What will you learn in Build, Train and Deploy ML Models with Keras on Google Cloud Course
Learn best practices for using TensorFlow to build scalable AI-powered models.
Construct and train basic neural networks, including feedforward and convolutional architectures for image recognition.
Apply convolutions to improve network performance on computer vision tasks.
Work with Keras API for efficient model building, including Sequential API and its workflow.
Program Overview
Module 1: A New Programming Paradigm
⏳ ~5 hrs
Topics: Intro to ML/DL and TensorFlow’s programming paradigm. Includes discussion with Andrew Ng, neural network basics, and “Hello, World” neural nets.
Hands-on: TensorFlow setup and simple classification model coding in Python.
Module 2: The Sequential Model API
⏳ ~6 hrs
Topics: Build and train neural networks using the Keras Sequential API—cover layers, model compilation, fitting, evaluation, and prediction.
Hands-on: Build CNNs in Colab for MNIST digit classification.
Module 3: Validation, Regularization & Callbacks
⏳ ~6 hrs
Topics: Techniques to avoid overfitting, set up validation workflows, and use callbacks including EarlyStopping.
Hands-on: Train models on Iris dataset, tune with regularization, and practice callback mechanisms.
Module 4: Model Persistence & Advanced Structures
⏳ ~6 hrs
Topics: Save/load models, select weight-only vs full model saving, explore pretrained models. Also introduction to advanced architectures: CNNs, RNNs, transformers, and autoencoders.
Hands-on: Use Keras for advanced model building and application.
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Job Outlook
Prepares you for roles like ML Engineer, TensorFlow Developer, and AI Software Engineer.
Serves as a stepping-stone for the DeepLearning.AI TensorFlow Developer Professional Certificate (3–6 months, ~4.7★ from 25K reviews).
Specification: Build, Train and Deploy ML Models with Keras on Google Cloud
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FAQs
- Basic understanding of Python and introductory ML concepts is recommended.
- The course starts with TensorFlow setup and simple models for hands-on learning.
- Explains neural network fundamentals in an accessible way.
- Focuses on Keras Sequential API for model building.
- Suitable for learners with programming background looking to specialize in AI.
- Covers model persistence (saving/loading) and deployment workflows.
- Demonstrates use of Keras models in cloud environments.
- Introduces best practices for production-ready ML pipelines.
- Emphasizes practical lab exercises with Colab and Google Cloud.
- Prepares learners for ML engineer or AI developer roles.
- Introduces CNNs for image recognition and basic RNN concepts.
- Hands-on labs allow practice with feedforward and convolutional networks.
- Explains model tuning, regularization, and callback mechanisms.
- Advanced topics like transformers and autoencoders are briefly introduced.
- Serves as a foundation for deeper study in specialized AI courses.
- Explains ML concepts and workflows in conceptual terms.
- Helps managers understand model design, training, and deployment processes.
- Supports strategic planning for AI-driven projects.
- Enhances communication with technical teams.
- Provides a framework for evaluating ML initiatives in organizations.
- Focuses specifically on Keras API and high-level model building.
- Balances theory with hands-on lab-driven exercises.
- Emphasizes cloud-based deployment with Google Cloud integration.
- Includes practical exercises for CNNs and Sequential models.
- Provides a clear path toward AI developer roles and DeepLearning.AI certifications.
