Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Course Syllabus

Overview: This course provides a hands-on introduction to TensorFlow, designed to help learners build and train neural networks for real-world AI applications. Through practical projects and guided instruction, you'll gain foundational skills in deep learning, focusing on computer vision and convolutional neural networks. The course spans approximately 22 hours of content, divided into four core modules and a final project, with flexible scheduling ideal for working professionals.

Module 1: A New Programming Paradigm

Estimated time: 5 hours

• Introduction to machine learning and deep learning concepts
• Understanding the shift from traditional programming to machine learning
• Building a simple neural network using TensorFlow
• Training and evaluating neural networks with real data

Module 2: Introduction to Computer Vision

Estimated time: 5 hours

• Basics of computer vision and image processing
• Implementing neural networks for image classification
• Using datasets like MNIST for training models
• Utilizing callbacks to monitor and control training processes

Module 3: Enhancing Vision with Convolutional Neural Networks

Estimated time: 5 hours

• Understanding convolutions and pooling layers
• Building convolutional neural networks (CNNs)
• Improving image recognition accuracy with CNNs
• Applying CNNs to real-world datasets

Module 4: Using Real-world Images

Estimated time: 7 hours

• Handling complex, real-world image data
• Implementing data augmentation techniques
• Applying transfer learning with pre-trained models
• Improving model generalization and performance

Module 5: Final Project

Estimated time: 5 hours

• Design and train a CNN for image classification
• Incorporate data augmentation and callbacks
• Evaluate model performance and submit for review

Prerequisites

• Basic understanding of Python programming
• Familiarity with high school-level math (algebra and statistics)
• Some exposure to machine learning concepts is helpful but not required

What You'll Be Able to Do After

• Build and train neural networks using TensorFlow
• Apply deep learning techniques to computer vision problems
• Use convolutions and pooling to improve model accuracy
• Implement data augmentation and transfer learning in real-world scenarios
• Earn a certificate as part of the DeepLearning.AI TensorFlow Developer Professional Certificate