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    • Google Artificial Intelligence for JavaScript Developers with TensorFlow JavaScript

    Google Artificial Intelligence for JavaScript Developers with TensorFlow JavaScript

    BY
    Google via Edx

    Brochure Enquire
    Lavel

    Beginner

    Mode

    Online

    Duration

    7 Weeks

    Fees

    Free

    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

    Course and certificate fees

    Type of course

    Free

    certificate availability

    Yes

    certificate providing authority

    Google

    certificate fees

    ₹25,044

    The syllabus

    Chapter 1: Course Introduction

    • 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

    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

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