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:
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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.
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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
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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.
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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 🙂
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
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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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