R Programming Library

A data science enthusiast, tweet and retweet about Data Science related stuffs: from basic to advanced level topics. |#Bigdata| #R |#Python l #DataScience l #ML

[R Programming Library @rp_library]

What programming language you prefer for DataScience projects?

[Python Programming @PythonPr]

Machine Learning Engineer Roadmap

[Ronald van Loon @Ronald_vanLoon]

#AI Tools for Literature Review
by @Faheem_uh

#ArtificialIntelligence #Innovation #EmergingTech #TechForGood

cc: @marcusborba @HaroldSinnott @karpathy

[Shalini Goyal @goyalshaliniuk]

AI agents are transforming how we work, create, and automate, from solopreneurs building tools in a weekend to enterprises orchestrating complex workflows.

Here’s a directory that organizes the AI Agent landscape into 13 categories, helping you discover exactly what’s possible and where to start.

1. No-Code Business Workflow Agents
Automate tasks like lead capture, email replies, and reporting - no coding needed.

2. Low-Code Internal Tool Agents
Build custom tools like dashboards or approval flows with minimal code effort.

3. API & Integration-Focused Agents
Connect your tools, databases, and apps to create seamless, automated systems.

4. Solopreneur & Creator Agents
Handle content, client tasks, and scaling, perfect for freelancers and creators.

5. Open Source Agent Frameworks
Total freedom to build and customize agents from scratch with open protocols.

6. Data & Knowledge Agents
Extract insights from PDFs, docs, or databases using AI-powered search and summarization.

7. Autonomous & Reasoning Agents
Agents that plan, reason, and act independently across multi-step workflows.

8. Enterprise Automation Agents
Built for large teams—integrate with existing tools while maintaining scale and compliance.

9. Multimodal & UX-Aware Agents
Understand text, visuals, and voice to improve app context and user experience.

10. E-Commerce & Retail Agents
Personalize shopping, boost conversions, and automate store operations.

11. Customer Support & Sales Chat Agents
Handle queries, qualify leads, and follow up - 24/7, no human bottleneck.

12. Voice-Based Agents
Enable voice-driven commands for smarter, hands-free user experiences.

AI Agents are no longer just hype. They’re tools that solve real problems at scale.

[Ronald van Loon @Ronald_vanLoon]

#ML and #GenAI for managers and decision-makers
by @enricomolinari

#ArtificialIntelligence #MachineLearning #ML

cc: @gp_pulipaka @iainljbrown @ogrisel

[Marko Denic @denicmarko]

SQL cheat sheet - Every JOIN explained:

[CJ @CjRandhawaX]

This CV has helped many people secure interview calls from leading organizations such as Facebook, OpenAI, Google, Amazon, Microsoft, Netflix, Apple, and others.

I'm sharing the exact ATS-editable templates with you.

Repost, like, and reply "CV" to get it for free.

[Must be following me] so I can DM you.

[Only for the first 500 people]

[Manish Kumar Shah @manishkumar_dev]

Free Certification Courses to Learn Artificial Intelligence in 2025:

1. Introduction to Generative AI
https://t.co/MpjHSmzcdE

2. Python
https://t.co/gTbLYAFn8B

3. Statistics and R
https://t.co/G4BpzsXId3

4. Data Science: Machine Learning
https://t.co/tvdobtN1qM

5. Mathematics & Statistics
https://t.co/UkJwpbSnk8

6. PowerBI
https://t.co/xtFHxf7sNB

7. Machine learning and AI
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8. Big Data Analytics Course
https://t.co/KYAgFiV67E

9. Machine Learning
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10. Neural Networks and Deep Learning
https://t.co/oZCiqT7pJZ

11. Generative AI for Everyone
https://t.co/LpNO5qnFl4

12. Generative AI with Large Language Models
https://t.co/wdb55vAdEf

13. AI For Everyone
https://t.co/dQOjiOz10e

14. Harvard AI Intro
https://t.co/pxEuO6YH5S

15. Machine Learning Crash Course
https://t.co/ONUpLTZQeA

16. Prompt Engineering for ChatGPT
https://t.co/nx6EOzYVSy

17. CS50's Introduction to Artificial Intelligence with Python
https://t.co/pxEuO6YH5S

18. Introduction to Machine Learning
https://t.co/y7D0GtjWN8

19. LangChain LLMs:
https://t.co/mt9w1WsCVg

20. Data Science: Machine Learning
https://t.co/Ui81tN81nj

21. Deep Learning Specialization
https://t.co/aurTSW5AbZ

22. IBM AI Engineering Professional Certificate
https://t.co/LmBt8YAxi7

23. Python for Data Science, AI & Development
https://t.co/gTbLYAFn8B

24. IBM Applied AI Professional Certificate
https://t.co/vVqmQ6VNCo

25. Introduction to Artificial Intelligence (AI)
https://t.co/zJBeTNByHT

Happy Learning 🌟

Follow @manishkumar_dev for more such content.

