Google Cloud Fundamentals: Big Data and Machine Learning FREE SESSION

• Data analysts, data scientists, and business analysts who are getting started with Google Cloud.
• Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results, and creating reports.
• Executives and IT decision makers evaluating Google Cloud for use by data scientists.

• Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
• Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
• Employ BigQuery and Cloud SQL to carry out interactive data analysis.
• Choose between different data processing products in Google Cloud.
• Create ML models with BigQuery ML, ML APIs, and AutoML.

The course includes presentations, demonstrations, and hands-on labs.

Module 1: Introduction to Google Cloud

• Identify the different aspects of Google Cloud’s infrastructure.
• Identify the big data and ML products that form Google Cloud.

Module 2: Recommending Products Using Cloud SQL and Spark

• Review how businesses use recommendation models.
• Evaluate how and where you will compute and store your housing rental model results.
• Analyze how running Hadoop in the cloud with Dataproc can enable scale.
• Evaluate different approaches for storing recommendation data off-cluster.

Module 3: Predicting Visitor Purchases Using BigQuery ML

• Analyze big data at scale with BigQuery.
• Learn how BigQuery processes queries and stores data at scale.
• Walkthrough key ML terms: features, labels, training data.
• Evaluate the different types of models for structured datasets.
• Create custom ML models with BigQuery ML.

Module 4: Real-time Dashboards with Pub/Sub, Dataflow, and Google Data Studio

• Identify modern data pipeline challenges and how to solve them at scale with Dataflow.
• Design streaming pipelines with Apache Beam.
• Build collaborative real-time dashboards with Data Studio.

Module 5: Deriving Insights from Unstructured Data Using Machine Learning

• Evaluate how businesses use unstructured ML models and how the models work.
• Choose the right approach for machine learning models between pre-built and custom.
• Create a high-performing custom image classification model with no code using AutoML.

Module 6: Summary

• Recap of key learning points.
•  Resources

Roughly one year of experience with one or more of the following:

• A common query language such as SQL.
• Extract, transform, and load activities.
• Data modeling.
• Machine learning and/or statistics.
• Programming in Python.