The concept that helped me go from bad models to good models: Bias and Variance.

In 4 minutes, I'll share 4 years of experience in managing bias and variance in my machine learning models. Let's go. 🧵 1. Generalization:

Bias and variance control your models ability to generalize on new, unseen data, not just the data it was trained on. The goal in machine learning is to build models that generalize well. To do so, I manage bias and variance.

K-means is an essential algorithm for Data Science.

But it's confusing for beginners.

Let me demolish your confusion: 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.

Tableau is about to die.

Introducing PandasAI, a free alternative for fast Business Intelligence.

Let dive in: 1. PandasAI

PandaAI transforms your natural language questions into actionable insights — fast, smartly, and effortlessly.

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). 🧵 Traditional K-Means finds clusters, but that's just the start.

The real challenge?

Interpreting clusters for business value.

The 3 types of machine learning (that every data scientist should know).

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

- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning.

Let's break them down:

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: 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.

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. 🧵 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.

Understanding P-Values is essential for improving regression models.

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

A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.

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. 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.

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: 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.

Principal Component Analysis (PCA) is the gold standard in dimensionality reduction.

But PCA is hard to understand for beginners.

Let me destroy your confusion: 1. What is PCA?

PCA is a statistical technique used in data analysis, mainly for dimensionality reduction. It's beneficial when dealing with large datasets with many variables, and it helps simplify the data's complexity while retaining as much variability as possible.

🚨 Say goodbye to manual ETL

Cleaned a 100k-word PDF dataset in 3 lines of Python code: 1. What is DocETL?

DocETL is a system for LLM-powered data processing.

You can create LLM-powered data processing pipelines.

🚨 Synthetic Data is the Future of AI

Introducing The Synthetic Data Vault (SDV).

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

Synthetic data keeps your data private.

SDV generates fake datasets that look REAL.

Here's how:

Logistic Regression is the most important foundational algorithm in Classification Modeling.

In 2 minutes, I'll crush your confusion.

Let's dive in: 1. Logistic regression is a statistical method used for analyzing a dataset in which there are one or more independent variables that determine a binary outcome (in which there are only two possible outcomes). This is commonly called a binary classification problem.

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

Let's go: 🧵 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.

🚨BREAKING: New Python library for Bayesian Marketing Mix Modeling and Customer Lifetime Value

It's called PyMC Marketing.

This is what you need to know: 🧵 1. What is PyMC Marketing?

PyMC-Marketing is a state-of-the-art Bayesian modeling library that's designed for Marketing Mix Modeling (MMM) and Customer Lifetime Value (CLV) prediction.

Stop Prompting LLMs.
Start Programming LLMs.

Introducing DSPy by Stanford NLP.

This is why you need to learn it: 1. Why DSPy?

DSPy is the open-source framework for programming—rather than prompting—language models.

It allows you to iterate fast on building modular AI systems.

Understanding P-Values is essential for improving regression models.

In 2 minutes, I'll crush your confusion.

Let's go: 1. The p-value:

A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.

🚨 BREAKING: IBM launches a free Python library that converts ANY document to data

Introducing Docling. Here's what you need to know: 🧵 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.

Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go! 1. Statistical Distributions:

There are 100s of distributions to choose from when modeling data. Choices seem endless. Use this as a guide to simplify the choice.

K-means is one of the most powerful algorithms for data scientists.

But it's confusing for beginners. Let's fix that: 1. What is 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.