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Deploy applications with Kubernetes objects — pods, replication controllers, deployments and services — and perform rolling updates and rollbacks in hands-on, task-based sessions.
Train and deploy an image classifier with Amazon Sagemaker's built-in image classification algorithm, distinguishing 37 dog and cat breeds from the IIIT-Oxford Pets Dataset.
Build a random forest model in R with the FFTrees package to predict poisonous mushrooms, splitting training and test sets, plotting distributions in ggplot2, and reading a confusion matrix.
Convert an Entity Relationship Diagram into a relational model in Dia, applying relational modeling rules and resolving one-to-many and many-to-many relationships with foreign keys and bridge tables.
Aprende desde cero a organizar datos en filas y columnas y realizar cálculos aritméticos básicos con Microsoft Excel.
Train and deploy a TensorFlow image classifier on Amazon SageMaker, using the SageMaker TensorFlow Estimator and the Oxford IIIT Pet dataset to tell cats from dogs.
Build a Scrum Master's toolbox of interactive websites and games that strengthen communication, cohesion, and teamwork across Scrum ceremonies for distributed or collocated teams.
Implement bubble, insertion, selection, merge, and quicksort in Java to order a Missouri tax dataset, comparing each algorithm's time and memory efficiency.
Build an agent-based model in NetLogo that simulates a hypothetical virus spreading through contact, tuning interaction rules and parameters to observe emergent behavior.
Prepare a COVID19 dataset of confirmed, recovered, and death cases and plot it with Plotly in Python to examine global spread, intensity, and lockdown impact.
Implement neural style transfer in PyTorch: blend a content and a style image, compute content and style loss functions, and minimize them to generate artistic images.
Save, load, and restore Keras models: persist weights alone or with the full architecture, and export to TensorFlow's SavedModel format for serving in production.
Map customer journeys in Miro to analyze customer segments, product value and features, and decide whether to modify product structure when entering the market.
Learn TypeScript's simple and advanced types, from strings and arrays to enums and interfaces, compile code from the command line, write classes, and restructure project files.
Implement Inversion of Control in an existing Spring Boot web service, injecting dependencies through the IOC framework and supplying POJOs to represent the data.
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