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The Large Language Model Course

How to become an #LLM Scientist or Engineer from scratch

🖕 Read Free: https://towardsdatascience.com/the-large-language-model-course-b6663cd57ceb/

😱 Download Roadmap Hd: https://towardsdatascience.com/wp-content/uploads/2025/01/0VFFby0bpiwUeaELv.png

#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

https://t.me/CodeProgrammer 🖥




You should do something about your AI skills. Why not do it this week, when all our AI courses, tracks, certifications and projects are 100% FREE? 🚀

Swipe for a few suggestions on courses, then hurry up and pick your favorites!

🟢 is ticking:

🔗 Register Free: https://www.datacamp.com/campaign/free-ai-access-week-2025

#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

https://t.me/CodeProgrammer ✈️


Python, Bash and SQL Essentials for Data Engineering Specialization

What you'll learn
Develop #dataengineering solutions with a minimal and essential subset of the Python language and the Linux environment

Design scripts to connect and query a #SQL #database using #Python

Use a #scraping library in Python to read, identify and extract data from websites

Enroll Free: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke

https://t.me/DataScience4


Running a Neural Network Model in OpenCV

Many machine learning models have been developed, each with strengths and weaknesses. This catalog is not complete without neural network models. In OpenCV, you can use a neural network model developed using another framework. In this post, you will learn about the workflow of applying a neural network in OpenCV. Specifically, you will learn:

🏐 What OpenCV can use in its neural network model
🏐 How to prepare a neural network model for OpenCV

Read: https://machinelearningmastery.com/running-a-neural-network-model-in-opencv/


What is the biggest obstacle to your success in your academic and scientific career?
Poll
  •   Poverty and lack of money
  •   Poor quality of education
  •   Laziness and procrastination
  •   Social, political and security problems
  •   Excessive desire (lust)
  •   Inability to concentrate
  •   Emotional problems
4 votes


"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

#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

https://t.me/CodeProgrammer ✈️


Practical Deep Learning

A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
New!

Enroll Free: https://course.fast.ai/

#DeepLearning #MachineLearning #FreeCourse #CodingSkills #AI #ArtificialIntelligence #PracticalAI #TechEducation #LearnToCode #DataScience #NeuralNetworks #PythonProgramming #TechInnovation #SelfPacedLearning #FutureSkills

https://t.me/CodeProgrammer 👩‍💻


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how transformers remember facts

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

https://t.me/DataScienceM


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

https://t.me/CodeProgrammer ✈️


🚀 EXCITING UPDATE ALERT 🚀

A brand-new, FREE course on LLM evaluations has just been launched in collaboration with Elvis Saravia! Dive into the world of AI model assessments and elevate your skills with cutting-edge insights.

📚 What’s Included?
- ⚖️ In-depth coverage of LLM-as-a-judge metrics, LLM unit testing, monitoring, and beyond
- 🤖 Hands-on experience with real-world projects, such as building a YouTube search agent
- 🎉 Access to open-source models via LiteLLM

Whether you're an AI enthusiast, developer, or researcher, this course is designed to empower you with practical knowledge and tools. Don’t miss out—enroll now to secure your spot! 👇
https://www.comet.com/site/llm-course/

#AI #MachineLearning #LLM #OpenSource #FreeCourse #TechEducation #DataScience #ArtificialIntelligence #LearnAI #LiteLLM #ElvisSaravia

https://t.me/CodeProgrammer


Some people asked me about a resource for learning about Transformers.

Here's a good one I am sharing again -- it covers just about everything you need to know.

brandonrohrer.com/transformers

Amazing stuff. It's totally worth your weekend.

#Transformers #DeepLearning #NLP #AI #MachineLearning #SelfAttention #DataScience #Technology #Python #LearningResource


🔰 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

https://t.me/CodeProgrammer

7.3k 2 317 3 42

Good morning all 😊


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

https://t.me/CodeProgrammer


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




Applied Machine Learning in Python: a Hands-on Guide with Code 🧠

🚀 Exciting news! free, online e-book has been updated with fresh theory 📕, detailed illustrations 🎨, well-documented demos 📝, links to YouTube lectures 🎥, and interactive dashboards 📊!

Each chapter is downloadable 📥, making it easy for you to dive in, learn, and complete your #DataScience projects efficiently! 🧑‍💻

Explore it now: https://geostatsguy.github.io/MachineLearningDemos_Book/intro.html

#DeepLearning #Python #MachineLearning #AI #DataAnalytics #TechEducation #FreeLearning #Ebook #DataVisualization #Coding #STEM #TechCommunity #LearnToCode

https://t.me/CodeProgrammer


80 Python Interview Questions.pdf
410.4Kb
🚀 80 Python Interview Questions with Answers & Code! 🚀

Why this resource? 
- Covers frequently asked questions in Python interviews 

📄 Each question comes with detailed answers and ready-to-use code snippets, making it perfect for beginners and experienced developers alike. Whether you're preparing for a job interview or leveling up your Python skills, this guide has you covered! 👀 

🔥 Don’t miss out! Save this, share it, and start preparing today! 💼 

#Python #DataScience #Programming #InterviewPrep #Coding #PythonInterview #TechInterview #DataScientist #PythonProgramming #LearnPython #CodeNewbie #CareerGrowth #TechJobs #PythonCode #PythonTips 

https://t.me/CodeProgrammer

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