Analyst Builder

Building the best platform for Data Analysts with Practice Technical Questions, Full Courses, Crash Courses, and more!

Founder & CEO

Alex Analytics LLC

Consulting focused on Product Design, Marketing Strategy, and Analytics for Startups and Tech Companies.

Founder

AmerisourceBergen

Manager of Operational Analytics and Intelligence

Data Analyst II

Junior Data Analyst

KPN Advisors

Data Analyst

The Family Place

Data Collection Specialist and Analyst

Helping people around the world get jobs in Analytics and Tech!

Check out my YouTube channel Alex The Analyst for free resources and Analyst Builder for my full learning platform.

Alex Freberg

I remember when I lost half my income overnight when I quit my job as an Analytics Manager to pursue Consulting and Entrepreneurship.

135k per year gone!

Looking back though it was the only logical option. Consulting was picking up, my YouTube channel was growing, and I felt this intense urge to build something bigger.

3 Years later it worked out better than I had hoped. We've grown Analyst Builder to over 140k users, I've consulted with some of the biggest tech companies in the world, and my YouTube channel has grown to over 1 million subscribers.

I've also worked more hours than I ever worked at a full-time job. I've had to grow in more areas and solve more problems than ever before. It's been twice as hard in almost every way - but also 10x more fun and exciting!

Founder of AnalystBuilder.com | YouTube "Alex The Analyst" | Not LinkedIn Top Voice

Databricks.

One of the best platforms in the world for Data Analytics and AI - and something you should learn.

This week we are starting our Databricks series to learn how to Work with Data, Build Dashboards, Work with AI, and then we are Building a Full Project at the end!

And with Databricks Free Edition we get to do all of it for free - can't beat that.

https://lnkd.in/epXAEc8i

Open video: Intro to Databricks (Free Edition) | UI Walkthrough on youtube.com

Intro to Databricks (Free Edition) | UI Walkthrough

When you work a Non-Profit you get access to all the tools in 1 day because they only use Excel.

When you work at a Fortune 500 Company it takes you 12 weeks to get access to everything because they use Excel, Python, SQL, Power BI, Tableau, Snowflake, Databricks, JupyterHub, AWS, Azure, GCP, ServiceNow, Jira, Confluence, Slack, Teams, Zoom, Outlook, Okta, VPN, GitHub, Bitbucket, SharePoint, OneDrive, Alteryx, SAP, Salesforce, Workday, Smartsheet, Trello, Notion, Miro, Looker, Asana, Monday, Power Automate, Power Apps, Cognos, Splunk, Airflow, Docker, Kubernetes, BigQuery, and Datadog.

When you work as a consultant you get access in a 1 day because your clients pay by the hour.

Over the past 6 years, I've had over 56 Million views on my YouTube channel.

Most of those views have come from my SQL Tutorials!

And shockingly, most of that has come after ChatGPT has become mainstream. The need for SQL is not going away. In fact, more and more teams are reaching out to me because they are just now starting to deploy systems that use SQL!

Don't think that SQL is a useless skill just because AI can write it. There's a lot of value from learning it and it's still my #1 recommended skill for Data Professionals to use!

It's hilarious when companies try to make it seem unethical to only be at work for the money.

What other reason would I be here?!?

Usually the loudest person in the meeting isn't the smartest

But people will still listen to them, fund their projects, give them more responsibility, and promote them faster.

Social skills and Communication make a huge difference in the workplace.

I would like to see a GenZ AI Maximalist try to convince Marcy from Accounting, who's been using Excel for 30 years, that she needs to use AI.

This would really highlight the world we currently are living in.

Not everyone has time to sit down at a computer and practice SQL and Python!

Some people need to do it on the go - or on their phone so their boss doesn’t know they’re trying to upskill and leave their job 😅

On AnalystBuilder.com you can practice SQL and Python on your phone!

You can take full courses or practice for your technical interviews anywhere!

I just turned 30... and I'm feeling old.

And in my old age I'm feeling quite nostalgic and I've been reminiscing quite a bit. In this video I wanted to share some things I've learned!

So strap in to hear what this old guy's advice on Life, Money, Career, and more.


https://lnkd.in/erK9B-QZ

Open video: I just Turned 30 - What I've learned + Advice for You on youtube.com

I just Turned 30 - What I've learned + Advice for You

Real Conversation between me and a big company.

Them: We love your YouTube channel, we love how you make your videos, and we'd like you to make one for us.

