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

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💠 All free Kaggle courses for data science
📁 Along with the course completion certificate

Python ⬅️ link

An introduction to machine learning ⬅️ link

Pandas ⬅️ link

Medium machine learning ⬅️ link

Data visualization ⬅️ link

Feature engineering ⬅️ link

An introduction to the SQL language ⬅️ link

Advanced SQL language ⬅️ link

An introduction to deep learning ⬅️ link

Computer vision ⬅️ link

Time series ⬅️ link

Data cleanup ⬅️ link

Geographical analysis ⬅️ link

Explainability of machine learning ⬅️ link

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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Python List Methods clearly Explained

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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🐱 7 of the best GitHub repos
To enter the world of data analysis and data science


1⃣ 100 Days of ML Code repo

✍️ A hundred-day program for learning and practicing machine learning coding.


🔢 Awesome Data Science repo

✍️ A curated list of great data science resources such as books, software, and tools.


🔢 Data Science for Beginners repo

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


🔢 Data Science Interviews Repo

✍️ A repository of questions and answers for science job interviews.


🔢 ML Technical Interviews repo

✍️ A good guide for machine learning and artificial intelligence technical interviews.


🔢 ML Interviews repo

✍️ A repository containing machine learning interview questions from basic topics to complex topics such as neural networks and reinforcement learning.


🔢 Data Science Python Notebooks repo

✍️ A collection of notebooks in various fields of data science such as deep learning, machine learning, data analysis and Python topics.

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

http://t.me/codeprogrammer ⭐️
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List of Running Processes using Python

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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Pandas is getting outdated.

5 reasons you should move to FireDucks 👇

1. Requires changing ONLY ONE line of code:
↳ Replace "𝗶𝗺𝗽𝗼𝗿𝘁 𝗽𝗮𝗻𝗱𝗮𝘀 𝗮𝘀 𝗽𝗱" with "𝗶𝗺𝗽𝗼𝗿𝗲 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝗽𝗮𝗻𝗱𝗮𝘀 𝗮𝘀 𝗽𝗱"
↳ The rest of the entire code remains the same.
↳ So, if you know Pandas, you already know how to use FireDucks.
↳ Done!

2. Ridiculously faster as per official benchmarks:
↳ Modin had an average speed-up of 0.9x over Pandas.
↳ Polars had an average speed-up of 39x over Pandas.
↳ 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.

📂 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/

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #deeplearning #Projects

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🐼 20 of the most used Pandas + PDF functions

👨🏻‍💻 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.

✔️ 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:



🏳️‍🌈 read_csv(): Fast data upload from CSV files

🏳️‍🌈 head(): look at the first five rows of the database to start..

🏳️‍🌈 info(): Checking data structure such as data type and empty values.

🏳️‍🌈 describe(): Generate descriptive statistics for numeric columns.

🏳️‍🌈 loc[ ]: accesses rows and columns by label or condition.

🏳️‍🌈 iloc[ ]: Access data by row number.

🏳️‍🌈 merge(): Merge dataframes with common columns.

🏳️‍🌈 groupby(): Grouping for easier analysis.

🏳️‍🌈 pivot_table(): Summarize data in pivot table format.

🏳️‍🌈 to_csv(): Save data as a CSV file.

🏳️‍🌈 pd.concat(): Concatenate multiple dataframes in rows or columns.

🏳️‍🌈 pd.melt(): Convert wide format data to long format.

🏳️‍🌈 pd.pivot_table(): Create a pivot table with multiple levels.

🏳️‍🌈 pd.cut(): Split the data into specific intervals.

🏳️‍🌈 pd.qcut(): Sort data by percentage.

🏳️‍🌈 pd.merge(): Merge data in database style for advanced linking.

🏳️‍🌈 DataFrame.apply(): Apply a custom function to the data.

🏳️‍🌈 DataFrame.groupby(): Analyze grouped data.

🏳️‍🌈 DataFrame.drop_duplicates(): Drop duplicate rows.

🏳️‍🌈 DataFrame.to_excel(): Save data directly to Excel file.


🐼 Pandas Functions
📄 PDF

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👩‍💻 Python Developer Roadmap is a guide for aspiring Python developers that helps structure and plan their learning and career development!

🌟 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.

🔐 License: MIT

🖥 Github

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


1️⃣ Data Science Fundamentals Course

✏️ Learn basic data science concepts such as analysis, modeling, and its real-world applications.
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✏️ Data analysis skills using Python libraries such as Pandas and NumPy.
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✏️ Using the R language to analyze data and implement data science projects.
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✏️ Working with TensorFlow to build and train deep learning models.

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A comprehensive playlist to step into and master the world of machine learning and data science!


1️⃣ Data Science Principles:

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


2️⃣ Building recommender systems:

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


3️⃣ Advanced Model Techniques and Applications:

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


4️⃣ The world of large language models (LLMs):

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

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas

http://t.me/codeprogrammer ⭐️
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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

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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IP Address Information using Python 🖥

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

http://t.me/codeprogrammer ⭐️
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📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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All Cheat Sheets Collection (3).pdf
2.7 MB
Python Cheatsheets ⭐️

Don't forget to React
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📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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