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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
Oct 13
🚨BREAKING: New Python library for agentic data processing and ETL with AI

Introducing DocETL.

Here's what you need to know: Image
1. What is DocETL?

It's a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks.

It offers:

- An interactive UI playground
- A Python package for running production pipelines Image
2. DocWrangler

DocWrangler helps you iteratively develop your pipeline:

- Experiment with different prompts and see results in real-time
- Build your pipeline step by step
- Export your finalized pipeline configuration for production use Image
Read 8 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 12
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 with pandas
- impute/mask with 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 8 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 11
🚨NEW: Python library for LLM Prompt Management

This is what it does: Image
The Python library is called Promptify.

It combines a prompter, LLMs, and pipeline to Solve NLP Problems with LLM's.

You can easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more with Promptify. Image
Don't understand what that means? Let's take an example:

This is an NLP Classification Task.

The prompt combines a model, prompter, and pipeline to perform a Medical classification of the patient's symptoms. Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 11
🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧵 Image
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. Image
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 Image
Read 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 8
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
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Oct 6
🚨Introducing Agent Development Kit (ADK) by Google

A simple framework from Google for building, evaluating, and deploying AI agents.

Here's what you need to know (a thread): 🧵 Image
1. What is ADK?

Agent Development Kit (ADK) is a framework from Google for building, evaluating, and deploying AI agents.

It is “model-agnostic” and “deployment-agnostic”: although it’s optimized to work well with Google’s models and infrastructure (like Gemini, Vertex AI), you can use it with other models and deploy it in different environments.Image
2. Key Features

- Agents are Structured
- Modular
- Tooling & integration
- Supports multi-agent system Image
Read 7 tweets

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