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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
Jul 14
85% of data scientists do customer segmentation the WRONG WAY.

AI Agents fix this—here's how I made an AI that clusters customers & recommends marketing actions (and you can too). 🧵 Image
Traditional K-Means finds clusters, but that's just the start.

The real challenge?

Interpreting clusters for business value. Image
AI Agents summarize clusters, spot hidden patterns, and suggest personalized strategies. Image
Read 7 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 14
The 3 types of machine learning (that every data scientist should know).

In 3 minutes I'll eviscerate your confusion. Let's go: 🧵 Image
1. The 3 Fundamental Types of Machine Learning:

- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning.

Let's break them down:
2. Supervised Learning:

Supervised Learning maps a set of inputs (features) to an output (target). There are 2 types: Classification and Regression.
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 13
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
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 11
When I was first exposed to the Confusion Matrix, I was lost.

There was a HUGE mistake I was making with False Negatives that took me 5 years to fix.

I'll teach you in 5 minutes. Let's dive in. 🧵 Image
1. The Confusion Matrix

A confusion matrix is a tool often used in machine learning to visualize the performance of a classification model. It's a table that allows you to compare the model's predictions against the actual values.
2. Correct Predictions:

True Positives (TP): These are cases in which the model correctly predicts the positive class.

True Negatives (TN): These are cases in which the model correctly predicts the negative class.
Read 11 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 10
Understanding P-Values is essential for improving regression models.

In 2 minutes, I'll crush your confusion. Image
1. The p-value:

A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.
2. Null Hypothesis (H₀):

The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under H₀, the regressor does not affect the outcome.
Read 13 tweets
🔥 Matt Dancho (Business Science) 🔥 Profile picture
Jul 7
Bayes' Theorem is a fundamental concept in data science.

But it took me 2 years to understand its importance.

In 2 minutes, I'll share my best findings over the last 2 years exploring Bayesian Statistics. Let's go. Image
1. Background:

"An Essay towards solving a Problem in the Doctrine of Chances," was published in 1763, two years after Bayes' death. In this essay, Bayes addressed the problem of inverse probability, which is the basis of what is now known as Bayesian probability.
2. Bayes' Theorem:

Bayes' Theorem provides a mathematical formula to update the probability for a hypothesis as more evidence or information becomes available. It describes how to revise existing predictions or theories in light of new evidence, a process known as Bayesian inference.
Read 12 tweets

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