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.


Homepage

https://www.udemy.com/course/frauddetectionpythontensorflow/

Predict Fraud With Data Visualization & Predictive Modeling! Fast Download
Predict Fraud With Data Visualization & Predictive Modeling! Full Download