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Create pivot tables in your Jupyter Notebook:

Here's the link to the #GitHub repo and documentation:

https://pivottable.js.org/examples/

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

https://t.me/DataScienceM


🍃 Stem-Leaf Plot - An intelligent visualization!

It's a simple and effective way to visualize and compare datasets.

📊 Imagine we have two datasets: Set 1 (7, 12, 14, 17, 19, 23, 25) and Set 2 (3, 11, 16, 18, 20, 21, 24). We'll use a stem-leaf plot to compare them.

🌿 First, let's create the 'stem' which represents the tens place (0, 1, 2) and the 'leaf' represents the ones place (0-9).

🔍 By comparing the plots, we can see that Dataset 1 has higher values in the tens place, while Dataset 2 has a more uniform distribution.

🎯 Stem-leaf plots are great for small datasets and provide a clear picture of data distribution. The special thing about a stem-and-leaf diagram is that the original data can be read out of the graphical representation.


Give it a try next time you need to compare datasets!

✍🏽 Have you used stem-leaf plots before?

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

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Complete Roadmap to Become a Data Scientist

#python #datascientist

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Best Deep Learning Courses:
https://mltut.com/best-deep-learning-courses-on-coursera/

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

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"Introduction to Applied Linear Algebra" by S. Boyd (Stanford) & L. Vandenberghe (UCLA)

📘 Freely available at: https://web.stanford.edu/~boyd/vmls/

📽 Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9

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Last week we introduced how transformer LLMs work, this week we go deeper into one of its key elements—the attention mechanism, in a new #OpenSourceAI course, Attention in Transformers: Concepts and #Code in #PyTorch

Enroll Free: https://www.deeplearning.ai/short-courses/attention-in-transformers-concepts-and-code-in-pytorch/

#LLMCourse #Transformers #MachineLearning #AIeducation #DeepLearning #TechSkills #ArtificialIntelligence

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Discover an incredible LLM course designed to deepen your understanding of the transformer architecture and its role in building powerful Large Language Models (LLMs). This course breaks down complex concepts into easy-to-grasp modules, making it perfect for both beginners and advanced learners. Dive into the mechanics of attention mechanisms, encoding-decoding processes, and much more. Elevate your AI knowledge and stay ahead in the world of machine learning!

Enroll Free: https://www.deeplearning.ai/short-courses/how-transformer-llms-work/
#LLMCourse #Transformers #MachineLearning #AIeducation #DeepLearning #TechSkills #ArtificialIntelligence

https://t.me/DataScienceM

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how transformers remember facts

#Transformers #NLP #LLM #MachineLearning #DeepLearning #AI #ArtificialIntelligence #TechInnovation #DataScience #NeuralNetworks

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Forward from: Python | Machine Learning | Coding | R
MIT's "Machine Learning" lecture notes

PDF: https://introml.mit.edu/_static/spring24/LectureNotes/6_390_lecture_notes_spring24.pdf

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

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"Mathematical Foundations of Machine Learning"

PDF: https://nowak.ece.wisc.edu/MFML.pdf

#Mathematics #MachineLearning #AI #DataScience #MathFoundations #MLTheory #ArtificialIntelligence #EducationalResources #PDFResource #TechLearning

https://t.me/DataScienceM


Need a reference for algebra?

Here's a cheat sheet you can download

https://t.me/DataScienceM

#Algebra #Statistics #Mathematics #DataAnalysis #Equations #Probability #MathSkills #NumbersGame #QuantitativeReasoning #MathIsFun


Forward from: Python | Machine Learning | Coding | R
🔰 How to become a data scientist in 2025?

👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.


🔢 Step 1: Strengthen your math and statistics!

✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:

Linear algebra: matrices, vectors, eigenvalues.

🔗 Course: MIT 18.06 Linear Algebra


Calculus: derivative, integral, optimization.

🔗 Course: MIT Single Variable Calculus


Statistics and probability: Bayes' theorem, hypothesis testing.

🔗 Course: Statistics 110

➖➖➖➖➖

🔢 Step 2: Learn to code.

✏️ Learn Python and become proficient in coding. The most important topics you need to master are:

Python: Pandas, NumPy, Matplotlib libraries

🔗 Course: FreeCodeCamp Python Course

SQL language: Join commands, Window functions, query optimization.

🔗 Course: Stanford SQL Course

Data structures and algorithms: arrays, linked lists, trees.

🔗 Course: MIT Introduction to Algorithms

➖➖➖➖➖

🔢 Step 3: Clean and visualize data

✏️ Learn how to process and clean data and then create an engaging story from it!

