## Browse Livestreams

- Regularization Techniques for Linear Regression
- Linear Regression Modeling
- Intro to Gradient Descent
- Intro to Linear Regression
- Covariance and Correlation (Bivariate EDA)
- Data Visualizations for EDA (univariate)
- Intro to Exploratory Data Analysis (EDA)
- Math for Data Science
- Classifying Penguins with Decision Trees
- Supervised Learning - Classification vs Regression
- What is Machine Learning?

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During this ML Workshop we will begin our exploration of machine learning through the lens of applied mathematics, primarily Statistics and Linear Algebra.

Tonight's discussion will focus on making sure that we have a good grasp of which math topics are important for different data professions. We'll discuss what the differences are between Data Analysts, Data Engineers, Data Scientists, Machine Learning Engineers, and Research Scientist roles, with a particular focus on how math preparation expectations differ between these roles.

We'll take an in-depth look at Data Science roles specifically, and think about which math topics are the most useful and prevalent across data science. Then, together as a group, we'll decide on some applied-math-heavy data science projects to build together in future workshop sessions.