Brochure
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- Chapter 1 Introduction
- Lesson 1: What's This All About?
- Lesson 2: Who Is the Course For?
- Chapter 1 Lesson 2: Graded Discussion
- Background About You Quiz
- Lesson 3: What Will You Learn?
- Course Syllabus: Graded Discussion
Google Artificial Intelligence for JavaScript Developers with TensorFlow JavaScript
Quick Facts
| particular | details | ||||
|---|---|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Learning efforts
3-4 Hours Per Week
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Course and certificate fees
Type of course
Free
certificate availability
Yes
certificate providing authority
certificate fees
₹25,044
The syllabus
Chapter 1: Course Introduction
Chapter 2: Introduction to ML and TensorFlow.js
- Chapter 2 Introduction
- Lesson 1: Artificial Intelligence, Machine Learning, and Deep Learning
- Lesson 2: Demystifying Machine Learning
- Lesson 3: How to Train ML Systems?
- Chapter 2 Lesson 3: Graded Discussion Prompt
- Chapter 2 Graded Quiz 1
- Lesson 4: What is TensorFlow.js?
- Lesson 5: 3 Ways to Use Machine Learning
- Chapter 2 Graded Quiz 2
Chapter 3: Using Pre-made Models
- Chapter 3 Introduction
- Lesson 1: What are Pre-trained Models
- Chapter 3 Lesson 1: Graded Discussion Prompt
- Lesson 2: Selecting a Model to use
- Chapter 3 Graded Quiz 1
- Lesson 3: Make Your Own Smart Camera
- Chapter 3 Lesson 3 - Graded Discussion Prompt: Extend the Smart Camera Project
- Lesson 4: Tensors In Tensors Out
- Chapter 3 Graded Quiz 2
- Lesson 5: Using Raw TFJS Pre-Trained Models
- Lesson 6: Using More Advanced Raw Pre-Trained Models
- Chapter 3 Graded Quiz 3
- Chapter 3: Technical Assessment: Smart Camera
Chapter 4: Writing Custom Models
- Chapter 4 Introduction
- Lesson 1: Rolling Your Custom Models
- Lesson 2: Training / Testing / Validation Datasets
- Lesson 3.1: Perceptrons and Neurons
- Lesson 3.2: Training Neurons
- Lesson 4.1: Implementing a Neuron for Linear Regression - Analyzing Data
- Lesson 4.2: Implementing a Neuron for Linear Regression - Importing and Normalizing Data
- Lesson 4.3: Implementing a Neuron for Linear Regression - Model Creation and Training
- Chapter 4 Graded Quiz 1
- Lesson 5.1: Multi-Layer Perceptrons - Finding the Limits of a Single Neuron
- Lesson 5.2: Multi-Layer Perceptron - Neural Networks for More Complex Non-Linear Data
- Lesson 6.1: Multi-Layer Perceptrons for Classification
- Lesson 6.2: Implementing a Classifier Using a Multi-Layer Perceptron
- Chapter 4 Technical Assessment: Make a Multilayered Perceptron for Classification
- Lesson 7.1: Beyond Perceptrons - Convolutional Neural Networks
- Lesson 7.2: Implementing a Convolutional Neural Network
- Chapter 4 Graded Quiz 2
- Chapter 4: Graded Discussion Prompt: Investigate and Discuss Three Pre-Trained Models
Chapter 5: Transfer Learning
- Chapter 5 Introduction
- Lesson 1: Transfer Learning
- Lesson 2: Make Teachable Machine using Transfer Learning
- Lesson 3: Transfer Learning with Layers Models
- Chapter 5: Graded Discussion Prompt
- Chapter 5: Graded Quiz
Chapter 6: Reusing existing models from Python
- Chapter 6 Introduction
- Lesson 1: Converting Models From Python
- Lesson 2: Converting Python Saved Models
- Lesson 3: Natural Language Processing - Understanding Written Text
- Chapter 6 Graded Quiz 1
- Lesson 4.1.: Comment Spam Detection
- Lesson 4.2.: Comment Spam Detection - Using a Pre-Trained NLP Model
- Lesson 4.3.: Comment Spam Detection - Tokenization
- Lesson 4.4.: Comment Spam Detection - Web Sockets
- Lesson 5: Dealing with Edge Cases
- Lesson 6: Using the Retrained Spam Detection Model
- Chapter 6: Graded Discussion Prompt
- Chapter 6 Graded Quiz 2
Chapter 7: To the Future and Beyond
- Chapter 7 Introduction
- Lesson 1: Machine Learning as a Web Engineer
- Lesson 2: To the Future and Beyond - Course Summary
- Chapter 7: Post-course Survey and Discussion Forum
Articles
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