Data cleaning: Working with missing values ​​and detecting outliers.

🔗 Course: Data Cleaning

Data visualization: Matplotlib, Seaborn, Tableau

🔗 Course: Data Visualization Tutorial

➖➖➖➖➖

🔢 Step 4: Learn Machine Learning

✏️ It's time to enter the exciting world of machine learning! You should know these topics:

Supervised learning: regression, classification.

Unsupervised learning: clustering, PCA, anomaly detection.

Deep learning: neural networks, CNN, RNN


🔗 Course: CS229: Machine Learning

➖➖➖➖➖

🔢 Step 5: Working with Big Data and Cloud Technologies

✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.

Big Data Tools: Hadoop, Spark, Dask

Cloud platforms: AWS, GCP, Azure

🔗 Course: Data Engineering

➖➖➖➖➖

🔢 Step 6: Do real projects!

✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.

Kaggle competitions: solving real-world challenges.

End-to-End projects: data collection, modeling, implementation.

GitHub: Publish your projects on GitHub.

🔗 Platform: Kaggle🔗 Platform: ods.ai

➖➖➖➖➖

🔢 Step 7: Learn MLOps and deploy models

✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.

MLOps training: model versioning, monitoring, model retraining.

Deployment models: Flask, FastAPI, Docker

🔗 Course: Stanford MLOps Course

➖➖➖➖➖

🔢 Step 8: Stay up to date and network

✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.

Read scientific articles: arXiv, Google Scholar

Connect with the data community:

🔗 Site: Papers with code
🔗 Site: AI Research at Google


#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast

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Forward from: Python | Machine Learning | Coding | R
The Big Book of Large Language Models by Damien Benveniste

✅ Chapters:
1⃣ Introduction

🔢 Language Models Before Transformers

🔢 Attention Is All You Need: The Original Transformer Architecture

🔢 A More Modern Approach To The Transformer Architecture

🔢 Multi-modal Large Language Models

🔢 Transformers Beyond Language Models

🔢 Non-Transformer Language Models

🔢 How LLMs Generate Text

🔢 From Words To Tokens

1⃣0⃣ Training LLMs to Follow Instructions

1⃣1⃣ Scaling Model Training

1⃣🔢 Fine-Tuning LLMs

1⃣🔢 Deploying LLMs

Read it: https://book.theaiedge.io/

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast

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Forward from: Python | Machine Learning | Coding | R
Free Certification Courses to Learn Data Analytics in 2025:

1. Python
🔗 https://imp.i384100.net/5gmXXo

2. SQL
🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql

3. Statistics and R
🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r

4. Data Science: R Basics
🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics

5. Excel and PowerBI
🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/

6. Data Science: Visualization
🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization

7. Data Science: Machine Learning
🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

8. R
🔗https://imp.i384100.net/rQqomy

9. Tableau
🔗https://imp.i384100.net/MmW9b3

10. PowerBI
🔗 https://lnkd.in/dpmnthEA

11. Data Science: Productivity Tools
🔗 https://lnkd.in/dGhPYg6N

12. Data Science: Probability
🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science

13. Mathematics
🔗http://matlabacademy.mathworks.com

14. Statistics
🔗 https://lnkd.in/df6qksMB

15. Data Visualization
🔗https://imp.i384100.net/k0X6vx

16. Machine Learning
🔗 https://imp.i384100.net/nLbkN9

17. Deep Learning
🔗 https://imp.i384100.net/R5aPOR

18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10

19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling

20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability

22. Introduction to Linear Models and Matrix Algebra
🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra

23. Data Science: Capstone
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone

24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY

26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2

27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience


🔗 Roadmap to become NLP Expert in 2025

https://t.me/DataScienceM


Forward from: Python | Machine Learning | Coding | R
Embark on an exciting journey through the intricate world of Artificial Intelligence with our comprehensive learning map! ✅

1⃣ Artificial Intelligence (AI)
Dive into the vast universe of AI, where machines learn to perform tasks that typically require human intelligence. From Reinforcement Learning to Augmented Programming, this broad circle encompasses a wide array of techniques and applications. Whether you're interested in Speech Recognition or Algorithm Building, this is your starting point for understanding how machines can mimic human cognition. #AI #MachineIntelligence