Me: That's great! Here's your video - hope you like it.

Them: We love it! Except change literally everything, add big zooms and backing track and cut out half your video and say these exact words.

Me: 😑

This is a weird story, but I used to love coffee.

I started working at AmerisourceBergen back in 2019 and they had free coffee in their breakroom. I would get it morning before my daily standup and I'd get a refill after.

I was hooked! 

I was a poor college kid just a year or so before that so anything free was like gold to me. The thing was - I overdid it a bit too much.

I started doing 3-4 cups per day. All my coworkers were always drinking coffee too. Then it got to the point I just started hating it. I hated going into the breakroom, I hated the smell of coffee. 

What I loved the most eventually became something I couldn't stand. Even to this day I almost never drink coffee. I'll have it maybe once a month on a cold morning or when I'm really tired, but other than that I hardly touch it.

No moral to the story or anything, just a random thing that happened and why I don't drink coffee anymore. Hope you're enjoying your coffee this Thursday!

Out of Office Reply Today: See you next week suckers, I’m going to look at fishes with my daughter on a class trip.

Today only you can get our new AI for Data Professionals course for only 1$!

Promo Link Here: https://lnkd.in/eMchGm4Y

This is a Crash Course focused on using AI as a Data Professional! You'll learn how to interact with AI - not just using ChatGPT, but also in a Code Editor and an API!

This deal will only last until Midnight 10/27 EST! After that it'll go up to it's full price of 14$.

AI is becoming a fundamental skill so I want to make sure everyone has the ability to get their hands on it.

I'll have an advanced course coming that dives into more advanced topics of AI!

Everyone who is a Lifetime Member or who has a Subscription will automatically have access to this course!

I personally think the Job Market is terrible, but let's hear what you all think!

In this video I'm trying something new - I'm listening and responding to peoples TikToks (because why not!)

Someone sent me Jess Ramos ⚡️video and it's featured in there too - She's not wrong!

https://lnkd.in/e_2CEAn3

Open video: Job Market in 2025: Over Saturated, Low-Paying, Helpless on youtube.com

Job Market in 2025: Over Saturated, Low-Paying, Helpless

Your coworker has a different analysis than you and you need to present to higher ups - what do you do?

This really happens! And it might be something that you get asked in an interview. 

See if you'd nail this interview question below!

Try it here: https://lnkd.in/ewv2myMB

The Tech Content Agency

Your groundbreaking tech is worthless

Harsh, but you need to hear it

The market is saturated with startups shouting "revolutionary"
 
Sure, you may have built the most cutting-edge tech ever seen

But that genius amounts to nothing if nobody knows you exist

Meanwhile, mediocre firms with inferior products are bathing in investor $ and media hype

It's not fair, it's a wake-up call

Technical wizardry and grinding alone won't captivate investors and customers

I ventured into LinkedIn to prospect clients for my Machine Learning agency

In just 1 year, I grew an engaged technical audience of 30k followers, generating 8.6 million impressions with no BS Data Science and Machine Learning content

Now, I help AI/ML founders turn their online presence into a compounding authority-building asset that amplifies credibility, opportunities, and impact—on autopilot

I've written content for: 
- Google ML Program Managers
- Microsoft Head of Engineering
- Airbnb and Google Data Scientists
- YouTube Senior Software Engineers, and others..

And founders backed by:
- YC
- A16Z
- Sequoia
- Techstars, and more..

As well as of equally awesome bootstrapped founders across:
- LLMs
- VectorDBs
- Data Analytics
- Data Engineering

If they could rise above the noise, so can you

I can help you with: 

✒️ No BS AI/ML Content  
⭐ LinkedIn Profile Revamp
🎯 Website Copy Optimization

Yeah, even if you're an introvert with a "boring" niche

My booking link: https://tally.so/r/wLW0oz (or shoot me a DM) 👉

Founder, AI/ML Copywriter & Brand Strategist 

Medium

● Grown Data Science Collective (#1 Medium Data Science Publication) from 0 to 830k+ followers

Founding Editor - Data Science Collective

Prediktiva

● Launched the first Data Science Agency in LATAM

Head of Data Science

Liftit

•  increases profitability and organic revenue by 57%
•  led strategic finance and analytics across 5 countries 
•  right hand to the CEO in raising a $14.3m Series A and $22.5m Series B

