Muhammad Naveed

Ai / ML Engineer

[Muhammad Naveed @ml_engineer_]

building in public is uncomfortable.
especially when you’re competing against tools way bigger than yours.

but we’re still building WhyAnalyst — a free AI data analyst designed to help people analyze data faster without spending hours .
Here is the link : https://t.co/OhZ0B63n5f

[Muhammad Naveed @ml_engineer_]

@GoogleAIStudio whyanalyst

[Muhammad Naveed @ml_engineer_]

@juliusai It's already in whyanalyst

[Muhammad Naveed @ml_engineer_]

@Forbes suggested whyanalyst

[Muhammad Naveed @ml_engineer_]

@drewhahn @juliusai Why not try whyanalyst

[Muhammad Naveed @ml_engineer_]

@LoopandPixels It's me

[Muhammad Naveed @ml_engineer_]

@justbyte_ Communication

[Muhammad Naveed @ml_engineer_]

@Tech_girlll whyanalyst tool which works as like julius ai but why people still use julius ai ?

[Muhammad Naveed @ml_engineer_]

@Palakonweb Whyanalyst .ai

[Muhammad Naveed @ml_engineer_]

This CSV would normally take 2 hours in Excel.
WhyAnalyst AI found the issue in 45 seconds.

[Muhammad Naveed @ml_engineer_]

@vladsiu @X Try this https://t.co/ExbljfRIUT

[Muhammad Naveed @ml_engineer_]

@IamYashKapoor yes happy to connect

[Muhammad Naveed @ml_engineer_]

@mchulet me too

[Muhammad Naveed @ml_engineer_]

@Palak3312 What's your skill set?

[Muhammad Naveed @ml_engineer_]

@nalinrajput23 But focus is not free

[Muhammad Naveed @ml_engineer_]

@nalinrajput23 Congrats

[Muhammad Naveed @ml_engineer_]

@misschopra Happy to connect

[Brij Pandey @LearnWithBrij]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/YkuDFVmW9e

2. LLMs from Scratch: https://t.co/u3kSz5SGuJ

3. Agentic AI Overview (Stanford): https://t.co/W6rzVHGSgC

4. Building and Evaluating Agents: https://t.co/sEl8vVax3F

5. Building Effective Agents: https://t.co/c7fD4aWFYO

6. Building Agents with MCP: https://t.co/GlMdR6htgA

7. Building an Agent from Scratch: https://t.co/kUQ9jPuI0R

8. Philo Agents: https://t.co/8JHvqw0DKn

🗂️ Repos

1. GenAI Agents: https://t.co/cyHPvOAjlK

2. Microsoft's AI Agents for Beginners: https://t.co/zFJAN74JQe

3. Prompt Engineering Guide: https://t.co/liUshX2XsP

4. Hands-On Large Language Models: https://t.co/TXFhbiboZY

5. AI Agents for Beginners: https://t.co/zFJAN74JQe

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/lkXP6itwK0

8. Hands-On AI Engineering:https://t.co/zB8EEctE4Y

9. Awesome Generative AI Guide: https://t.co/lF7CuIQHRw

10. Designing Machine Learning Systems: https://t.co/XlYUZYOoVi

11. Machine Learning for Beginners from Microsoft: https://t.co/hF5UzZoMJB

12. LLM Course: https://t.co/4tLAwy8fOQ

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/0OEKVLgF34

2. Google's Agent Companion: https://t.co/r0Dxe4VvDO

3. Building Effective Agents by Anthropic: https://t.co/I0ZyuwiOS3.

4. Claude Code Best Agentic Coding practices: https://t.co/HIBC2TwwAP

5. OpenAI's Practical Guide to Building Agents: https://t.co/1I8n0wnjHQ

📚Books:

1. Understanding Deep Learning: https://t.co/XEzhyAcWbq

2. Building an LLM from Scratch: https://t.co/4sZmBnHPEg

3. The LLM Engineering Handbook: https://t.co/IkAYNFkVNI

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/KsFnET47hx

5. Building Applications with AI Agents - Michael Albada: https://t.co/lJhMLtsLql

6. AI Agents with MCP - Kyle Stratis: https://t.co/C2lhD8uTDL

7. AI Engineering: https://t.co/34EyUiIVMv

📜 Papers

1. ReAct: https://t.co/kfQ8tWysne

2. Generative Agents: https://t.co/wbfqXq8KZK.

3. Toolformer: https://t.co/OQ7m49YWls

4. Chain-of-Thought Prompting: https://t.co/XeNgLQdTIL.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/tUZyPEGhni

2. MCP with Anthropic: https://t.co/wx1DAIWis0

3. Building Vector Databases with Pinecone: https://t.co/8XsQzDstTB

4. Vector Databases from Embeddings to Apps: https://t.co/9n6DvZGTMN

5. Agent Memory: https://t.co/OxFAaM0fp7

Repost for your network ♻️

[adah @adahstwt]

Everyone wants to become an AI Engineer. 👾

But the roadmap is actually this:

math basics (linear algebra, probability)
↓
python
↓
data handling (numpy, pandas)
↓
data visualization
↓
machine learning fundamentals
↓
deep learning (neural networks, backprop)
↓
frameworks (pytorch / tensorflow)
↓
nlp basics
↓
computer vision basics
↓
llms (how they actually work)
↓
prompt engineering
↓
fine-tuning models
↓
vector databases (pinecone / faiss)
↓
embeddings & retrieval
↓
rag systems
↓
agents & tool usage
↓
model evaluation
↓
deployment (apis, fastapi)
↓
docker
↓
cloud (aws / gcp)
↓
scaling inference
↓
monitoring models

Now you’re “ready”🎉

but here’s the catch:
most people quit before even reaching halfway.

Be honest…
how far are you in this roadmap?