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Matt Dancho (Business Science)

@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

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

2. #ScikitLearn: The premier ML toolkit in Python

3. #H2O: Blazing speed + AutoML

4. #MLFlow: Easy model lifecycle management

5. #FastAPI: Incredibly fast + easy APIs in python

6. #Streamlit: Simplified data science web apps

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…

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More from @mdancho84

Matt Dancho (Business Science)

@mdancho84

Apr 22

Understanding regression models is essential in data science.

In 4 minutes, I'll demolish your confusion. Let's go:

1. The 6 Diagnostic Checks Every Data Scientist Should Run

Once you've built a regression model, your job isn't done. These 6 checks will tell you whether your model can actually be trusted.

2. Posterior Predictive Check

Ask yourself: do the model-predicted lines resemble the observed data line? If your model is a good fit, simulated data from it should look similar to your actual data. When they diverge wildly, your model is missing something important.

Read 9 tweets

Matt Dancho (Business Science)

@mdancho84

Apr 19

Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go!

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.

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 10 tweets

Matt Dancho (Business Science)

@mdancho84

Apr 18

RIP manual research workflows.

Someone just open-sourced what comes after Karpathy’s AutoResearch.

It’s called AutoResearchClaw.

And this thing is insane.

A few weeks ago, Karpathy showed where research was heading:

AI agents running the experiment loop.

That was already a big signal.

AutoResearchClaw takes it even further.

It doesn’t just help with research.

It tries to automate the entire scientific method end-to-end.

You give it a raw idea.

One CLI command.

Then it runs.

Not just “brainstorming.”

Not just “summarizing.”

Actually running the workflow.

Read 8 tweets

Matt Dancho (Business Science)

@mdancho84

Apr 15

These 7 statistical analysis concepts have helped me as an AI Data Scientist.

Let's go: 🧵

Step 1: Learn These Descriptive Statistics

Mean, median, mode, variance, standard deviation. Used to summarize data and spot variability. These are key for any data scientist to understand what’s in front of them in their data sets.

2. Learn Probability

Know your distributions (Normal, Binomial) & Bayes’ Theorem. The backbone of modeling and reasoning under uncertainty. Central Limit Theorem is a must too.

Read 9 tweets

Matt Dancho (Business Science)

@mdancho84

Apr 7

🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧵

1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines.

2. Supported Documents

MarkItDown supports:

- PDF
- PowerPoint
- Word
- Excel
- Images (EXIF metadata and OCR)
- Audio (EXIF metadata and speech transcription)
- HTML
- Text-based formats (CSV, JSON, XML)
- ZIP files (iterates over contents)
- Youtube URLs
- EPubs

Read 7 tweets

Matt Dancho (Business Science)

@mdancho84

Apr 2

RIP document extractors.

Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.

Here’s what it does: 🧵

What it does:

→ Extracts structured data from messy text
→ Grounds every field to the exact source location
→ Handles 100+ page docs
→ Generates interactive HTML for verification
→ Works with Gemini + local models

What it replaces:

→ Regex/fragile parsing
→ Custom NER pipelines
→ Expensive extraction APIs
→ Manual data entry

Read 8 tweets

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