I.M.Z

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[I.M.Z @ismaj1030]

@joycemedina1923 @joycemedina1923 aquí pareja de 25 años para intercambio en CDMX 🙏🏻🫶🏻

[Mehmet Songur @mehmetsongur_]

Tez, makale, makine öğrenmesi veya veri analizi projeleriniz için temiz ve kaliteli veriye mi ihtiyacınız var?

Google Dataset Search, araştırmacılar ve öğrenciler için tam bir hayat kurtarıcı! Dünyanın her yerinden milyonlarca açık veri kümesi parmaklarınızın ucunda.

https://t.co/6RkXZBX7ys

[Miguel Ángel Durán @midudev]

¡Aprende Programación, Cloud y DevOps practicando!
Servidores gratis con ejercicios reales.

✓ Practica Git y servidores Linux
✓ Crea y prueba recursos AWS sin miedo
✓ Kubernetes, Docker, Terraform y más

No hace falta registro:
https://t.co/bEONWcAc91

[Vaishnavi @_vmlops]

The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges & Opportunities

https://t.co/TdxOaj05Ve

[Neo Kim @systemdesignone]

API Design Playbook

Giveaway Alert!!!

• Core API fundamentals.
• Clean & scalable design principles.
• Popular patterns used in real-world systems.
• Practical concepts for interviews & building projects.

(24 hours only.)

To get it for free:

1 Follow @systemdesignone [MUST]
2 Like & Retweet to get DM
3 Reply "Playbook"

Then I'll DM you the details.

[Python Coding @clcoding]

The Big Book of Small Python Projects: 81 Easy Practice Programs (Free Book)

Download the book for free: https://t.co/RbBW0XsM5b

[Kirk Borne @KirkDBorne]

Ace the #DataScience Interview with 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street: https://t.co/yENgxdSS6m
—————
#Analytics #PredictiveAnalytics #DataScientist #AI #MachineLearning #ML #Statistics #Algorithms #SQL #Database #Python

[Kirk Borne @KirkDBorne]

Machine Learning Refined — Foundations, Algorithms, and Applications (with 100 in-depth coding exercises in Python): https://t.co/oUBZkXUe4x
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#DataScience #ML #AI #Mathematics #DataScientist

[Kirk Borne @KirkDBorne]

[PDF] Extensive Tutorial on #OutlierDetection Techniques: https://t.co/XeX1Ye1RLP
➕
🌟Comprehensive textbook on Outlier Analysis: https://t.co/XWGQ3IFzia
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#DataScience #AI #MachineLearning #ML #Statistics #Edge #EdgeAI #EdgeComputing #TimeSeries #AnomalyDetection

[Data Science Dojo @DataScienceDojo]

Wondering if the LLM Bootcamp is right for you?

Join our Information Session to find out who it’s really designed for — whether you’re a data professional looking to level up with GenAI, a product leader aiming to build smarter AI-driven products, or a beginner taking your first step into the world of Large Language Models.

We’ll walk you through the structure, learning outcomes, and real-world applications so you can decide with clarity.

If you’re exploring AI, this is the best place to start. 🚀

Register now and secure your spot: https://t.co/ch0HhdLTdR

[Kirk Borne @KirkDBorne]

Modern #TimeSeries #Forecasting with #Python — Industry-ready #MachineLearning and #DeepLearning time series analysis with PyTorch and PANDAS: https://t.co/TUYDe9a2jN v/ @PacktDataML

𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
🔵Apply ML and global models to improve forecasting accuracy through practical examples.
🟢Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS.
🔵Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions.
Also... Purchase of the print or Kindle book includes a free eBook in PDF format

[Mili @Ing_Mili]

Mi compañera de vuelo, si te gustan los temas de IA 🤖 este libro de Pedro Salazar Ugarte es un #MustRead 📗

[Suryakant Chaurasiya @heyitsurya]

A lot of people think learning AI requires expensive courses.

It doesn’t.

Some of the best AI education online is completely free directly from the companies building the technology.

A major part of my AI learning journey came from combining two things:

• Learning the fundamentals
• Building projects consistently

That combination changes everything.

You understand the concepts faster when you actually apply them.

Here are 9 free AI learning resources worth exploring:

Anthropic → Building Effective Agents
https://t.co/UZphNg8rCE

Google → Machine Learning Crash Course
https://t.co/Mz7RCDHmih

Microsoft → AI for Beginners
https://t.co/efKA0RTUhP

Amazon Web Services (AWS) → Machine Learning Essentials
https://t.co/6o6SFit58w

Meta → AI Resources
https://t.co/cahkWcp8XB

Hugging Face → Transformers Course
https://t.co/bB3UnV8Bql

OpenAI → AI Resources
https://t.co/yGH4YgZFNM

https://t.co/JusdByoV7g → AI Engineering
https://t.co/uoVeSmWShl

NVIDIA → CUDA by Example
https://t.co/5ZLiPbj8eE

The best way to learn AI is not by waiting until you feel ready.

Learn one concept.
Build one project.
Improve as you go.
Start small.
Build consistently.
Share what you learn.

