Application Integration on Oracle Cloud Ed 4 Course in Saudi Arabia

Develop a data strategy around machine learning

Examine use cases that are then reimagined through an ML lens

Recognize biases that ML can amplify

Leverage Google Cloud Platform tools and environment to do ML

Learn from Google's experience to avoid common pitfalls

Carry out data science tasks in online collaborative notebooks

Invoke pre-trained ML models from Cloud AI Platform

Describe how to improve data quality

Perform exploratory data analysis

Build and train supervised learning models

Optimize and evaluate models using loss functions and performance metrics

Mitigate common problems that arise in machine learning

Create repeatable and scalable training, evaluation, and test datasets

Create TensorFlow 2.x and Keras machine learning models

Describe Tensorflow 2.x key components

Use the tf.data library to manipulate data and large datasets

Use the Keras Sequential and Functional APIs for simple and advanced model creation

Train, deploy, and productionalize ML models at scale with Cloud AI Platform

Compare the key required aspects of a good feature.

Combine and create new feature combinations through feature crosses

Perform feature engineering using BQML, Keras, and TensorFlow 2.x

Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep

Understand and apply how TensorFlow transforms features

Optimize model performance with hyperparameter tuning

Experiment with neural networks and fine-tune performance

Enhance ML model features with embedding layer

Summary