Course content

Linear Regression Models: Introduction to Linear Regression

Course: 1 Hour, 19 Minutes

• Course Overview
• Statistical Tools and Regression
• Reasons to Use Regression
• Regression Loss: Least Square Error
• Capturing Variance in Regression
• Prediction Using Regression
• Introduction to Deep Learning
• The Architecture of Neural Networks
• Neurons: The Building Blocks of a Neural Network
• Linear Regression Using a Single Neuron
• Training a Neural Network
• Gradient Descent Optimization
• Exercise: Introduction to Linear Regression

Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras

Course: 42 Minutes

• Course Overview
• Loading a Dataset
• Splitting a Dataset for Training and Validation
• Keras Installation
• Training and Evaluating a Model
• Building a Sequential Model
• Training a Neural Network
• Evaluating a Neural Network
• Exercise: Building Simple Regression Models

Linear Regression Models: Multiple and Parsimonious Linear Regression

Course: 1 Hour, 11 Minutes

• Course Overview
• Understanding Multiple Regression
• Kitchen Sink Regression
• Training and Evaluating the Model
• Preparing Data for a Neural Network
• Building a Neural Network
• Evaluating the Neural Network
• Finding Correlations in a Dataset
• Introducing Parsimonious Regression
• Applying Parsimonious Regression with Scikit Learn
• Applying Parsimonious Regression with Keras
• Exercise: Multiple Linear Regression

Linear Regression Models: An Introduction to Logistic Regression

Course: 58 Minutes

• Course Overview
• Introducing Logistic Regression
• The Logistic Regression Curve
• Logistic Regression and Classification
• Logistic Regression vs. Linear Regression
• Logistic Regression in Keras
• Preparing Data for Logistic Regression
• Classification using a Logistic Regression Model
• Preparing Data for a Neural Network
• Building and Evaluating the Keras Classifier
• Exercise: An Introduction to Logistic Regression

Linear Regression Models: Simplifying Regression and Classification with Estimators

Course: 36 Minutes

• Course Overview
• Introducing Estimators
• Preparing Data for a Linear Regressor Estimator
• Training and Evaluating a Regressor Estimator
• Preparing Data for a Linear Classifier Estimator
• Training and Evaluating a Classifier Estimator
• Exercise: Using Tensor Flow Estimators