Which resource are you starting with first? 👇

♻️ Repost to give your network an unfair advantage.__

📌 If you want a high-res PDF of this guide:

1. Follow @coder_surya
2. Save the post.
3. Repost to your network.
4. Join My AI Community: https://t.co/ioQEJKhR1q

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#AI #ArtificialIntelligence #MachineLearning #GenerativeAI #ChatGPT #OpenAI #DeepLearning #AIEngineering #LearnAI #Tech #Programming #Developers #LLM #DataScience #AIAgents

[Kirk Borne @KirkDBorne]

🏆The Kaggle Book — Master Data Analysis and Data Science Competitions with Machine Learning, GenAI, and LLMs [2nd Ed.]: https://t.co/hrVstwy4zH v/ @PacktDataML

Table of Contents:
🔶Introducing Data Science Competition
🔷Organizing Data with Datasets
🔶Work & Learn with Kaggle Notebooks
🔷Kaggle Models
🔶Leveraging Discussion Forums
🔷Detailing Competition Tasks & Metrics
🔶Designing Good Validation Schemes
🔷Modeling for Tabular Competitions
🔶Hyperparameter Optimization
🔷Ensembling & Stacking Solutions
🔶Modeling Image Classification & Segmentation

My Review (on Amazon):
This 700-page masterpiece of writing covers everything you need—start to finish—to be a competitive coder, specifically for Kaggle data science competitions. The book covers the mechanics of the competitions (platform, resources, rankings, leaderboards), then the infrastructure (notebooks, GitHub, data sets, frameworks, discussion forums), and then nearly 500 pages devoted to "Elevating Your Game" (in-depth coverage of modeling techniques, evaluation metrics, validation strategies, hyperparameter optimization, ensembles, stacking, and various categories of competitions: tabular data, computer vision, NLP, Gen AI, simulations). The book concludes with a valuable section on building your Kaggle portfolio for career advancement and new opportunities. This is an outstanding data science / AI / Machine Learning training resource for anyone, even if you are not into the competitions, though especially if you are a dedicated Kaggler.

[Kirk Borne @KirkDBorne]

Principles of Deep Learning Theory—Theoretical & Mathematical Foundations
[471-page PDF Draft] https://t.co/1y5opTWW5d
-or-
Buy new edition: https://t.co/1PUSo1VwNG
———————
#DataScience #AI #MachineLearning #ML #NeuralNetworks #Algorithms #Mathematics #DataScientist

[灰狐 @huihoo]

Free Book《Foundations of Computation》256 pages CC License
Download the PDF ebook directly
https://t.co/2HH0FdoXNr
A Zip archive of the full source code
https://t.co/oyF51nz2uE

Foundations of Computation is a free textbook for a one-semester course in theoretical computer science. It has been used for several years in a course at Hobart and William Smith Colleges. The course has no prerequisites other than introductory computer programming. The first half of the course covers material on logic, sets, and functions that would often be taught in a course in discrete mathematics. The second part covers material on automata, formal languages, and grammar that would ordinarily be encountered in an upper level course in theoretical computer science.

Table of Contents:
Chapter 1: Logic and Proof
Chapter 2: Sets, Functions, and Relations
Chapter 3: Regular Expressions and FSA's
Chapter 4: Grammars
Chapter 5: Turing Machines and Computability

[Rushika Rai @rushu888]

Data Science Full Course ✅✅

link - https://t.co/JgWjPjpVlF

[UNAMdigital @UNAMdigital]

Participa en la conferencia gratuita previa a los talleres y conoce las posibilidades de la #IA en actividades profesionales y creativas.

Conferencia: 10 de junio, 18 h. Registro: https://t.co/DyZ4nJGrFb

Talleres: 15 al 19 de junio: https://t.co/E3Rr3OD2Re

@amciencias #DGTIC

[コムテ @commte]

Claude Code に質の高いPR書かせたい
Google Engineering Practices をスキルにリポジトリごと入れて、必要な部分だけ読ませてる

どのような基準でチェックされるか（レビ���アー視点）と、どうPRを小分けにして説明文を書くべきか（開発者視点）の技術基準がまとまってる

https://t.co/zLva7oTEjp

[Job Corner @JOBCORNER247]

Free Certification Courses to Learn Data Science in 2026:

1. Python
🔗 https://t.co/dIF8TuaOLu

2. SQL
🔗 https://t.co/Ge4bFjars9

3. Statistics and R
🔗 https://t.co/41ya4HlxPS

4. Data Science: R Basics
🔗https://t.co/56xixoYoMg

5. Excel and PowerBI
🔗 https://t.co/oWULVWbQPg

6. Data Science: Visualization
🔗https://t.co/eQ7buwP4QU

7. Data Science: Machine Learning
🔗https://t.co/RYTIF3cocp

8. R
🔗https://t.co/aPvThGhFHw

9. Tableau
🔗https://t.co/94KUoem3Bf

10. PowerBI
🔗 https://t.co/gOV6kIG2kQ

11. Data Science: Productivity Tools
🔗 https://t.co/0fvxcgIQ4W

12. Data Science: Probability
🔗https://t.co/J0eC5jlOGX

13. Mathematics
🔗https://t.co/b62getHyo7

14. Statistics
🔗 https://t.co/Y82ToFqsFs

15. Data Visualization
🔗https://t.co/Yz8e2T6mdn

16. Machine Learning
🔗 https://t.co/VoHcNGJvfC

17. Deep Learning
🔗 https://t.co/TY3cjGHXdK

18. Data Science: Linear Regression
🔗https://t.co/5N2UOWUgxw

19. Data Science: Wrangling
🔗https://t.co/zTsUAAn1hE

20. Linear Algebra
🔗 https://t.co/UPNbNapSf0

21. Probability
🔗 https://t.co/UdOPPPyYJ8

22. Introduction to Linear Models and Matrix Algebra
🔗https://t.co/3hOAvinvhe

23. Data Science: Capstone
🔗 https://t.co/ac8w6CLS9T

24. Data Analysis
🔗 https://t.co/tKh5FYjMRM

25. IBM Data Science Professional Certificate
https://t.co/gFsFS3B6zu

26. Neural Networks and Deep Learning
https://t.co/SPyU710NvG

27. Supervised Machine Learning: Regression and Classification
https://t.co/ATOsYimRAJ