Head of Analytics

Suyo PBC

•  set up and directed finance function from scratch, including financial modelling, FP&A, budgeting & reporting, KPIs & OKRs tracking
•  developed and allocated operational budgets for the national expansion plan
•  supported the CEO during Seed-Round due-diligence and negotiations

Head Of Finance

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷30M+ Views 📝

Done-for you Audience Building & Content Writing for AI/ML Founders that frees up 100% of your time, so you can focus entirely on building your company  
  
Is your work too important to ignore? 💌 https://tally.so/r/wk69VJ  (or shoot me a DM) 👉
  
It's 2025 and we're at the peak of the attention economy  
  
Where Authenticity is king, visibility is currency and people wanna buy from people

Not faceless dehumanized brands  
  
So, they told you LinkedIn was "free marketing" you slapped some posts and called it strategy  
  
You've poured hours crafting the 'perfect' post, only to watch it sink without a trace..  
  
Then it dawned on you, your technical brilliance isn't enough to draw a crowd  
  
Hell, maybe you've even tried mimicking influencers, scrambling for any traction..  
  
Your post got 500 f*cking views... from your friends...  
  
Being ignored is soul-crushing  
  
It's not your fault tough  
  
When you started your company, you didn't sign-up for a degree in an Audience Building, Copywriting and LinkedIn strategy 

And here you are, sacrificing yet another late night for your vision  

"Am I just sabotaging my efforts?"  
  
"Is my life-work meaningless?"  
  
Getting traction feels impossible  
  
You hoped hiring some marketer would save your weekends but..  
  
Tech is a code they can't crack  
  
They can only churn out the lame, soul-sucking crap we all ignore on sight  
  
With armies of founders flooding feeds, your voice dissolves into meaningless white noise  
  
But in the process, you've wasted more of your precious time explaining 101 tech concept and rewriting low-quality drafts 

Done being ignored?  
  
Let's make the world listen 
  
I can help you with:  

✒️ No BS AI/ML Content  
⭐ LinkedIn Profile Revamp
🎯 Website Copy Optimization  

I've done it before:

↳ For founders backed by YC, a16z, Sequoia, and Techstars
↳ For bootstrappers in LLMs, ML, Vector DBs, Data Analytics, and Engineering
↳ For AI and Machine Learning leaders at Google, Microsoft, Airbnb, and YouTube

If they could rise above the noise, so can you

Yeah, even if you're an introvert with a "boring" niche

Still with me?  
  
Amazing—You're amazing!  
  
Cheers,  
Paolo

My booking link: 💌 https://tally.so/r/wLW0oz (or shoot me a DM) 👉

Paolo Perrone

"You're learning CUDA all wrong," the NVIDIA engineer said

Then he showed me their internal training path
"Wait, you DON'T start with code?"

Here's the exact 90-day roadmap they use👇

Phase 1️⃣ Intuition (Week 1-2)
Don't touch CUDA yet. Seriously
Build your mental model of the hardware and the why first

▶︎ UC Berkeley CS 61C, Lecture 17
This is the physics layer. Understand why GPU differs from a CPU
🔗 https://lnkd.in/gVi6Bsut

▶︎ Coursera Parallel Computing Course (First 3 modules only)
Learn  parallel algorithms and thinking
🔗 https://lnkd.in/g4FtxbE5

▶︎ Stanford CS231n Lecture 15 - Hardware/Software interface
See how frameworks like PyTorch use hardware for AI
🔗 https://lnkd.in/gzaR7xrZ

Phase 2️⃣ CUDA Basics (Week 3-4)
Now we code

▶︎ NVIDIA's official CUDA C++ Programming Guide (Chapters 1-5 only)
Learn threads, blocks, grids and kernel structure
🔗 https://lnkd.in/gsZsEqPp

▶︎ cuda-samples repo
Reading isn't enough. Compile, run, and modify official NVIDIA examples
🔗 https://lnkd.in/gGRgvm7G

```cuda
__global__ void vectorAdd(float *a, float *b, float *c) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    c[i] = a[i] + b[i];
}
```

If this doesn't make sense yet, you skipped Phase 1

Phase 3️⃣ Memory Mastery (Week 5-8)
Where 90% of developers fail, and where all performance hides

▶︎ Mark Harris's GTC Talk on Coalesced Memory Access
Single most important CUDA performance concept
Learn how threads must access global memory in aligned groups
🔗 https://lnkd.in/gz6Nbe5H

