Artificial Intelligence TensorFlow Machine Learning

Artificial Intelligence – TensorFlow Machine Learning, available at $19.99, has an average rating of 3.3, with 70 lectures, based on 24 reviews, and has 454 subscribers.

You will learn about Understand Machine Learning from basics Understand the basics of a neural network Understand basics of Tensor operations for Tensorflow Create project using Polynomial Regression Create basic HTML, Javascript projects Use JSFiddle to create Javascript applets Use NodeJs to create comprehensive machine learning projects This course is ideal for individuals who are Beginners: Getting started with Tensorflow or Intermediates: Learn by having an integrated platform or Experts: Further polishing your skills It is particularly useful for Beginners: Getting started with Tensorflow or Intermediates: Learn by having an integrated platform or Experts: Further polishing your skills.

Enroll now: Artificial Intelligence – TensorFlow Machine Learning

Summary

Title: Artificial Intelligence – TensorFlow Machine Learning

Price: $19.99

Average Rating: 3.3

Number of Lectures: 70

Number of Published Lectures: 70

Number of Curriculum Items: 70

Number of Published Curriculum Objects: 70

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

Understand Machine Learning from basics

Understand the basics of a neural network

Understand basics of Tensor operations for Tensorflow

Create project using Polynomial Regression

Create basic HTML, Javascript projects

Use JSFiddle to create Javascript applets

Use NodeJs to create comprehensive machine learning projects

Who Should Attend

Beginners: Getting started with Tensorflow

Intermediates: Learn by having an integrated platform

Experts: Further polishing your skills

Target Audiences

Beginners: Getting started with Tensorflow

Intermediates: Learn by having an integrated platform

Experts: Further polishing your skills

This course teaches machine learning from the basics so that you can get started with created amazing machine learning programs. With a well structured architecture, this course is divided into 4 modules:

Theory section: It is very important to understand the reason of learning something. The need for learning machine learning and javascript in this particular case is explained in this section.

Foundation section: In this section, most of the basic topics required to approach a particular problem are covered like the basics of javascript, what are neural networks, dom manipulation, what are tensors and many more such topics

Practice section: In this section, you put your learnt skills to a test by writing code to solve a particular problem. The explanation of the solution to the problem is also provided in good detail which makes hands-on learning even more efficient.

Project section:In this section, we build together a full stack project which has some real life use case and can provide a glimpse on the value creation by writing good quality machine learning programs

Happy Coding,

Vinay Phadnis 馃檪

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Machine Learning or AI

Lecture 2: why browser or Javascript

Lecture 3: Course Guidelines

Chapter 2: Javascript Basics

Lecture 1: Section Intro

Lecture 2: Integrating with HTML

Lecture 3: Variables and Variable types

Lecture 4: Conditioning

Lecture 5: Functions()

Lecture 6: Looping in Javascript

Chapter 3: DOM manipulations

Lecture 1: Intro to the section

Lecture 2: dom manipulation

Lecture 3: querySelectorAll()

Lecture 4: Buttons!

Chapter 4: Getting started with Machine Learning

Lecture 1: What is a tensor

Lecture 2: Creating a tensor

Lecture 3: Tensorflow zeros

Lecture 4: Tensorflow Variables

Lecture 5: Operations of variables

Lecture 6: Project #2

Lecture 7: Solution #2

Chapter 5: Equation Finder

Lecture 1: Install node and npm

Lecture 2: Project Description

Lecture 3: npm init

Lecture 4: Creating the input dataset – 1

Lecture 5: Creating the input dataset – 2

Lecture 6: Building a model

Lecture 7: Loss and train Function

Lecture 8: Building User Interface (Part -1)

Lecture 9: Building User Interface (Part -2)

Lecture 10: Creating the html

Lecture 11: Styling the HTML

Lecture 12: Completing index.js

Lecture 13: Adding Babel javascript

Lecture 14: package.json

Lecture 15: Installing Yarn

Lecture 16: Running the Program!

Chapter 6: Getting Started with Neural Networks

Lecture 1: What are Neurons

Lecture 2: What are weights

Lecture 3: What are biases

Lecture 4: Activation Functions

Chapter 7: Build your own Neural Network

Lecture 1: XOR logic project

Lecture 2: Training Loop

Lecture 3: Running the Program!

Chapter 8: Flappy Bird Game

Lecture 1: Project Intro

Lecture 2: index.html

Lecture 3: Adding minified js files

Lecture 4: Adding assets

Lecture 5: Importing assets

Lecture 6: Setting Gravity

Chapter 9: Setting up Neural Network for Flappy Bird

Lecture 1: Genetics

Lecture 2: Create Population

Lecture 3: Activate Brain

Lecture 4: Evolve Population

Lecture 5: Selection

Lecture 6: Mutating Genes

Lecture 7: Finishing Genetic.js

Chapter 10: Back to Gameplay.js

Lecture 1: Creating a Bird Object

Lecture 2: Creating a Tree Object

Lecture 3: Creating a Tree Group

Lecture 4: Creating a Bird Group

Lecture 5: Bitmap Objects

Lecture 6: Adding Buttons

Lecture 7: Update switch case

Lecture 8: Add Collisions

Lecture 9: Reset the Background

Lecture 10: More Methods

Lecture 11: Completing the Code!

Lecture 12: Running the Project

Chapter 11: Mathematics of Machine Learning

Lecture 1: Section Intro

Lecture 2: Bonus Lecture!

Instructors

Vinay Phadnis
CTO, Machine Learning & Quantum Consultant

Rating Distribution

1 stars: 2 votes

2 stars: 4 votes

3 stars: 2 votes

4 stars: 7 votes

5 stars: 9 votes

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