DeepLearning.AI TensorFlow Developer Professional Course Syllabus

Overview: This Professional Certificate program from DeepLearning.AI provides a practical introduction to deep learning using TensorFlow, designed for beginners with some background in Python and machine learning. The course is divided into five core modules followed by a hands-on final project, totaling approximately 74 hours of learning. Learners will progress through foundational concepts to real-world applications, including computer vision, natural language processing, and time series forecasting, with hands-on coding assignments in each module. The flexible, self-paced structure makes it ideal for aspiring developers, data scientists, and AI practitioners.

Module 1: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Estimated time: 22 hours

• Understand the fundamentals of TensorFlow and its role in AI and machine learning
• Build and train a simple neural network using Keras
• Apply neural networks to computer vision tasks
• Use TensorFlow datasets and preprocessing tools

Module 2: Convolutional Neural Networks in TensorFlow

Estimated time: 18 hours

• Work with real-world image data using CNNs
• Implement data augmentation to improve model generalization
• Apply dropout techniques to prevent overfitting
• Use transfer learning with pre-trained models

Module 3: Natural Language Processing in TensorFlow

Estimated time: 16 hours

• Process and tokenize text data for NLP
• Build RNNs, GRUs, and LSTMs for sequence modeling
• Apply embedding layers to represent text
• Develop text classification and sentiment analysis models

Module 4: Sequences, Time Series, and Prediction

Estimated time: 18 hours

• Prepare time series data for deep learning
• Build models using RNNs and CNNs for forecasting
• Implement best practices for sequence prediction

Module 5: Best Practices in TensorFlow

Estimated time: 10 hours

• Optimize model performance using callbacks and hyperparameter tuning
• Apply regularization techniques to improve training
• Use TensorFlow tools for debugging and visualization

Module 6: Final Project

Estimated time: 20 hours

• Design and train a deep learning model using TensorFlow
• Apply techniques from computer vision, NLP, or time series forecasting
• Submit a working notebook with documented results and analysis

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• Experience with mathematical concepts such as linear algebra and calculus is helpful

What You'll Be Able to Do After

• Build and train deep neural networks using TensorFlow
• Apply convolutional neural networks to computer vision tasks
• Develop natural language processing systems using RNNs, GRUs, and LSTMs
• Implement time series forecasting models with real-world data
• Use best practices for developing and optimizing TensorFlow models