▶︎ GPU Gems 3, Chapter 39 - "Parallel Prefix Sum with CUDA"
Masterclass in shared memory to avoid bank conflicts, a fundamental optimization
🔗 https://lnkd.in/gNhZRCHE

▶︎  CUDA C++ Best Practices Guide - "Memory Optimizations" Chapter
Read to understand Global, Shared, Constant, Texture memory models
🔗 https://lnkd.in/grbhz7_V

Phase 4️⃣ Real Kernels (Week 9-12)
Stop playing with toy arrays. Build something that matters

• Implement softmax (harder than you think)
• Write a basic GEMM that doesn't suck
• Port one PyTorch operation to CUDA

Repos that ship:

▶︎ tiny-cuda-nn by NVIDIA
Goldmine of highly optimized, real-world kernels for NN
🔗 https://lnkd.in/gGbFzVsb

▶︎ FlashAttention
Reading this code teaches more on memory-aware kernel design than any book
🔗 https://lnkd.in/g6sMnBsC

▶︎ Triton Language Examples
Modern, Pythonic way to write efficient GPU code, simplifying raw CUDA boilerplate
🔗 github.com/openai/triton

⚡ NVIDIA engineers 6-month shortcut

Skip CUDA
Learn Triton first (handles 80% of use cases better)
Then return to CUDA when hitting limits

The difference between you and everyone else?

You have the map
90 days from now, you'll be shipping production kernels
Not stuck debugging tutorials

♻️ Repost to give someone the shortcut you wish you had

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷50M+ Views 📝

Everyone's paying $1,000 for AI agent courses.

Here's the complete roadmap for $0.

I've watched developers burn thousands on "AI Agent Masterclasses."
Meanwhile, everything you need is free on the internet.

11 courses. Zero cost. Production-ready skills.

Here's the exact path:

1️⃣ Start with the basics (don't skip this)
• Generative AI for Everyone - How AI actually creates things
• Getting Started with LLMs - The brains behind every agent
🔗 https://lnkd.in/gvC4kHmd
🔗 https://lnkd.in/gSAnj7Em

2️⃣ Master the art of talking to AI
• Prompt Engineering for VLMs - Turn "write email" into magic
• GPT Wrappers - Build real apps, not toy demos
🔗https://lnkd.in/gBSkP8pV
🔗 https://lnkd.in/gUAKHxXh

3️⃣ Make agents that remember
• Agentic RAG with LlamaIndex - Pull real data, not hallucinations
• Agent Memory - Turn goldfish AI into elephants
🔗 https://lnkd.in/gxMCeMvw
🔗 https://lnkd.in/gM8bjwcx

4️⃣ Build your first real agent
• AI Agents with RAG + LangChain - Your complete toolkit
• Agentic Design Patterns with AutoGen - Proven blueprints
🔗 https://lnkd.in/gSSyAQ59
🔗 https://lnkd.in/g3wg3XtU

5️⃣ Scale to agent teams
• Multi-Agent Systems with CrewAI - Orchestrate agent armies
• AI Agents in LangGraph - Production workflows that work
🔗 https://lnkd.in/gzM7XHFU
🔗 https://lnkd.in/gKtANHsV

6️⃣ Learn the language
• AI Python for Beginners - Customize everything
🔗 https://lnkd.in/giTwkAbB

The uncomfortable truth:

While you're watching YouTube gurus sell $997 courses,
someone following this roadmap is shipping real agents.

And billing real clients.

I spent $3K on courses before finding these.
Save your money. Build something instead.

Which course are you starting with today?

♻️ Repost to save someone from another overpriced "certification"

4 math concepts you need to understand LLMs in 10 minutes.

No PhD required.

I spent 6 months confused by AI papers.
Then I found this breakdown by Giles Thomas.
Everything clicked.

Here's the cheat sheet:

1️⃣ Vectors = Direction + Distance in Space

Think GPS coordinates, but in 50,000 dimensions.
Each position represents a different meaning.
"Cat" lives near "dog" in this space.
"Airplane" lives far away.

That's it. That's embeddings.

2️⃣ Matrices = Space Transformers

Remember rotating shapes in geometry class?
Same thing, but with meaning.

A matrix takes your 50,257-dimensional vocab
Projects it down to 768 dimensions
Then back up again.

It's compression without losing the important stuff.

3️⃣ Dot Product = Similarity Score

Multiply matching positions. Add them up.
Big number = similar vectors.
Small number = different vectors.

