Course Overview
This course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text, along with recommendation systems.
Who should attend
- Data Engineers and programmers interested in learning how to apply machine learning in practice
- Anyone interested in learning how to leverage machine learning in their enterprise
Prerequisites
To get the most out of this course, participants should have:
- Knowledge of machine learning and TensorFlow to the level covered in Machine Learning on Google Cloud coursework
- Experience coding in Python
- Knowledge of basic statistics
- Knowledge of SQL and cloud computing (helpful)
Course Objectives
This course teaches participants the following skills:
- Implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing
- Solve an ML problem by building an end-to-end pipeline, going from data exploration, preprocessing, feature engineering, model building, hyperparameter tuning, deployment, and serving
- Develop a range of image classification models from simple linear models to high-performing convolutional neural networks (CNNs) with batch normalization, augmentation, and transfer learning
- Forecast time-series values using CNNs, recurrent neural networks (RNNs), and LSTMs
- Apply ML to natural language text using CNNs, RNNs, LSTMs, reusable word embeddings, and encoder-decoder generative models
- Implement content-based, collaborative, hybrid, and neural recommendation models in TensorFlow
German
Switzerland