Joseph E. Gonzalez

Come to Berkeley

Come to Berkeley

Lecture 24.06 - Precision and Recall

Lecture 24.06 - Precision and Recall

Lecture 24.05 - Decision Rules

Lecture 24.05 - Decision Rules

Lecture 24.03 - Fitting Logisitc Regression in pytorch

Lecture 24.03 - Fitting Logisitc Regression in pytorch

Lecture 24.02 - Logistic Model and Function

Lecture 24.02 - Logistic Model and Function

Lecture 24.04 Fitting LR in SKLearn

Lecture 24.04 Fitting LR in SKLearn

Lecture 24.01 - Classification by Estimating Proportions

Lecture 24.01 - Classification by Estimating Proportions

Lecture 24.00 - Recap of Classification Part 1

Lecture 24.00 - Recap of Classification Part 1

Lecture 19.06 - Lasso in Scikit Learn

Lecture 19.06 - Lasso in Scikit Learn

Lecture 19.05 - Ridge Regression in Scikit Learn

Lecture 19.05 - Ridge Regression in Scikit Learn

Lecture 19.03 - Regularization Concepts

Lecture 19.03 - Regularization Concepts

Lecture 19.02 - Review of Cross Validation

Lecture 19.02 - Review of Cross Validation

Lecture 19.04 - Regularization Concepts in the Notebook

Lecture 19.04 - Regularization Concepts in the Notebook

Lecture 19.01 - Building The Basic Model (Recap from Lecture 18)

Lecture 19.01 - Building The Basic Model (Recap from Lecture 18)

Lecture 19.00 - Lecture Overview

Lecture 19.00 - Lecture Overview

Lecture 18.03 - Using SKLearn Pipelines

Lecture 18.03 - Using SKLearn Pipelines

Lecture 18.04 - Cross Validation

Lecture 18.04 - Cross Validation

Lecture 18.02 - Building and Testing a Simple Model

Lecture 18.02 - Building and Testing a Simple Model

Lecture 18.01 - Constructing a Train Test Split using SkLearn

Lecture 18.01 - Constructing a Train Test Split using SkLearn

Lecture 18.00 - The Train Test Split and Cross Validation

Lecture 18.00 - The Train Test Split and Cross Validation

Lecture 17.05 - Overfitting

Lecture 17.05 - Overfitting

Lecture 17.04 - RBF Features + Too Many Features

Lecture 17.04 - RBF Features + Too Many Features

Lecture 17.03 - Redundant Features

Lecture 17.03 - Redundant Features

Lecture 17.02 - Data and Model Setup

Lecture 17.02 - Data and Model Setup

Lecture 17.01 - Overview of the Pitfalls of Feature Engineering

Lecture 17.01 - Overview of the Pitfalls of Feature Engineering

Lecture 17.00 - Posting Lectures

Lecture 17.00 - Posting Lectures

Lecture 16.04 - Feature Engineering

Lecture 16.04 - Feature Engineering

Lecture 16.03 - Non-linear Features

Lecture 16.03 - Non-linear Features

Lecture 16.02 - Feature Functions

Lecture 16.02 - Feature Functions

Lecture 15.01 - Review of Modeling Recipe

Lecture 15.01 - Review of Modeling Recipe