This is how LLMs know "king - man + woman = queen"

4️⃣ Neural Network Layers = Just Matrix Math

Remove the activation function and what's left?
Matrix multiplication.
A projection from one space to another.

Add activation back = now it can learn patterns.

The GPT-2 Example That Makes It Real:

Input: "The cat sat on the"
1. Convert each word to a one-hot vector (50,257 dimensions)
2. Project down to embeddings (768 dimensions)  
3. Let attention mechanism figure out relationships
4. Project back up to vocab space
5. Pick highest probability: "mat"

That's literally how ChatGPT works.
Just bigger.

Why This Matters:

Everyone's scared of AI math.
But it's high school algebra in a trench coat.

Once you see through the intimidation,
you can actually build stuff.

♻️ Repost to demystify AI for someone else

Stanford just dropped CS231N on YouTube.

18 lectures. Completely free.
The computer vision course that launched 1000 startups.

I just binged the first 5 lectures.
My brain hurts in the best way.

Here's what nobody tells you about CS231N:

This isn't just another deep learning course.
It's the one Andrej Karpathy taught.
The one that birthed Tesla's Autopilot team.

What's actually inside:

1️⃣ Lecture 1-4: The Foundations
• Why images are just numbers
• Linear classifiers that actually make sense
• Backprop explained without the PhD math
• Neural networks from first principles

2️⃣ Lecture 5-8: Where It Gets Real
• CNNs - why convolutions dominate vision
• Training tricks that save weeks of compute
• Architectures that actually ship to production
• Attention mechanisms before they were cool

3️⃣ Lecture 9-12: Modern Deep Learning
• Object detection that works at 60fps
• Segmentation for self-driving cars
• Video understanding (the frontier)
• Distributed training without the pain

4️⃣ Lecture 13-18: The Future Stuff
• Generative models (before DALL-E existed)
• Self-supervised learning secrets
• 3D vision for robotics
• Vision-language models
• Human-centered AI design

Fair warning:
This isn't a weekend binge.
It's 18 hours of dense content.
Plus 40+ hours if you do the assignments.

But it's the difference between using AI and understanding AI.

Which lecture are you starting with?

Mine was "CNNs for Visual Recognition."
Still implementing ideas from it 3 years later.

♻️ Repost to help someone level up from AI user to AI builder

Microsoft just dropped a free AI agents course.

And it's actually useful.

Here's what Microsoft actually covers:

1️⃣ What AI agents really are (hint: not chatbots)
2️⃣ Framework selection (spoiler: it doesn't matter)
3️⃣ Design patterns that prevent disasters
4️⃣ Tool use without token bankruptcy
5️⃣ Agentic RAG (when agents need memory)
6️⃣ Planning patterns (so they don't go rogue)
7️⃣ Multi-agent systems (when one isn't enough)
8️⃣ Improvement strategies (because v1 always sucks)
9️⃣ Production deployment (the part everyone skips)

No fluff. No "prompt engineering secrets."
Just engineering principles that work.

The uncomfortable truth:

While you're watching YouTube gurus sell $997 "masterclasses,"
Microsoft's giving away better content for free.

Course link in comments.

What's stopping you from building agents that actually ship?

♻️ Repost to help someone skip the expensive mistakes

Microsoft just dropped a bombshell: 
Analog optical computers that run AI without digital conversions. 

Here's why this changes everything about AI efficiency...

I was skeptical when I read the paper.

Then I saw the numbers:
• 1000x faster than digital processors
• 100x less energy consumption
• Zero conversion overhead

We've been doing AI backwards this whole time.

Think about it:
Light doesn't need to become 1s and 0s to compute.
Photons can multiply, add, and transform naturally.
No transistors. No heat. No bottlenecks.

The breakthrough: diffractive optical neural networks (DONNs).

Instead of converting light → digital → compute → light,
They just... compute with light directly.

Here's what's mind-blowing:

Traditional AI inference:

1. Convert input to digital
2. Process through silicon
3. Generate heat like crazy
4. Convert back to output
5. Repeat millions of times

Analog optical:
1. Light goes in
2. Light computes
3. Light comes out
4. That's it

The implications are staggering:

🔬 For edge computing: AI chips that don't need cooling
🔬 For data centers: 90% reduction in power costs
🔬 For mobile: Week-long battery life with always-on AI
🔬 For scale: Training models without melting glaciers

But here's the kicker:

This isn't theoretical.
Teams are already building optical transformers.
Running actual neural networks.
Getting real results.

