Predict fraud with data visualization & predictive modeling!

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Python Artificial Intelligence?

Chapter 2: Python Basics

Lecture 1: Installing Python and PyCharm

Lecture 2: Got a Python problem or question?

Lecture 3: How to use PyCharm

Lecture 4: Introduction and Variables

Lecture 5: Multivalue Variables

Lecture 6: Control Flow

Lecture 7: Functions

Lecture 8: Classes and Wrapup

Lecture 9: Source Files

Chapter 3: TensorFlow Basics

Lecture 1: Installing TensorFlow

Lecture 2: Introduction and Setup

Lecture 3: FAQ: Help with TensorFlow Installation

Lecture 4: What is TensorFlow?

Lecture 5: Constant and Operation Nodes

Lecture 6: Placeholder Nodes

Lecture 7: Variable Nodes

Lecture 8: How to Create a Regression Model

Lecture 9: Building Linear Regression

Lecture 10: Source Files

Chapter 4: Fraud Detection (Credit Card)

Lecture 1: Introduction

Lecture 2: New Location to Download Dataset

Lecture 3: Project Overview

Lecture 4: Introducing a Dataset

Lecture 5: Building Training: Testing Datasets

Lecture 6: Eliminating Dataset Bias

Lecture 7: Building a Computational Graph

Lecture 8: Building Functions to Connect Graph

Lecture 9: Training the Model

Lecture 10: Testing the Model

Lecture 11: Source Files

Chapter 5: Bootcamp Peek! Machine Learning Neural Networks

Lecture 1: Introduction to Machine Learning Neural Networks

Lecture 2: Introduction to Machine Learning

Lecture 3: Introduction to Neutral Networks

Lecture 4: Introduction to Convolutions

Chapter 6: Explore the Keras API

Lecture 1: Introduction to the Keras API

Lecture 2: Introduction to TensorFlow and Keras

Lecture 3: Understanding Keras Syntax

Lecture 4: Introduction to Activation Functions

Chapter 7: Format Datasets and Examine CIFAR-10

Lecture 1: Introduction to Datasets and CIFAR-10

Lecture 2: Exploring CIFAR-10 Dataset

Lecture 3: Understanding Specific Data Points

Lecture 4: Formatting Input Images

Chapter 8: Build an Image Classifier Model

Lecture 1: Introduction to the Image Classifier Model

Lecture 2: Building the Model

Lecture 3: Compiling and Training the Model

Lecture 4: Gradient Descent and Optimizer

Chapter 9: Save and Load Trained Models

Lecture 1: Introduction to Saving and Loading

Lecture 2: Saving and Loading Model to H5

Lecture 3: Saving Model to Protobuf File

Lecture 4: Bonus Summary

Chapter 10: Bonus Sections Source Material

Lecture 1: Texts Assets: Understand Machine Learning Neural Networks

Lecture 2: Texts Assets: Explore the Keras API

Lecture 3: Asset Files: Format Datasets and Examine CIFAR-10

Lecture 4: Asset Files: Build the Image Classifier Model

Lecture 5: Asset Files: Save and Load Trained Models

Chapter 11: Resources

Lecture 1: Bonus Lecture: Get 155 courses!

Lecture 2: Please leave us a rating.