πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Aug 13 β€’ 8 tweets β€’ 3 min read β€’ Read on X
🚨 BREAKING: IBM launches a free Python library that converts ANY document to data

Introducing Docling. Here's what you need to know: 🧡 Image
1. What is Docling?

Docling is a Python library that simplifies document processing, parsing diverse formats β€” including advanced PDF understanding β€” and providing seamless integrations with the gen AI ecosystem. Image
2. Document Conversion Architecture

For each document format, the document converter knows which format-specific backend to employ for parsing the document and which pipeline to use for orchestrating the execution, along with any relevant options. Image
3. PDF Conversion to Markdown

Here is an example of the DocLayNet paper from arXiv, converted into Markdown format by Docling. Image
4. Core Technology:

Docling includes:

- PDF Backends for parsing
- Layout Analysis Model
- Vision-Based Table Formatter
- OCR for Text Image
5. Every data analyst, data scientist, and data engineer needs to learn Generative AI

99% of them are overlooking AI. This is a massive opportunity for you.

I'd like to help.
On Wednesday, August 20th, I'm sharing one of my best AI/ML Projects for FREE:

How I built an AI Customer Segmentation Agent with Python:

Register here (1,821 registered): learn.business-science.io/ai-registerImage
That's a wrap! Over the next 24 days, I'm sharing the 24 concepts that helped me become an AI data scientist.

If you enjoyed this thread:

1. Follow me @mdancho84 for more of these
2. RT the tweet below to share this thread with your audience

β€’ β€’ β€’

Missing some Tweet in this thread? You can try to force a refresh
γ€€

Keep Current with πŸ”₯ Matt Dancho (Business Science) πŸ”₯

πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @mdancho84

Aug 15
These 7 statistical analysis concepts have helped me as an AI Data Scientist.

Let's go: 🧡 Image
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. Image
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. Image
Read 12 tweets
Aug 14
K-means is an essential algorithm for Data Science.

But it's confusing for beginners.

Let me demolish your confusion: Image
1. K-Means

K-means is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection. Image
2. Unsupervised:

K-means is an unsupervised algorithm used on data with no labels or predefined outcomes. The goal is not to predict a target output, but to explore the structure of the data by identifying patterns, clusters, or relationships within the dataset.
Read 13 tweets
Aug 14
The 10 types of clustering that all data scientists need to know.

Let's dive in: Image
1. K-Means Clustering:

This is a centroid-based algorithm, where the goal is to minimize the sum of distances between points and their respective cluster centroid. Image
2. Hierarchical Clustering:

This method creates a tree of clusters. It is subdivided into Agglomerative (bottom-up approach) and Divisive (top-down approach). Image
Read 14 tweets
Aug 12
Type 1 and Type 2 errors are confusing. In 3 minutes, I'll demolish your confusion. Let's dive in. 🧡 Image
1. Type 1 Error (False Positive):

This occurs when the pregnancy test tells Tom, the man, that he is pregnant. Obviously, Tom cannot be pregnant, so this result is a false alarm. In statistical terms, it's detecting an effect (in this case, pregnancy) when it actually doesn't exist.
2. Type 2 Error (False Negative):

This happens when Lisa, who is actually pregnant, takes the test, and it tells her that she's not pregnant. The test failed to detect the real condition of pregnancy. In statistical terms, it's failing to detect a real effect (pregnancy) that is there.
Read 12 tweets
Aug 9
Stop doing Customer Segmentation with plain vanilla Scikit Learn.

Add these 7 Python libraries to your RFM, clustering, and
customer segmentation projects: Image
1. Data preparation

- load data withβ€―pandas
- impute/mask withβ€―Feature-engine

Website: feature-engine.trainindata.com/en/latest/inde…Image
2. Feature creation:

- derive recency/frequency/monetary features
- Use rfm or Lifetimes

Github: github.com/sonwanesuresh9…Image
Read 9 tweets
Aug 9
RIP Data Scientists.

The Generative AI Data Scientist is NOW what companies want.

This is actually good news. Let me explain: Image
Companies are sitting on mountains of unstructured data.

PDF
Word docs
Meeting notes
Emails
Videos
Audio Transcripts

This is useful data. But it's unusable in its existing form. Image
The AI data scientist builds the systems to analyze information, gain business insights, and automates the process.

- Models the system
- Use AI to extract insights
- Drives predictive business insights Image
Read 6 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!

:(