🔥 Matt Dancho (Business Science) 🔥 Profile picture
Aug 19 5 tweets 2 min read Read on X
STOP DOING CUSTOMER SEGMENTATION WITH MACHINE LEARNING.

Start using AI.

This is how: Image
ML is great for 1 thing: finding clusters.

That's only 33% of the problem.

The other 66% is identifying what those clusters mean (and figuring out how to market to them). Image
That's where AI comes in handy:

1. AI is great at summarizing large quantities of data
2. AI is excellent at making decisions from the summary

Problem: You need to make an AI Customer Segmentation Agent Image
SOLUTION: AI for Customer Segmentation Analysis Agents

On Wednesday, August 20th, I'm sharing one of my best AI Projects: Customer Segmentation Agent with AI

Register here to learn how to build AI customer segmentation agents (500 seats): learn.business-science.io/registration-a…Image
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

• • •

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

Keep Current with 🔥 Matt Dancho (Business Science) 🔥

🔥 Matt Dancho (Business Science) 🔥 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 18
🚨 Synthetic Data is the Future of AI

Introducing The Synthetic Data Vault (SDV).

This is what you need to know: Image
Synthetic Data is the Future of AI

Synthetic data keeps your data private.

SDV generates fake datasets that look REAL.

Here's how: Image
I built a 100k-row customer dataset in 4 lines:

Perfect for HIPAA-compliant Machine Learning & AI.

Google Colab Example: colab.research.google.com/drive/1L6i-JhJ…Image
Read 7 tweets
Aug 16
Correlation is the skill that has singlehandedly benefitted me the most in my career.

In 3 minutes I'll demolish your confusion (and share strengths and weaknesses you might be missing).

Let's go: Image
1. Correlation:

Correlation is a statistical measure that describes the extent to which two variables change together. It can indicate whether and how strongly pairs of variables are related. Image
2. Types of correlation:

Several types of correlation are used in statistics to measure the strength and direction of the relationship between variables. The three most common types are Pearson, Spearman Rank, and Kendall's Tau. We'll focus on Pearson since that is what I use 95% of the time.Image
Read 12 tweets
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 13
🚨 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 — including advanced PDF understanding — 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
Read 8 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!

:(