📹 3blue1brown presented the shortest and most understandable lecture on neural networks!

In the new episode, he talks about the mechanism of attention and transformers. The lecture has become even more concise and exciting!

Ideal for absolute beginners and even those who are far from technical.

The author managed to explain the key aspects of the neural network in just 9 minutes using bright graphics and simple examples.

📌 Original

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

http://t.me/codeprogrammer ⭐️

📕 Python Basics Made Simple!

📷 Course: AI Python for Beginners

👨‍💻 Instructor: Andrew Ng

In the #AIPythonforBeginners course series you'll learn how to identify strings, integers, and floats with the type() function, and build a solid Python foundation for your AI journey.

Enroll Free: https://learn.deeplearning.ai/courses/ai-python-for-beginners

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

http://t.me/codeprogrammer ⭐️

💠 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

http://t.me/codeprogrammer ⭐️

Python List Methods clearly Explained

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

http://t.me/codeprogrammer ⭐️

🐱 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 ⭐️

List of Running Processes using Python

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

http://t.me/codeprogrammer ⭐️

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

http://t.me/codeprogrammer ⭐️

5 Free Courses for Mastering LLMs

Link: https://machinelearningmastery.com/5-free-courses-mastering-llms

https://t.me/CodeProgrammer ✅

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

http://t.me/codeprogrammer ⭐️

Best LLMs Courses

Link: https://www.mltut.com/best-large-language-models-courses/

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

http://t.me/codeprogrammer ⭐️

🐼 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

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

http://t.me/codeprogrammer ⭐️

Regression & Classification Loss Functions

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

http://t.me/codeprogrammer ⭐️

Python Network Programming Cheat Sheet 🖥

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

http://t.me/codeprogrammer ✅

Git commands basics

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

http://t.me/codeprogrammer ✅

👩‍💻 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 ✅

🎯 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.
✂️✂️✂️✂️✂️

2️⃣ Applied Data Science Course with Python

✏️ Learn how to use Python for data analysis, modeling, and practical projects.
✂️✂️✂️✂️✂️

3️⃣ Data Analysis Course with Python

✏️ Data analysis skills using Python libraries such as Pandas and NumPy.
✂️✂️✂️✂️✂️

4️⃣ Data visualization course with Python

✏️ Learn to create advanced charts with tools like Matplotlib and Seaborn.
✂️✂️✂️✂️✂️

5️⃣ Applied Data Science Course with R

✏️ Using the R language to analyze data and implement data science projects.
✂️✂️✂️✂️✂️

6️⃣ Data visualization course with R

✏️ Learn how to create professional charts and visualize data with tools like ggplot2.
✂️✂️✂️✂️✂️

7️⃣ Big Data Fundamentals Course

✏️ Learn the fundamentals of big data and related technologies such as Hadoop and Spark.
✂️✂️✂️✂️✂️

8️⃣ Scala Programming Course for Data Science

✏️ Familiarity with the Scala language and its use in data analysis projects.
✂️✂️✂️✂️✂️

9️⃣ Data Science for Business Course

✏️ Learn how to use data to improve business decisions.
✂️✂️✂️✂️✂️

1️⃣ Deep Learning Fundamentals Course

✏️ Familiarity with the basics of deep learning and the concepts of neural networks.
✂️✂️✂️✂️✂️

1️⃣ Deep Learning Course with TensorFlow

✏️ Working with TensorFlow to build and train deep learning models.

https://t.me/CodeProgrammer ✅

✅ 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 ⭐️

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

http://t.me/codeprogrammer ⭐️

IP Address Information using Python 🖥

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

http://t.me/codeprogrammer ⭐️