python topics.

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

http://t.me/codeprogrammer โญ๏ธ
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List of Running Processes using Python

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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Pandas is getting outdated.

5 reasons you should move to FireDucks ๐Ÿ‘‡

1. Requires changing ONLY ONE line of code:
โ†ณ Replace "๐—ถ๐—บ๐—ฝ๐—ผ๐—ฟ๐˜ ๐—ฝ๐—ฎ๐—ป๐—ฑ๐—ฎ๐˜€ ๐—ฎ๐˜€ ๐—ฝ๐—ฑ" with "๐—ถ๐—บ๐—ฝ๐—ผ๐—ฟ๐—ฒ ๐—ณ๐—ถ๐—ฟ๐—ฒ๐—ฑ๐˜‚๐—ฐ๐—ธ๐˜€.๐—ฝ๐—ฎ๐—ป๐—ฑ๐—ฎ๐˜€ ๐—ฎ๐˜€ ๐—ฝ๐—ฑ"
โ†ณ The rest of the entire code remains the same.
โ†ณ So, if you know Pandas, you already know how to use FireDucks.
โ†ณ Done!

2. Ridiculously faster as per official benchmarks:
โ†ณ Modin had an average speed-up of 0.9x over Pandas.
โ†ณ Polars had an average speed-up of 39x over Pandas.
โ†ณ But FireDucks had an average speed-up of 50x over Pandas.

3. Pandas is single-core; FireDucks is multi-core.

4. Pandas follows eager execution; FireDucks is based on lazy execution. This way, FireDucks can build a logical execution plan and apply possible optimizations.

5. That said, even under eager execution, FireDucks is way faster than Pandas, as depicted in the image below.

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #FireDucks

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Forwarded from Tomas
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Master #DataAnalytics for real-world problems. I have curated a list of 40 Data Analytics Projects (solved & explained) that will help you build analytics skills using #Python.

Includes projects like:

1. Rainfall Trends in India Analysis
2. Netflix Content Strategy Analysis
3. Creating a Mutual Fund Plan
4. Stock Market Portfolio Optimization
5. Metro Operations Optimization
6. Analyzing the Impact of Carbon Emissions

Find this list of projects here:
https://thecleverprogrammer.com/2024/11/01/data-analytics-projects-with-python/

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #deeplearning #Projects

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๐Ÿผ 20 of the most used Pandas + PDF functions

๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป The first time I used Pandas, I was supposed to quickly clean and organize a raw and complex dataset with the help of Pandas functions. Using the groupby function, I was able to categorize the data and get in-depth analysis of customer behavior. Best of all, it was when I used loc and iloc that I could easily filter the data.

โœ”๏ธ Since then I decided to prepare a list of the most used Pandas functions that I use on a daily basis. Now this list is ready! In the following, I will introduce 20 of the best and most used Pandas functions:



๐Ÿณ๏ธโ€๐ŸŒˆ read_csv(): Fast data upload from CSV files

๐Ÿณ๏ธโ€๐ŸŒˆ head(): look at the first five rows of the database to start..

๐Ÿณ๏ธโ€๐ŸŒˆ info(): Checking data structure such as data type and empty values.

๐Ÿณ๏ธโ€๐ŸŒˆ describe(): Generate descriptive statistics for numeric columns.

๐Ÿณ๏ธโ€๐ŸŒˆ loc[ ]: accesses rows and columns by label or condition.

๐Ÿณ๏ธโ€๐ŸŒˆ iloc[ ]: Access data by row number.

๐Ÿณ๏ธโ€๐ŸŒˆ merge(): Merge dataframes with common columns.

๐Ÿณ๏ธโ€๐ŸŒˆ groupby(): Grouping for easier analysis.

๐Ÿณ๏ธโ€๐ŸŒˆ pivot_table(): Summarize data in pivot table format.

๐Ÿณ๏ธโ€๐ŸŒˆ to_csv(): Save data as a CSV file.

๐Ÿณ๏ธโ€๐ŸŒˆ pd.concat(): Concatenate multiple dataframes in rows or columns.

๐Ÿณ๏ธโ€๐ŸŒˆ pd.melt(): Convert wide format data to long format.

๐Ÿณ๏ธโ€๐ŸŒˆ pd.pivot_table(): Create a pivot table with multiple levels.

๐Ÿณ๏ธโ€๐ŸŒˆ pd.cut(): Split the data into specific intervals.

๐Ÿณ๏ธโ€๐ŸŒˆ pd.qcut(): Sort data by percentage.

๐Ÿณ๏ธโ€๐ŸŒˆ pd.merge(): Merge data in database style for advanced linking.

๐Ÿณ๏ธโ€๐ŸŒˆ DataFrame.apply(): Apply a custom function to the data.

๐Ÿณ๏ธโ€๐ŸŒˆ DataFrame.groupby(): Analyze grouped data.

๐Ÿณ๏ธโ€๐ŸŒˆ DataFrame.drop_duplicates(): Drop duplicate rows.

๐Ÿณ๏ธโ€๐ŸŒˆ DataFrame.to_excel(): Save data directly to Excel file.


โ”Œ ๐Ÿผ Pandas Functions
โ””
๐Ÿ“„ PDF

#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents

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๐Ÿ‘ฉโ€๐Ÿ’ป Python Developer Roadmap is a guide for aspiring Python developers that helps structure and plan their learning and career development!

๐ŸŒŸ It provides a step-by-step plan that covers key aspects of Python development, from basic knowledge and syntax to more advanced topics such as databases, web development, testing, machine learning, and microservices development.

