TensorFlow for Deep Learning Bootcamp Course Syllabus

Overview: This comprehensive bootcamp is designed to prepare you for the TensorFlow Developer Certificate exam through hands-on projects and real-world applications. The course spans approximately 8 hours of on-demand video content, structured into 9 focused modules that build your skills progressively in deep learning with TensorFlow 2 and Keras. Each module combines theory, coding exercises, and practical tools to ensure mastery across computer vision, natural language processing, and time series forecasting. Lifetime access allows flexible learning at your own pace.

Module 1: Introduction to TensorFlow & Certification Overview

Estimated time: 0.5 hours

• Understand the scope of the TensorFlow Developer Certificate exam
• Install TensorFlow and configure your development environment
• Overview of course structure and learning path
• Set up Python and Jupyter for TensorFlow projects

Module 2: TensorFlow Fundamentals

Estimated time: 0.75 hours

• Introduction to tensors and tensor operations
• Auto-differentiation with GradientTape
• Reshaping and broadcasting in TensorFlow
• Core data types and operations in tf.Tensor

Module 3: Neural Network Regression and Classification

Estimated time: 1 hour

• Build dense neural networks using Keras Sequential API
• Implement loss functions, optimizers, and metrics
• Train and evaluate models on regression tasks
• Solve classification problems with softmax and cross-entropy

Module 4: Computer Vision & CNNs

Estimated time: 1 hour

• Design convolutional neural networks (CNNs) for images
• Apply CNNs to Fashion MNIST and CIFAR-10 datasets
• Use pooling, dropout, and batch normalization layers
• Improve model performance with data augmentation

Module 5: Transfer Learning & Fine-Tuning

Estimated time: 0.75 hours

• Leverage pretrained models like MobileNetV2
• Perform feature extraction from pre-trained networks
• Finetune models on custom datasets
• Evaluate trade-offs between training from scratch and transfer learning

Module 6: Time Series Forecasting & RNNs

Estimated time: 1 hour

• Build RNNs with LSTM and GRU layers
• Preprocess time series data for deep learning
• Predict future values using sequence models
• Analyze performance on real-world time series datasets

Module 7: Natural Language Processing (NLP)

Estimated time: 1 hour

• Text vectorization and tokenization with TensorFlow
• Use word embeddings and embedding layers
• Model sequences for sentiment analysis
• Generate text using recurrent architectures

Module 8: TensorFlow Tools & Deployment

Estimated time: 0.75 hours

• Monitor training with TensorBoard
• Save and load models using SavedModel format
• Convert models to TFLite for mobile and edge devices
• Optimize inference for lightweight deployment

Module 9: Final Exam Prep & Project Walkthrough

Estimated time: 1.25 hours

• Complete a certification-level end-to-end project
• Walkthrough of best practices for the official exam
• Review common pitfalls and debugging strategies
• Final tips to pass the TensorFlow Developer Certificate exam

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• Experience with Jupyter Notebooks or similar environments

What You'll Be Able to Do After

• Pass the TensorFlow Developer Certificate exam confidently
• Build and train deep learning models using TensorFlow 2 and Keras
• Apply CNNs, RNNs, and transfer learning to real-world datasets
• Develop AI applications in computer vision, NLP, and time series
• Deploy models using TFLite and monitor training with TensorBoard