The uncomfortable truth?

We spent decades optimizing the wrong thing.
Making digital chips faster instead of questioning if we need digital at all.

While we fought over CUDA cores,
Nature was showing us the answer:
Light doesn't need translation.

The future of AI it's no transistors.

Who else is ready for AI at the speed of light?

P.S. The best part? This technology already exists in your DVD player. We just never thought to run neural networks on it.

♻️ Repost to help someone see why their $50K GPU investment might become obsolete

Manual ML experimentation is dead.

I ran 147 experiments last night while watching Netflix. 

Zero code. Zero burnout. Here's how 👇

The old way of building ML models is broken.
You know the drill:

→ Spend 3 days on EDA
→ Test 12 architectures manually
→ Hyperparameter tune until 2 AM
→ Feature engineer based on "intuition"
→ Miss the deadline anyway

Neo just made that obsolete.
And I have beta access to share.

How Neo works:

📊 Autonomous EDA – Finds patterns you'd miss after staring at data for hours
🎯 Multi-step reasoning – Understands WHY models fail, not just that they failed
🤖 Comparative analysis – Tests XGBoost vs Neural Nets vs everything else
🧪 Parallel experimentation – Runs 100s of model variations simultaneously  
⚡ Feature engineering on steroids – Creates features you wouldn't think of
💬 Chat guidance – You steer, Neo executes at superhuman speed
🛠️ GPU sandbox included – No more "out of memory" nightmares

The kicker: Neo explained every decision.

"Tried polynomial features. Overfitted. Switching to target encoding."
"Random Forest plateaued at 0.87 R². Testing gradient boosting variants."

It's like having a senior ML engineer who never sleeps.

Help me get this to ML engineers who need it: 
♻️ Repost this 
💬 Comment "NEO"  
➕ Follow me 

I have 37 beta invites left.
Prioritizing people who do all 3.

David Kimai just open-sourced the Context Engineering handbook.

7.1K stars in 2 weeks.

Here's what's actually inside:

The Core Idea:
Prompt engineering = what you type
Context engineering = everything else the model sees

Most people optimize the prompt.
Smart people optimize the context.

What You Get:

📁 00_foundations/
Basic theory in plain English.
Why context beats prompts every time.
Token budgets that don't blow up.

📁 10_guides_zero_to_hero/
Start here. Literally.
Minimal examples that actually run.
No 500-line boilerplate.

📁 20_templates/
Copy-paste context patterns.
Memory systems. Tool integration. Control flow.
YAML configs you can steal.

📁 30_examples/
Full implementations:
- Chatbots with real memory
- Agents that don't hallucinate
- RAG that actually retrieves

📁 40_reference/
Deep dives on:
- Why attention windows matter
- Context pruning strategies
- Evaluation metrics that work

The Practical Stuff:

Every concept has:
✓ Runnable Python code
✓ ASCII diagrams
✓ Before/after metrics
✓ "Why this matters" sections

No slides. No theory dumps.
Just: problem → solution → code.

My Favorite Parts:

The biological metaphor:
atoms → molecules → cells → organs
(single prompts → few-shot → memory → multi-agent)

The token calculator:
Shows exactly why your context explodes.
And how to fix it.

The "cognitive tools" templates:
Prompt programs that make models think step-by-step.
No training required.

Who This Is For:

- Engineers tired of prompt tweaking
- Anyone hitting token limits
- Teams building production agents
- People who want stuff that works

The repo assumes you know Python.
Everything else is explained.

Start with 10_guides/01_min_prompt.py
Run it. Break it. Understand it.

Then steal whatever you need.

https://lnkd.in/gpWCq58e

What part of context engineering confuses you most?

♻️ Repost to help someone graduate from prompt engineering

The only AI/ML roadmap that actually matches reality.
Not what influencers sell. What gets you hired.
Based on 50+ real hiring decisions.

