Predict fraud with data visualization predictive modeling!

Predict fraud with data visualization & predictive modeling!, available at $49.99, has an average rating of 3.9, with 58 lectures, based on 130 reviews, and has 1387 subscribers.

You will learn about 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! This course is ideal for individuals who are Beginners who want to learn to use Artificial Intelligence. or Prior coding experience is helpful. For an in-depth intro to Python, search for our Ultimate Python Beginner Course. or Topics involve intermediate math, so familiarity with university-level math is very helpful. It is particularly useful for Beginners who want to learn to use Artificial Intelligence. or Prior coding experience is helpful. For an in-depth intro to Python, search for our Ultimate Python Beginner Course. or Topics involve intermediate math, so familiarity with university-level math is very helpful.

Enroll now: Predict fraud with data visualization & predictive modeling!

Summary

Title: Predict fraud with data visualization & predictive modeling!

Price: $49.99

Average Rating: 3.9

Number of Lectures: 58

Number of Published Lectures: 58

Number of Curriculum Items: 58

Number of Published Curriculum Objects: 58

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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!

Who Should Attend

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.

Target Audiences

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.

“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!

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.

Instructors

Mammoth Interactive
Top-Rated Instructor, 3.3 Million+ Students

John Bura
Best Selling Instructor Web/App/Game Developer 1Mil Students

Rating Distribution

1 stars: 6 votes

2 stars: 12 votes

3 stars: 19 votes

4 stars: 43 votes

5 stars: 50 votes

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