Carl Osipov walks you through the process of building machine learning models with TensorFlow. You'll learn about data exploration, feature engineering, model creation, training, evaluation, deployment, and more.
Explore the AWS machine learning platform using Amazon SageMaker
Wenming Ye (Amazon Web Services), Miro Enev
Machine learning and IoT projects are increasingly common at enterprises and startups alike and have been the key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev lead a hands-on deep dive into the AWS machine learning platform, using Project Jupyter-based Amazon SageMaker to build, train, and deploy ML/DL models to the cloud and AWS DeepLens.
Enterprise and organizational adoption, Extensions and customization, Usage and application
Hands-on data science with Python
Zachary Glassman (The Data Incubator)
Zachary Glassman leads a hands-on dive into building intelligent business applications using machine learning, walking you through all the steps of developing a machine learning pipeline. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend these models into two applications from real-world datasets.
Reproducible research best practices (highlighting Kaggle Kernels)
Rachael Tatman (Kaggle)
Rachael Tatman shows you how to take an existing research project and make it fully reproducible using Kaggle Kernels. You'll learn best practices for and get hands-on experience with each of the three components necessary for completely reproducible research.