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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.

Regularization Techniques for Linear Regression

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.

Linear Regression Modeling - August 20, 2024 - Temzee Machine Learning Workshop

Linear Regression Modeling

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 Gradient Descent, August 15, 2024 - Temzee Machine Learning Workshop

Intro to Gradient Descent

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.

Intro to Linear Regression Livestream - Machine Learning Workshop

Intro to Linear Regression

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.

Understanding Covariance and Correlation for Data Science in Python

Covariance and Correlation (Bivariate EDA)

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.

Data Visualizations for Exploratory Data Analysis (EDA)

Data Visualizations for EDA (univariate)

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.

Intro to Exploratory Data Analysis - Temzee Livestream

Intro to Exploratory Data Analysis (EDA)

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.

Applied Math for Data Science - Temzee Livestream

Math for Data Science

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.

Decision Tree Deep Dive

Classifying Penguins with Decision Trees

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

Supervised Learning - Classification vs Regression

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.

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