nayr📀d villa 🇪🇨🇰🇷

Meet me at Midnight

[CodeCrafters @CodeCrafters11]

ALL FREE CERTIFICATION RESOURCES

AWS
https://t.co/24S0lhk6Pb

CISSP
https://t.co/HWgcPPaTDG

CISA
https://t.co/OFeOfr8rnq

CISM
https://t.co/4Q4YnyHt33

Digital Marketing
https://t.co/XvrW8k9lBj

[Santi Torres @SantiTorAI]

Si tuviera que convertirme en ingeniero IA en 90 días, no empezaría con cursos.

Construiría proyectos a partir de estos 10 repos de GitHub :

[Ronin @DeRonin_]

How to become AI engineer in next 6 months:

By the end, you want to be able to:

- build LLM apps end-to-end
- use APIs from OpenAI / Anthropic / open-source stacks
- design prompts and context properly
- add tool calling and structured outputs
- deploy real projects

So, let’s discuss your roadmap month by month

Month 1: Get solid enough in coding and fundamentals

What to learn:

- Python really well
- Git + GitHub
- CLI / terminal basics
- JSON, APIs, HTTP, async basics
- basic SQL
- basic data handling with pandas
- virtual environments, package management, error handling
- FastAPI or Flask

Month 2: Master LLM app development

What to learn:

- prompting fundamentals
- system vs user instructions
- structured outputs / JSON schemas
- function/tool calling
- streaming responses
- conversation state
- cost / latency / token basics
- failure handling
- prompt injection awareness

Month 3: Learn RAG properly

What to learn:

- embeddings
- chunking
- vector databases
- metadata filtering
- reranking
- retrieval quality issues
- hallucination reduction
- citations and grounding

Month 4: Agents, tools, workflows, evals

- agent loops
- tool selection
- state management
- retries
- when NOT to use agents
- multi-step workflows
- evaluation harnesses
- task success metrics

Month 5: Deployment, product thinking, and reliability

What to learn:

- FastAPI production patterns
- Docker
- background jobs
- queues
- auth + API key security
- logging
- observability
- prompt/version management
- eval dashboards
- cost monitoring
- rate limits
- caching

Month 6: Specialize and become hireable

these knowledge and skills you gained can be applied in three directions

you need to choose one of them and focus on practice

although everything mentioned above is also best learned purely through practice

Direction 1: AI product engineer

Best if you want startup jobs fast

Focus on:

- LLM apps
- RAG
- agents
- deployment
- product UX

Direction 2: Applied ML / LLM engineer

Focus on:

- fine-tuning
- when to fine-tune vs prompt
- evaluation
- inference optimization
- open-source models
- training pipelines

Direction 3: AI automation engineer

Focus on:

- workflow orchestration
- business process automation
- multi-tool systems
- CRM, docs, email, support, ops use cases

This roadmap will help you go through a practical path, and the key is to study each of these points and then test them in real work

By month six, you will already have several built products or examples of completed tasks

And it will be much easier to get a job as an AI engineer

Save it so you don't lose it and can return to study later

[Jesse--The Data Guy @Mbadiwejesse]

Machine Learning Project Ideas

1️⃣ Beginner ML Projects
• Linear Regression (House Price Prediction)
• Student Performance Prediction
• Iris Flower Classification
• Movie Recommendation (Basic)
• Spam Email Classifier

2️⃣ Supervised Learning Projects
• Customer Churn Prediction
• Loan Approval Prediction
• Credit Risk Analysis
• Sales Forecasting Model
• Insurance Cost Prediction

3️⃣ Unsupervised Learning Projects
• Customer Segmentation (K-Means)
• Market Basket Analysis
• Anomaly Detection
• Document Clustering
• User Behavior Analysis

4️⃣ NLP (Text-Based ML) Projects
• Sentiment Analysis (Reviews/Tweets)
• Fake News Detection
• Resume Screening System
• Text Summarization
• Topic Modeling (LDA)

5️⃣ Computer Vision ML Projects
• Face Detection System
• Handwritten Digit Recognition
• Object Detection (YOLO basics)
• Image Classification (CNN)
• Emotion Detection from Images

6️⃣ Time Series ML Projects
• Stock Price Prediction
• Weather Forecasting
• Demand Forecasting
• Energy Consumption Prediction
• Website Traffic Prediction

7️⃣ Applied / Real-World ML Projects
• Recommendation Engine (Netflix-style)
• Fraud Detection System
• Medical Diagnosis Prediction
• Chatbot using ML
• Personalized Marketing System

8️⃣ Advanced / Portfolio Level ML Projects
• End-to-End ML Pipeline
• Model Deployment using Flask/FastAPI
• AutoML System
• Real-Time ML Prediction System
• ML Model Monitoring Drift Detection.