๐Ÿ” License: MIT

๐Ÿ–ฅ Github

https://t.me/CodeProgrammer โœ…
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๐ŸŽฏ All free IBM courses for data science
โœ… Along with a certificate of completion


1๏ธโƒฃ Data Science Fundamentals Course

โœ๏ธ Learn basic data science concepts such as analysis, modeling, and its real-world applications.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

2๏ธโƒฃ Applied Data Science Course with Python

โœ๏ธ Learn how to use Python for data analysis, modeling, and practical projects.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

3๏ธโƒฃ Data Analysis Course with Python

โœ๏ธ Data analysis skills using Python libraries such as Pandas and NumPy.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

4๏ธโƒฃ Data visualization course with Python

โœ๏ธ Learn to create advanced charts with tools like Matplotlib and Seaborn.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

5๏ธโƒฃ Applied Data Science Course with R

โœ๏ธ Using the R language to analyze data and implement data science projects.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

6๏ธโƒฃ Data visualization course with R

โœ๏ธ Learn how to create professional charts and visualize data with tools like ggplot2.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

7๏ธโƒฃ Big Data Fundamentals Course

โœ๏ธ Learn the fundamentals of big data and related technologies such as Hadoop and Spark.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

8๏ธโƒฃ Scala Programming Course for Data Science

โœ๏ธ Familiarity with the Scala language and its use in data analysis projects.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

9๏ธโƒฃ Data Science for Business Course

โœ๏ธ Learn how to use data to improve business decisions.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

1๏ธโƒฃ Deep Learning Fundamentals Course

โœ๏ธ Familiarity with the basics of deep learning and the concepts of neural networks.
โœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธโœ‚๏ธ

1๏ธโƒฃ Deep Learning Course with TensorFlow

โœ๏ธ Working with TensorFlow to build and train deep learning models.

https://t.me/CodeProgrammer โœ…
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โœ… A comprehensive playlist to step into and master the world of machine learning and data science!


1๏ธโƒฃ Data Science Principles:

๐Ÿ˜‰ Essential Mathematics for Machine Learning: Link
๐Ÿ˜‰ Overview and commonly used terms: Link
๐Ÿ˜‰ Current interview trends: Link
๐Ÿ˜‰ Linear Regression Guide: Link
๐Ÿ˜‰ Logistic Regression Playlist: Link
๐Ÿ˜‰ Classification criteria: Link
๐Ÿ˜‰ Simple Bayes Classifier: Link
๐Ÿ˜‰ Types of variables: Link
๐Ÿ˜‰ Dimension reduction: Link
๐Ÿ˜‰ Entropy, mutual entropy, KL divergence: link
๐Ÿ˜‰ Dynamic Pricing Overview: Link


2๏ธโƒฃ Building recommender systems:

๐Ÿ˜‰ Netflix Calibrated Recommendations: Link
๐Ÿ˜‰ Netflix Integrated Recommendation Model: Link
๐Ÿ˜‰ The Evolution of Recommender Systems: Link
๐Ÿ˜‰ Embedding tutorial: Link
๐Ÿ˜‰ Annoy library for approximate nearest neighbor: link
๐Ÿ˜‰ Reducer product for ANN: Link
๐Ÿ˜‰ Model-based account recommendations: Link
๐Ÿ˜‰ PID controller for diversity: link
๐Ÿ˜‰ Instagram Recommender System: Link
๐Ÿ˜‰ LinkedIn CTR Modeling: Link
๐Ÿ˜‰ Meituan's two-tower recommendation model: Link
๐Ÿ˜‰ Scalable Two Tower Model Question-Item: Link
๐Ÿ˜‰ Twitter Recommender Algorithm: Link
๐Ÿ˜‰ eBay language model for recommender system: link
๐Ÿ˜‰ Overcoming biases for recommender systems: Link


3๏ธโƒฃ Advanced Model Techniques and Applications:

๐Ÿ˜‰ Importance of Model Calibration: Link
๐Ÿ˜‰ Detect and monitor data changes: Link
๐Ÿ˜‰ Neural Networks Training: Link
๐Ÿ˜‰ Analytics-based advertising with Pinterest: Link
๐Ÿ˜‰ Using Pre-trained Bert: Link
๐Ÿ˜‰ Model Compression with Knowledge Distillation: Link
๐Ÿ˜‰ Multi-Armed Bandit Strategies: Link


4๏ธโƒฃ The world of large language models (LLMs):

๐Ÿ˜‰ Conversational AI: Link
๐Ÿ˜‰ The dual nature of conversational language models: link
๐Ÿ˜‰ Frontier Developments in LLM: Link
๐Ÿ˜‰ Improving the performance of open source LLMs: Link
๐Ÿ˜‰ Building artificial intelligence in Shah Rukh Khan style: Link

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas

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Data Visualization Cheat sheets and Resources.zip
127.4 MB
Data Visualization Cheat sheets and Resources

Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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IP Address Information using Python ๐Ÿ–ฅ

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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๐Ÿš€ Free Python Course with Certificate๐Ÿš€

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All Cheat Sheets Collection (3).pdf
2.7 MB
Python Cheatsheets โญ๏ธ

Don't forget to React
โค๏ธ to this msg if you want more content Like this ๐Ÿ‘

๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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python ๐Ÿ’™โœจ.pdf
916.8 KB
Python Notes โญ๏ธ

Don't forget to React
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๐Ÿ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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