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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
May 20, 2023 • 10 tweets • 5 min read • Read on X
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BIG ANNOUNCEMENT: I'm beyond excited to announce that in 5 days, I'm launching my brand new course- The #Python for Machine Learning & API's Course.

This course will transform your #career.

Here's what's inside... 🧵

#datascience #course Image
This launch marks the culmination of 2 years of research...

It covers The 6 Top #Python libraries for machine learning and production:
1. #Pycaret: Low-code machine learning Image
2. #ScikitLearn: The premier ML toolkit in Python Image
3. #H2O: Blazing speed + AutoML Image
4. #MLFlow: Easy model lifecycle management Image
5. #FastAPI: Incredibly fast + easy APIs in python Image
6. #Streamlit: Simplified data science web apps Image
Ready to learn more AND advance your career with Python?

Then you can't miss this event.

$400 in giveaways that everyone gets for attending live!
What's the next step?

Just join my course waitlist + live launch event here.

I'm super excited!! 😀

👉Register Here: learn.business-science.io/python-ml-apis… Image

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 Profile picture
Nov 1
🚨NEW Whitepaper on AI Agents by OpenAI

The maker of ChatGPT shares how it builds AI Agents.

Get the 34-page white paper here: Image
This Whitepaper covers:

1. Building, evaluating, and deploying AI agents
2. Architectures, tool integration, and scaling
3. Agent ops and evaluation frameworks

Get it here:

I have one more thing before you go.

If you want to become a generative AI data scientist in 2025 ($200,000 career), then I'd like to help:cdn.openai.com/business-guide…Image
🚨WANT TO BECOME A GENERATIVE AI DATA SCIENTIST IN 2025 ($200,000 career)?

Discover how I built an AI Customer Segmentation Agent with Python:

- Scikit Learn
- K-Means
- LangChain
- LangGraph
- OpenAI

👉Register here (500 seats): learn.business-science.io/ai-registerImage
Read 5 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 26
This is wild.

A new paper shows how you can predict real purchase intent without asking people.

~90% of human test–retest reliability.

Here's what's inside the 28 page paper: Image
1. Problem with direct Likert from LLMs:

When you ask LLMs to output 1–5 ratings directly, the distributions are too narrow/skewed and don’t look like human survey data, limiting usefulness for concept testing. Image
2. Proposed fix — Semantic Similarity Rating (SSR):

Have the LLM write a short free-text purchase-intent statement, then map that text onto a 5-point Likert score using embedding cosine similarity to predefined anchor sentences (i.e., semantic matching instead of raw numbers). Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 22
How to build AI agents:

A great cheat sheet (bookmark for later).

Here's how to use it: Image
1️⃣ System Prompt: Define your agent’s role, capabilities, and boundaries. This gives your agent the necessary context.

2️⃣ LLM (Large Language Model): Choose the engine. GPT-5, Claude, Mistral, or an open-source model — pick based on reasoning needs, latency, and cost.
3️⃣ Tools - Equip your agent with tools: API access, code interpreters, database queries, web search, etc. More tools = more utility. Max 20.

4️⃣ Orchestration: Use frameworks (like LangChain, AutoGen, CrewAI) to manage reasoning, task decomposition, and multi-agent collaboration.
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 20
Understanding P-Values is essential for improving regression models.

In 2 minutes, I'll crush your confusion. Image
1. The p-value:

A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.
2. Null Hypothesis (H₀):

The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under H₀, the regressor does not affect the outcome.
Read 15 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 20
Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go! Image
1. Statistical Distributions:

There are 100s of distributions to choose from when modeling data. Choices seem endless. Use this as a guide to simplify the choice. Image
2. Discrete Distributions:

Discrete distributions are used when the data can take on only specific, distinct values. These values are often integers, like the number of sales calls made or the number of customers that converted.
Read 13 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 18
Top 10 Python Libraries for Generative AI You Need to Master in 2025

(The tools behind document agents, intelligent assistants, and next-gen interfaces.)

Everything you need to know: 🧵 Image
1. LangChain

The backbone of intelligent LLM apps.

Build agents that:
✅ Reason
✅ Use tools
✅ Remember conversations
✅ Access APIs

If you're building anything with GPTs, LangChain is your starting point.

langchain.com
2. LangGraph

LangChain + DAGs = LangGraph.

It powers:
- Multi-agent workflows
- Conditional logic
- Real-time state management

If you're serious about production AI agents, this is a must.
langgraph.dev
Read 15 tweets

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