#DataAnalytics
#DataScience
#TheDataGuy

[Aman Kharwal @amankk_9]

4 LLM Projects that will upgrade your resume as an AI/ML Engineer.
https://t.co/qg3isAHJ8v

[Jordi Neil @JordiNeil]

Les dejo un pdf que es una joya para aprender ML, DL e IA.
Incluye material de Stanford + MIT.

Si lo abren y no entienden nada, van a su IA favorita y le piden que les explique lo que necesiten paso a paso.

Traten de no saltarse tanto la parte de matemáticas porque aunque es la más difícil, es muy interesante aprenderla para que así no sean alguien que simplemente usa funciones de sklearn y ya.

Les dejo el link abajo 👇

[Vaishnavi @_vmlops]

The only GitHub repo you'll ever need for Machine Learning projects

https://t.co/pv5Oq2i6mC

[Kamesh Turkar @KameshTurkar2]

Want to make $4,000+ per month leveling up your analytics game?

Move beyond Excel master Python and R for serious data work and USD-paying gigs.

I’m sharing a FREE guide inspired by “Advancing into Analytics”

Learn how to go from spreadsheet basics → analytics expert

Perfect for career growth, freelancing, and remote jobs
No coding experience needed!

To get your guide for free:

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Drop “𝗜𝗢” below and I’ll DM your FREE copy!

[Tom Dörr @tom_doerr]

Curated learning paths for AI engineering

[Ashish Kushwaha @ashishllm]

1) Learn Python and its frameworks like Fastapi.
2) Learn Linux basics
3) Practise DSA in Python
4) Write some python programs
5) Write some python automation scripts
6) Learn system design. Recommending @gkcs_ course.
7) Build small-small projects and record it using Loom.
8) Post it everywhere.
9) Learn in Public Build in Public should be the Mantra.

Add more...

[Michael Pyrcz🌻 @GeostatsGuy]

🚀 When I say I’m sharing my entire #DataAnalytics and #Geostatistics course with anyone eager to learn — I’m not joking! 😀📊

🎓 Here it is! Imagine, every lecture is paired with a free online e-book, plus hands-on, well-documented #Python workflows and interactive dashboards that make data and methods come alive! 🐍💻📈

🌍 @GeostatsGuy is here to guide you on your #DataScience journey — because the world’s biggest challenges are spatial, and mastering #Geostatistics is how we’ll tackle them together! 💪🌐

Let’s make an impact. 🔥I'm stoked!
#WhatStartsHere Open, accessible, actionable education is my raison d'être!

👉 Watch the full course here:
🎥 https://t.co/HGqqHDQKcu∀.

[Jean de Nyandwi @Jeande_d]

Reinforcement Learning of Large Language Models, Spring 2025(UCLA)

Great set of new lectures on reinforcement learning of LLMs. Covers a wide range of topics related to RLxLLMs such as basics/foundations, test-time compute, RLHF, and RL with verifiable rewards(RLVR).

[Nina @HeyNina101]

Stanford just did something wild. They put their entire graduate-level AI course on YouTube. No paywall, no signup. It’s the exact curriculum Stanford charges $7,570 for ❱❱❱❱ watch free now

[Jaydeep @_jaydeepkarale]

This is the Github repository I would start with if I was to start learning Python in 2025

'The ultimate Python study guide' is a curated repository which is
• has a collection of standalone modules which can be run in an IDE like PyCharm and in the browser like Replit

• Has carefully crafted comments which guide a reader through what the programs are doing step-by-step.

• Only builtin libraries are leveraged so that these concepts can be conveyed without the overhead of domain-specific concepts.

Link in image description

[Pamela Fox @pamelafox]

Our Python+AI series is over, but you can still watch the videos, download the slides, and try out the code. Get it all from https://t.co/qbhw0u9Lir

[Victor @victor_explore]

🔥FREE Google Collab notebooks to implement every Machine Learning concept. Link in comment.

[Sumanth @Sumanth_077]

Andrew Ng just dropped a new free course on Agentic AI!

This course teaches you how to build agentic systems that take action through iterative, multi-step workflows.

100% Free

[nayr📀d villa 🇪🇨🇰🇷 @nayrodv]

@BharukaShraddha Ai

[Tech Fusionist @techyoutbe]

AI/ML Engineer Roadmap (30 Days) 🔥🔥

Fundamentals -> Maths & Stats -> Programming -> Data Processing -> Machine Learning -> Deep Learning -> Specialized in NLP & CV -> Advanced ML -> MLOps -> Advanced ML with ML System Design & Architecture Pattern

I know it's hard to cover everything in 30 days but atleast we can start....make progress than what we are today!! 👍👍

[Rohit Ghumare @ghumare64]

@aaditsh @akshay_pachaar https://t.co/uNHSHdGOyp