1️⃣ Python (Month 1-2)
What actually matters:
• NumPy arrays you can debug
• Pandas without the memory leaks
• Matplotlib that doesn't look terrible
• scikit-learn basics that work

Skip: Advanced decorators, async madness

2️⃣ Math Foundation (Yes, Really)
Master these:
• Linear algebra (just matrices & vectors)
• Calculus (gradients, that's it)
• Probability (Bayes theorem saves lives)
• Stats (p-values aren't everything)

Ignore: Topology, measure theory, abstract algebra

3️⃣ ML Fundamentals (Month 3)
Real skills:
• Supervised vs unsupervised (know when)
• Overfitting (how to spot it)
• Cross-validation that makes sense
• Metrics beyond accuracy

Not needed: Every algorithm ever invented

4️⃣ Deep Learning (Month 4-5)
Survival kit:
• Build ONE neural net from scratch
• Understand backprop (really understand)
• PyTorch basics (forget TensorFlow)
• Why your model converges to garbage

Skip: GANs, complex architectures (for now)

5️⃣ Transformers (Month 6)
Just know:
• Attention mechanism basics
• Hugging Face pipeline
• When NOT to fine-tune
• Why context windows matter

Truth: You'll use pre-trained 99% of the time

6️⃣ MLOps (Month 7 - The Part Everyone Skips)
Critical:
• Docker your models
• FastAPI endpoints that don't crash
• Version everything (data too)
• Monitor what actually matters

Forget: Complex orchestration (yet)

7️⃣ Pick ONE Specialization
The choices:
• NLP (where the jobs are)
• Computer Vision (harder than it looks)
• Tabular (unsexy but pays)
• RL (good luck in production)

Reality: Jack of all trades = master of none

8️⃣ Interview Prep (Month 8+)
What matters:
• LeetCode mediums (not hards)
• ML system design basics
• Explain algorithms simply
• "Tell me about a model that failed"

Never asked: Derive backprop mathematically

9️⃣ The Portfolio That Works
Must haves:
• One end-to-end project deployed
• Clear documentation (README matters)
• Actual users (even if it's 10)
• Cost analysis included

Skip: 20 half-finished notebooks

🚨 The uncomfortable truth:
Everyone's selling 600-hour courses.
The winners? Following 8-month sprints.

While you're reading another roadmap, someone just:
• Shipped their first model
• Failed in production  
• Fixed it
• Got hired

You don't need perfection.
You need momentum.

What's stopping you from starting today?

♻️ Repost to help someone escape tutorial hell and get that AI salary bump

AI agents won’t fix your data science workflow. Unless you give them the right environment.

This is how we deliver Data Science projects in 2025 in seven steps:

Start with a prompt, not a blank notebook.
Let the Zerve agent generate the initial workflow and code.
Review the plan and data previews in the full IDE.
Iterate with natural language; the agent stays context-aware.
Scale experiments from one run to thousands instantly.
Trust that every result is tracked and reproducible.
Deploy your work as a workflow, API, or app.

Get Zerve here: https://bit.ly/41i1A61

AI agents won't replace data scientists.
But a data scientist using an agentic environment like Zerve will.

Carmelo Iaria just dropped the CrewAI course that kills the "AI agents are toys" narrative.

While everyone's building chatbots that break in production,
he's teaching you to build $100K enterprise systems.

Not demos. Not prototypes.
Real multiagent applications that solve real problems.

Here's the brutal difference:

Most courses: "Build a customer support bot!"
This course: Pick ONE enterprise use case:
• Automated employee onboarding (saves HR 20 hours/week)
• Multiagent customer support (handles L1/L2 automatically)
• Sales enablement automation (qualifies leads 24/7)

Then you build it. Production-ready. End to end.

The secret weapon: AAMAD Framework
(AI-Assisted Multiagent Application Development)

I've watched developers burn weeks debugging agent hallucinations.
Carmelo open-sourced the framework that prevents that nightmare.

What you actually build:

1️⃣ Market research docs (what clients actually pay for)
2️⃣ Real frontend (not another Gradio demo)
3️⃣ CrewAI orchestration (agents that delegate, not just respond)
4️⃣ Human-in-the-loop systems (for when agents get confused)
5️⃣ Production monitoring (catch hallucinations before customers)
6️⃣ Deployment that survives real traffic

No lectures. No theory dumps.
Just "here's how I built this for Fortune 500s."

Weekly code reviews with Carmelo himself.
Like having a $300/hour consultant on speed dial.

The uncomfortable truth:

While you're watching "Build ChatGPT in 10 minutes" tutorials,
someone taking this course is shipping enterprise AI.

And billing accordingly.

Your capstone becomes your portfolio.
Your portfolio becomes your pipeline.

Stop building toys. Start generating invoices.

♻️ Repost if you're ready to graduate from chatbot tutorials

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Alex Freberg's LinkedIn Performance

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