🔢 Machine Learning (ML)
As we move inward, explore the fascinating realm of Machine Learning, a subset of AI focused on developing algorithms that enable machines to learn from data. Discover the power of Supervised and Unsupervised Learning, K-Means clustering, and Hypothesis Testing. This circle will equip you with the skills needed to analyze data and build predictive models. #MachineLearning #DataScience

🔢 Neural Networks
Next, delve into Neural Networks, computer models designed to simulate the workings of the human brain. These networks are used in various applications, from image recognition to natural language processing. Learn about Backpropagation, Feed Forward networks, and Support Vector Machines. This circle will provide you with the foundation to develop complex models that can solve real-world problems. #NeuralNetworks #DeepLearningBasics

🔢 Deep Learning
In the narrower circle, discover Deep Learning, an advanced branch of ML that uses multi-layered neural networks to tackle complex challenges. Explore Long Short-Term Memory (LSTM) networks, Transformers, and Auto Encoders. These techniques are at the forefront of modern AI applications like machine translation and medical diagnosis. Join us to master these cutting-edge technologies. #DeepLearning #AdvancedAI

🔢 Generative AI
Finally, in the smallest and most specialized circle, uncover Generative AI, which focuses on creating new and innovative content using AI. Dive into Generative Adversarial Networks (GANs), Large Language Models (LLM), and Transfer Learning. This circle will empower you to generate creative content such as images and text using AI. #GenerativeAI #CreativeTech

Our AI learning map is your gateway to mastering the latest advancements in technology. Whether you're a beginner eager to grasp the basics or a professional looking to expand your expertise, this map offers a clear path to achieving your goals in the ever-evolving field of AI. Start your journey today and unlock the potential of artificial intelligence! #AILearningMap #TechFuture

https://t.me/CodeProgrammer ✈️


Hugging Face Transformers: Leverage Open-Source AI in Python

Transformers is a powerful Python library created by Hugging Face that allows you to download, manipulate, and run thousands of pretrained, open-source AI models. These models cover multiple tasks across modalities like natural language processing, computer vision, audio, and multimodal learning. Using pretrained open-source models can reduce costs, save the time needed to train models from scratch, and give you more control over the models you deploy.

More: https://realpython.com/huggingface-transformers/

https://t.me/DataScienceM 🆙


Forward from: Python | Machine Learning | Coding | R
⚠️ O'Reilly Media, one of the most reputable publishers in the fields of programming, data mining, and AI, has made 10 data science books available to those interested in this field for free .

✔️ To use the online and PDF versions of these books, you can use the following links:👇

0⃣ Python Data Science Handbook
Online
PDF

1⃣ Python for Data Analysis book
Online
PDF

🔢 Fundamentals of Data Visualization book
Online
PDF

🔢 R for Data Science book
Online
PDF

🔢 Deep Learning for Coders book
Online
PDF

🔢 DS at the Command Line book
Online
PDF

🔢 Hands-On Data Visualization Book
Online
PDF

🔢 Think Stats book
Online
PDF

🔢 Think Bayes book
Online
PDF

🔢 Kafka, The Definitive Guide
Online
PDF

#DataScience #Python #DataAnalysis #DataVisualization #RProgramming #DeepLearning #CommandLine #HandsOnLearning #Statistics #Bayesian #Kafka #MachineLearning #AI #Programming #FreeBooks

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📚 Python For Large Language Models (2025)

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Forward from: Python | Machine Learning | Coding | R
ChatGPT Cheat Sheet for Business (2025).pdf
8.0Mb
ChatGPT Cheat Sheet for Business - DataCamp

Unlock the full potential of AI with our comprehensive ChatGPT Cheat Sheet for Business! Tailored specifically for professionals and entrepreneurs, this guide offers actionable insights on leveraging ChatGPT to streamline workflows, enhance customer interactions, and drive business growth. Whether you're a marketing specialist, project manager, or CEO, this cheat sheet is your go-to resource for mastering conversational AI.

From crafting compelling content to automating routine tasks, learn how to harness the power of ChatGPT in real-world business scenarios. With clear examples and step-by-step instructions, you’ll be able to integrate ChatGPT seamlessly into your operations, improving efficiency and innovation.

Don’t miss out on staying ahead of the competition by embracing the future of AI-driven solutions!

#ChatGPT #AIforBusiness #DataCamp #CheatSheet #ConversationalAI #BusinessGrowth #Automation #CustomerEngagement #ContentCreation #EfficiencyBoost #Innovation #FutureOfWork #TechTrends #AIInnovation #DigitalTransformation #BusinessSuccess

https://t.me/CodeProgrammer ⭐️

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