Frauddetectionpythontensorflow Course

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

Last updated 2022-01-10 | 4

- 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!

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."

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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!

Who this course is for:

  • 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.

Course content

11 sections • 58 lectures

What is Python Artificial Intelligence? Preview 05:00

Installing Python and PyCharm Preview 05:18

Got a Python problem or question? Preview 00:14

How to use PyCharm Preview 05:16

Introduction and Variables Preview 11:49

Multivalue Variables Preview 23:15

Control Flow Preview 29:08

Functions Preview 14:19

Classes and Wrapup Preview 40:52

Source Files Preview 00:01

Installing TensorFlow Preview 00:03

Introduction and Setup Preview 04:11

FAQ: Help with TensorFlow Installation Preview 00:10

What is TensorFlow? Preview 13:28

Constant and Operation Nodes Preview 14:26

Placeholder Nodes Preview 11:06

Variable Nodes Preview 08:22

How to Create a Regression Model Preview 05:48

Building Linear Regression Preview 24:18

Source Files Preview 00:01

Introduction Preview 05:14

New Location to Download Dataset Preview 00:09

Project Overview Preview 03:29

Introducing a Dataset Preview 08:50

Building Training: Testing Datasets Preview 15:16

Eliminating Dataset Bias Preview 08:43

Building a Computational Graph Preview 13:09

Building Functions to Connect Graph Preview 14:48

Training the Model Preview 14:04

Testing the Model Preview 20:50

Source Files Preview 00:01

Introduction to Machine Learning Neural Networks Preview 02:01

Introduction to Machine Learning Preview 11:23

Introduction to Neutral Networks Preview 10:23

Introduction to Convolutions Preview 14:10

Introduction to the Keras API Preview 01:49

Introduction to TensorFlow and Keras Preview 09:06

Understanding Keras Syntax Preview 19:13

Introduction to Activation Functions Preview 13:26

Introduction to Datasets and CIFAR-10 Preview 01:53

Exploring CIFAR-10 Dataset Preview 08:36

Understanding Specific Data Points Preview 17:43

Formatting Input Images Preview 12:04

Introduction to the Image Classifier Model Preview 02:23

Building the Model Preview 18:18

Compiling and Training the Model Preview 12:38

Gradient Descent and Optimizer Preview 14:50

Introduction to Saving and Loading Preview 02:08

Saving and Loading Model to H5 Preview 15:20

Saving Model to Protobuf File Preview 17:50

Bonus Summary Preview 05:40

Texts Assets: Understand Machine Learning Neural Networks Preview 00:01

Texts Assets: Explore the Keras API Preview 00:01

Asset Files: Format Datasets and Examine CIFAR-10 Preview 00:01

Asset Files: Build the Image Classifier Model Preview 00:01

Asset Files: Save and Load Trained Models Preview 00:01

Bonus Lecture: Get 155 courses! Preview 00:06

Please leave us a rating. Preview 00:03