Course Description

This course serves as a comprehensive guide for web engineers, designers, and creative thinkers eager to integrate Artificial Intelligence and Machine Learning into their web applications without the need for a deep mathematical background. With an emphasis on practical implementation using TensorFlow.js, this course enables learners to apply AI technologies across various platforms, including client and server sides, native apps, and IoT devices. No prior Machine Machine Learning background is required, but a basic understanding of HTML, CSS, and JavaScript is recommended.

What Students Will Learn

• Key terminologies in AI and Machine Learning, efficiently using TensorFlow.js
• The conceptual differences and practical applications of AI, ML, and Deep Learning
• Implementation strategies for using pre-built and custom ML models on the web
• Methods for integrating and executing ML models across various web technologies
• Real-world applications and creative project ideas leveraging AI and ML

Prerequisites

No previous experience in Machine Learning is necessary. A basic, functional knowledge of HTML, CSS, and JavaScript is highly recommended to fully benefit from the course.

Course Coverage

• Difference between AI, ML, and Deep Learning
• Introduction and hands-on with TensorFlow.js
• Using pre-built ML models and understanding tensors
• Building custom ML models and learning neural network basics
• Practical applications like spam detection and real-time object detection
• Transfer learning and model conversion from Python to JavaScript

Who This Course Is For

This course is designed for web developers, software engineers, and IT professionals who are looking to integrate Machine Learning into their projects without needing to dive deep into the complex mathematics often associated with AI training. Creative individuals and entrepreneurs who want to leverage machine learning to enhance their products or services will also find this course valuable.

Application of Learned Skills

Learners can apply the skills from this course to build enhanced web applications that are capable of performing complex tasks such as real-time object recognition, automated text classification, and predictive analytics. These capabilities can be utilized across different industries such as e-commerce, healthcare, automotive, and any other sector looking to incorporate smart technologies for better user engagement and operational efficiency.

Syllabus

• Welcoming Module
• Basics of Machine Learning and TensorFlow.js
• Exploration and implementation of pre-built models
• Fundamentals of writing custom models and neural networks
• Advanced topics: Transfer learning and reusing Python models
• Wrapping up and future trends in ML application