[tut_ml @tut_ml]

Best SQL Courses- https://t.co/IZUPUpVdo8

@KirkDBorne
@antgrasso
@ronald_vanloon
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #GPT5 #AI #GPT4 #gpt24 #NeuralNetworks #SQL

[RAVI KUMAR SAHU @RAVIKUMARSAHU78]

40 websites where you can find remote jobs and earn in USD in 2024:

[Shruti @heyshrutimishra]

Apple finally launched iOS 18

Here are 11 mind-blowing features you cannot afford to miss:

#5 is insane!

[James @heyoliverjane]

6 visual summaries of the best books in the world:

1. Atomic Habits

[Inferam @InferamAI]

Free Vs Paid

[Kirk Borne @KirkDBorne]

Popular #MachineLearning #Algorithms Explained: https://t.co/O8KSh3qonG via @Python_Dv
—————
#bigdata #algorithms #Analytics #datascience #deeplearning #ai #artificialintelligence #python #DataScientists

[Data Science Dojo @DataScienceDojo]

💥 This blog lists down-trending data science, analytics, and engineering GitHub repositories that can help you with learning data science to build your own portfolio: https://t.co/edoHNEgNrg

#github #datascience #dataanalytics #dataengineering

[Data Science Dojo @DataScienceDojo]

🧠 An overview to SQL query execution order!

#sql #programming #datascience

[Matt Dancho (Business Science) @mdancho84]

For years, I was hyperparameter tuning XGBoost models wrong. In 3 minutes, I'll share one secret that took me 3 years to figure out. When I did, it cut my training time 10X. Let's dive in.

1. XGBoost: XGBoost (eXtreme Gradient Boosting) is a popular machine learning algorithm, especially for structured (tabular) data. It's claim to fame is winning tons of Kaggle Competitions. But more importantly, it's fast, accurate, and easy to use. But it's also easy to screw it up.

2. Hyperparameter Tuning: To stabilize your XGBoost models, you need to perform hyperparameter tuning. Otherwise XGBoost can overfit your data causing predictions to be horribly wrong on out of sample data.

3. My 3-Year "Beginner" Mistake: XGBoost has tons of parameters. The mistake I was making was treating all of the parameters equally. This caused me hours of tuning my models. And my results weren't half as good until I started doing this.

4. How I improved my hyperparameter tuning: XGBoost has one parameter that rules them all. And after 3 years, I noticed that model stability was 80% driven by this parameter. What was it? Learning rate. When I figured this out that's when things started to change. My models got better. My training times were reduced. Win win.

5. My Simple 2 Step Hyperparameter Tuning Method for XGBoost: What I was doing wrong was doing random grid search over all of the parameters. This took hours. So I made a key change. I began isolating Learning Rate, tuning it first. This was Step 1. The search space for one parameter is super fast to tune.

6. What about the other parameters? Once learning rate was tuned, I then opened the search space to more parameters. This is Step 2. The rest of the parameters have maybe 20% contribution to performance, so that means I can reduce the search space dramatically.

7. The big benefit: Separating tuning into 2 steps cut my training times by a factor of 10X. And my models actually became better. Faster training, better models. Win win.

Good luck!

===

Ready to learn Data Science for Business?

I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://t.co/LR39RJ5XKB

And if you'd like to speed it up, I have a live workshop where I'll share how to use ChatGPT for Data Science: https://t.co/EaMpKrJiqX

If you like this post, please reshare ♻️ it so others can get value.

[R Programming Library @rp_library]

Useful AI contents:

[Poonam Soni @CodeByPoonam]

Netflix Offers Salary upto $900,000 for AI-Focused Role

Google, Microsoft and others are offering FREE online courses to Learn AI

Here's a list of FREE AI courses:

[Kirk Borne @KirkDBorne]

Microsoft offers FREE courses on these topics:
✨ Artificial Intelligence #AI
✨ Internet of Things #IoT
✨ #DataScience
✨ #MachineLearning

The follow a project-based approach, allowing you to learn while actively building and creating!
✅ AI for beginners: A 12-week, 24-lesson curriculum all about Artificial Intelligence.
https://t.co/jMczjWzi8c
✅ IoT: Learn by doing a project that covers the journey of food from farm to table. This includes farming, logistics, manufacturing, retail, and consumer - all popular industry areas for IoT devices.
https://t.co/EAbbIuuJC4
✅ Machine Learning: A great course on classical machine learning, using Scikit-learn!
https://t.co/HnkdaERjbs
✅ Data Science: Covers #DeepLearning & Data Science in more detail!
https://t.co/hZP5aLIQYG

[Data Science Dojo @DataScienceDojo]

📂 Cheat sheets for data science and machine learning: https://t.co/j7uPdIPDaM

#DataScience #MachineLearning #CheatSheet