Deep Learning Machine Learning Masterclass w TensorFlowJS

Deep Learning & Machine Learning Masterclass w/ TensorFlowJS, available at $54.99, has an average rating of 4.5, with 98 lectures, based on 4 reviews, and has 135 subscribers.

You will learn about Get acquainted with machine learning and deep learning capabilities using JavaScript Understand the JavaScript Machine Learning ecosystem Know how to decide, analyze, and make predictions from real-world data Build deep learning models with TensorFlow .js and practice on realistic datasets This course is ideal for individuals who are Developers transferring from other languages or JavaScript developers interested in Machine Learning and Deep learning or Data analysts and data scientists who want to explore the possibilities of Machine Learning and Deep Learning using JavaScript It is particularly useful for Developers transferring from other languages or JavaScript developers interested in Machine Learning and Deep learning or Data analysts and data scientists who want to explore the possibilities of Machine Learning and Deep Learning using JavaScript.

Enroll now: Deep Learning & Machine Learning Masterclass w/ TensorFlowJS

Summary

Title: Deep Learning & Machine Learning Masterclass w/ TensorFlowJS

Price: $54.99

Average Rating: 4.5

Number of Lectures: 98

Number of Published Lectures: 98

Number of Curriculum Items: 98

Number of Published Curriculum Objects: 98

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Get acquainted with machine learning and deep learning capabilities using JavaScript

Understand the JavaScript Machine Learning ecosystem

Know how to decide, analyze, and make predictions from real-world data

Build deep learning models with TensorFlow .js and practice on realistic datasets

Who Should Attend

Developers transferring from other languages

JavaScript developers interested in Machine Learning and Deep learning

Data analysts and data scientists who want to explore the possibilities of Machine Learning and Deep Learning using JavaScript

Target Audiences

Developers transferring from other languages

JavaScript developers interested in Machine Learning and Deep learning

Data analysts and data scientists who want to explore the possibilities of Machine Learning and Deep Learning using JavaScript

Machine learning and Deep Learning have been gaining immense traction lately, but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser-based JavaScript library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a JavaScript developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.

Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using JavaScript and the TensorFlow.js library.

This course is project-based so you will not be learning a bunch of useless coding practices. At the end of this course, you will have real-world apps to use in your portfolio. We feel that project-based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately.

You can use the projects you build in this course to add to your LinkedIn profile. Give your portfolio fuel to take your career to the next level.

Learning how to code is a great way to jump into a new career or enhance your current career. Coding is the new math and learning how to code will propel you forward in any situation. Learn it today and get a head start for tomorrow. People who can master technology will rule the future.

Course Curriculum

Chapter 1: 00a Mammoth Interactive Courses Introduction

Lecture 1: 00 About Mammoth Interactive

Lecture 2: 01 How To Learn Online Effectively

Lecture 3: Source Files

Chapter 2: 00b (Prerequisite) Introduction to HTML

Lecture 1: 01. Course Requirements

Lecture 2: 02. What Is JSbin

Lecture 3: 03. Setting Up The HTML Document

Lecture 4: 04. Header Tags And Paragraphs Tags

Lecture 5: 05. Styles

Lecture 6: 06. Bold Underline And Italic Tags

Lecture 7: 07. Adding In A Link

Lecture 8: 08. Adding In A Image

Lecture 9: 09. Adding A Link To An Image

Lecture 10: 10. Lists

Lecture 11: 11. Tables

Lecture 12: 12. Different Kinds Of Input

Lecture 13: 13. Adding In A Submit Button

Lecture 14: 14. Scripts And Style Tags

Chapter 3: 01b (Prerequisite) Introduction to CSS

Lecture 1: 01. Course Requirements

Lecture 2: 02. HTML Styles Crash Course

Lecture 3: 03. Adding Code To The CSS

Lecture 4: 04. Adding In IDs To The CSS

Lecture 5: 05. Classes In CSS

Lecture 6: 06. Font Families

Lecture 7: 07. Colors In CSS

Lecture 8: 08. Padding In CSS

Lecture 9: 09. Text Align And Transforms

Lecture 10: 10. Margins And Width

Lecture 11: 11. Changing The Body

Lecture 12: 12. Latin Text Generator

Lecture 13: 13. Adding In A Horizontal Menu With HTML And CSS

Lecture 14: 14. Adding A Background Image

Lecture 15: 15. Playing Around With Margins In CSS

Chapter 4: 01a (Prerequisite) Introduction to Javascript

Lecture 1: 01. Course Requirements

Lecture 2: 02. Html, CSS And Javascript Crash Course

Lecture 3: 03. Adding In Functions

Lecture 4: 04. Scaling Functions

Lecture 5: 05. Changing The Text In Javascript

Lecture 6: 06. Variables

Lecture 7: 07. Arrays

Lecture 8: 08. Objects

Lecture 9: 09. Variable Scope

Lecture 10: 10. Adding User Input Text

Lecture 11: 11. Calling Functions

Lecture 12: 12. If Statements

Lecture 13: 13. Else If And Else Statements

Lecture 14: 14. Changing The Style With Javascript

Chapter 5: 01c TensorFlow JS Fundamentals

Lecture 1: 01 What Is Machine Learning

Lecture 2: 02 What Is Tensorflow JS

Lecture 3: 03 Load Tensorflow Object

Lecture 4: Source Files

Chapter 6: 01d Build Your First Tensors

Lecture 1: 00 Linear Algebra For Machine Learning

Lecture 2: 01 Build Tensors

Lecture 3: 02 Tensor Utility Methods

Lecture 4: 03 Perform Math With Tensors

Lecture 5: Source Files

Chapter 7: 01e What is a Neural Network

Lecture 1: 00A What Is Deep Learning

Lecture 2: 00B What Is A Neural Network

Lecture 3: Source Files

Chapter 8: 02 Build a Neural Network with One Hot Encoding

Lecture 1: 00 What Is One Hot Encoding

Lecture 2: 01 Build Training Data

Lecture 3: 02 Build The Neural Network

Lecture 4: 03 Train The Neural Network

Lecture 5: 04 Make A Prediction

Lecture 6: Source Files

Chapter 9: 03 Build a Neural Network to Detect Lines in Images

Lecture 1: 01 Build Training Data To Represent Images

Lecture 2: 02 Build The Convolutional Neural Network

Lecture 3: 03 Train The Convolutional Neural Network

Lecture 4: 04 Make A Prediction Of Number Of Lines-4

Lecture 5: Source Files

Chapter 10: 04 Build an LSTM Recurrent Neural Network

Lecture 1: 00 What Is A Recurrent Neural Network

Lecture 2: 01 Generate Sequence And Label

Lecture 3: 02 Generate Dataset

Lecture 4: 03 Build The LSTM Model

Lecture 5: 04 Train The Model

Lecture 6: Source Files

Chapter 11: 05 Build a Model to Classify Iris Species

Lecture 1: 01 Process Iris Data

Lecture 2: 02 Convert Data To Tensors

Lecture 3: 03 Separate Training And Testing Data

Lecture 4: 04 Create Training And Testing Datasets

Lecture 5: 05 Build The Model

Lecture 6: 06 Train The Model

Lecture 7: 07 Make A Prediction

Lecture 8: Source Files

Chapter 12: 06 Build a Positive vs Negative Text Classifier

Lecture 1: 01 Load Model And Dataset

Lecture 2: 02 Get User Input For Sentiment Analysis

Lecture 3: 03 Make A Prediction

Lecture 4: Source Files

Chapter 13: 07 Build a Neural Network to Recognize Handwriting

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: 0 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 3 votes

5 stars: 1 votes

Frequently Asked Questions

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