# Machine Learning Workshops (Livestreams)

### Tuesdays at 8PM Eastern on the Temzee YouTube Channel

## Regularization Techniques for Linear Regression

### August 27, 2024

Learn about how overfitting and multicollinearity are problematic and how to think about them via the bias-variance tradeoff framework. Learn the intuition behind different distance measures (L1, L2, etc) and how Regularization using Ridge, Lasso and ElasticNet help reign in extreme model coefficients. Finally, combine regularization techniques with the log normalization of skewed variables to generate a final predictive model.

## Linear Regression Modeling

### August 20, 2024

In this livestream we dig into modeling best practices for Linear Regression including: cross validation, filling null values, one hot encoding, feature scaling and using Scikit-Learn pipelines.

## Intro to Gradient Descent

### August 15, 2024

We take a detour from Linear Regression to cover the Gradient Descent algorithm in the context of simple linear regression which helps us to visualize exactly how gradient descent finds the optimal model parameters.

## Intro to Linear Regression

### August 6, 2024

Get introduced to Simple Linear Regression. We'll start with a baseline model, learn about three important error metrics: MAE, MSE, and RMSE. And then we'll fit a regression using Scikit-Learn and talk about how linear regression calculates the parameters that minimize the sum of the squared residuals.

## Covariance and Correlation (Bivariate EDA)

### July 30, 2024

Build an increased mathematical intuition around the critical statistics concepts of: variance, standard deviation, covariance and correlation –with covariance and correlation being our first foray into bivariate EDA.

## Data Visualizations for EDA (univariate)

### July 23, 2024

Learn about appropriate univariate data visualizations for both categorical and continuous data including: Histograms, Density Plots, Box Plots, Bar Charts, Stacked Bar Charts, and Pie Charts.

## Intro to Exploratory Data Analysis (EDA)

### July 16, 2024

We lay the ground work for our journey through applied math for data science beginning with Exploratory Data Analysis with a focus on univariate EDA and data visualizations.

## Math for Data Science

### July 9, 2024

An overview of different job titles under the umbrella of "data professional" and an exploration of how much math preparation is expected for different roles.

## Classifying Penguins with Decision Trees

### July 2, 2024

A deep dive on the Decision Tree algorithm including: decision tree visualizations, gini impurity, how decision thresholds are calculated, and calculating the best threshold with Python.

## Supervised Learning - Classification vs Regression

### June 27, 2024

Provides a framework for approaching Machine Learning. Explores the categories of classification and regression algorithms. Basic data cleaning and beginning modeling using a baseline.

## What is Machine Learning?

### June 25, 2024

Covers: Categories of ML algorithms including Supervised and Unsupervised learning. Important vocab like: model, algorithm, tabular data, feature, target, generalizability. Fitting a Decision Tree model with Scikit-Learn.