Predict Fraud With Data Visualization & Predictive Modeling!

Create a credit card fraud detection model! Learn predictive modeling, logistic regression, and regression analysis.

What you’ll learn
Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram.
Learn TensorFlow and how to build models of linear regression
Make a Credit Card Fraud Detection Model in Python. Learn how to keep your data safe!
Requirements
Please download PyCharm Community Edition 2017.2.3.
Description
“There are not that many tutorials on PyCharm. In fact, hardly any. Because of this one, I got my first broad overview of not only PyCharm, but also TensorFlow. Bottom-line: It’s a great value for money.” ⭐ ⭐ ⭐ ⭐ ⭐ “Incredible course. Looking forward for more content like this. Thank you and good job.” – Joniel G.”Makes learning Python interesting and quick.”————————————————————————————————————Do you want to learn how to use Artificial Intelligence (AI) for automation? In this course, we cover coding in Python, working with TensorFlow, and analyzing credit card fraud. We interweave theory with practical examples so that you learn by doing.This course was funded by a wildly successful Kickstarter.AI is code that mimics certain tasks. You can use AI to predict trends like the stock market. Automating tasks has exploded in popularity since TensorFlow became available to the public (like you and me!) AI like TensorFlow is great for automated tasks including facial recognition. One farmer used the machine model to pick cucumbers! Join Mammoth Interactive in this course, where we blend theoretical knowledge with hands-on coding projects to teach you everything you need to know as a beginner to credit card fraud detection.Enroll today to join the Mammoth community!

Overview

Section 1: Introduction

Lecture 1 What is Python Artificial Intelligence?

Section 2: Python Basics

Lecture 2 Installing Python and PyCharm

Lecture 3 Got a Python problem or question?

Lecture 4 How to use PyCharm

Lecture 5 Introduction and Variables

Lecture 6 Multivalue Variables

Lecture 7 Control Flow

Lecture 8 Functions

Lecture 9 Classes and Wrapup

Lecture 10 Source Files

Section 3: TensorFlow Basics

Lecture 11 Installing TensorFlow

Lecture 12 Introduction and Setup

Lecture 13 FAQ: Help with TensorFlow Installation

Lecture 14 What is TensorFlow?

Lecture 15 Constant and Operation Nodes

Lecture 16 Placeholder Nodes

Lecture 17 Variable Nodes

Lecture 18 How to Create a Regression Model

Lecture 19 Building Linear Regression

Lecture 20 Source Files

Section 4: Fraud Detection (Credit Card)

Lecture 21 Introduction

Lecture 22 New Location to Download Dataset

Lecture 23 Project Overview

Lecture 24 Introducing a Dataset

Lecture 25 Building Training: Testing Datasets

Lecture 26 Eliminating Dataset Bias

Lecture 27 Building a Computational Graph

Lecture 28 Building Functions to Connect Graph

Lecture 29 Training the Model

Lecture 30 Testing the Model

Lecture 31 Source Files

Section 5: Bootcamp Peek! Machine Learning Neural Networks

Lecture 32 Introduction to Machine Learning Neural Networks

Lecture 33 Introduction to Machine Learning

Lecture 34 Introduction to Neutral Networks

Lecture 35 Introduction to Convolutions

Section 6: Explore the Keras API

Lecture 36 Introduction to the Keras API

Lecture 37 Introduction to TensorFlow and Keras

Lecture 38 Understanding Keras Syntax

Lecture 39 Introduction to Activation Functions

Section 7: Format Datasets and Examine CIFAR-10

Lecture 40 Introduction to Datasets and CIFAR-10

Lecture 41 Exploring CIFAR-10 Dataset

Lecture 42 Understanding Specific Data Points

Lecture 43 Formatting Input Images

Section 8: Build an Image Classifier Model

Lecture 44 Introduction to the Image Classifier Model

Lecture 45 Building the Model

Lecture 46 Compiling and Training the Model

Lecture 47 Gradient Descent and Optimizer

Section 9: Save and Load Trained Models

Lecture 48 Introduction to Saving and Loading

Lecture 49 Saving and Loading Model to H5

Lecture 50 Saving Model to Protobuf File

Lecture 51 Bonus Summary

Section 10: Bonus Sections Source Material

Lecture 52 Texts Assets: Understand Machine Learning Neural Networks

Lecture 53 Texts Assets: Explore the Keras API

Lecture 54 Asset Files: Format Datasets and Examine CIFAR-10

Lecture 55 Asset Files: Build the Image Classifier Model

Lecture 56 Asset Files: Save and Load Trained Models

Section 11: Resources

Lecture 57 Bonus Lecture: Get 155 courses!

Lecture 58 Please leave us a rating.

Beginners who want to learn to use Artificial Intelligence.,Prior coding experience is helpful. For an in-depth intro to Python, search for our Ultimate Python Beginner Course.,Topics involve intermediate math, so familiarity with university-level math is very helpful.