Google AI for JavaScript Developers with TensorFlow.js

This introductory module provides an overview of the course and its applications in real-world contexts. Students learn about the course structure, target audience, and the types of projects they will build. The module helps establish expectations and demonstrates the practical value of applying TensorFlow.js to web applications.

Skills you'll gain

This module covers fundamental concepts of machine learning and introduces TensorFlow.js. Students learn the differences between AI, ML, and Deep Learning, core ML concepts without complex mathematics, and the capabilities of TensorFlow.js. The module explains different approaches to implementing machine learning in web applications.

Skills you'll gain

This module focuses on leveraging existing machine learning models in JavaScript applications. Students learn to select appropriate pre-trained models, implement object detection systems, create smart security cameras, load saved TensorFlow.js models, and work with tensors as inputs and outputs. The module emphasizes practical implementation of ready-to-use AI capabilities.

Skills you'll gain

This module teaches students how to develop their own machine learning models from scratch. Students learn about perceptrons (artificial neurons), implementing linear regression with single neurons, building multi-layered perceptrons for complex data, utilizing convolutional neural networks for image processing, and researching different ML architectures. The module provides hands-on experience with core neural network concepts.

Skills you'll gain

This module explains how to adapt existing models for new purposes through transfer learning. Students learn the concept of reusing pre-trained models with custom data, creating their own version of Google's Teachable Machine, and developing practical project ideas using transfer learning techniques. The module demonstrates efficient ways to create powerful ML applications with minimal training data.

Skills you'll gain

This module covers the process of converting and using Python-based machine learning models in JavaScript. Students learn the benefits of model reuse across platforms, converting saved Python models to TensorFlow.js format, implementing comment spam detection systems, selecting appropriate models for specific tasks, handling edge cases, and retraining models to address custom requirements.

Skills you'll gain

This concluding module helps students transition from the course to real-world applications. Students learn about career opportunities in machine learning for web engineers, strategies for collaborative development, and how to join the TensorFlow.js community. The module provides a comprehensive summary of course concepts and guidance for continuing education in machine learning.

Skills you'll gain