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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
Jun 12
🚨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
Jun 11
Python is insane for time series.

Case in point: Pytimetk 📈 Image
PyTimetk’s Mission: To make time series analysis easier, faster, and more enjoyable in Python.

Pytimetk uses a Polars backend for massive speedups. Image
Pytimetk tutorials:

✅ Sales Analysis: business-science.github.io/pytimetk/tutor…Image
Read 10 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 5
A Python Library for Time Series using Hidden Markov Models.

Let me introduce you to hmmlearn. Image
1. Hidden Markov Models

A Hidden Markov Model (HMM) is a statistical model that describes a sequence of observable events where the underlying process generating those events is not directly visible, meaning there are "hidden states" that influence the observed data, but you can only see the results of those states, not the states themselvesImage
2. HMM for Time Series with hmmlearn

hmmlearn implements the Hidden Markov Models (HMMs).

We can use HMM for time series. Example: Using HMM to understand earthquakes.

Tutorial: hmmlearn.readthedocs.io/en/latest/auto…Image
Read 9 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 4
🚨NEW AI for Data Scientists Workshop

This is what's coming: Image
Generative AI is the future of Data Science.

Those who can build with LLMs and Python have unlimited career potential. Image
To help, I'm going to lift the curtains on one of my BEST AI projects that companies will pay you $$$ for.

Here's what I'll share in the training: Image
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 4
❌Move over PowerBI. There's a new AI analyst in town.

💡Introducing ThoughtSpot. Image
1. AI Analyst

ThoughtSpot’s Spotter is an AI analyst that uses generative AI to answer complex business questions in natural language, delivering visualizations and insights instantly.

It supports iterative querying (e.g., “What’s next?”) without predefined dashboards. Image
2. Self-Service Analytics

Unlike Tableau and Power BI, which rely on structured dashboards, ThoughtSpot emphasizes self-service analytics with a search-based interface, making it accessible to non-technical users.

Its AI-driven approach feels like “ChatGPT for data.” Image
Read 8 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jun 3
Top 7 most important statistical analysis concepts that have helped me as a Data Scientist.

This is a complete 7-step beginner ROADMAP for learning stats for data science. 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

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