Video: 5.2 How does ML on unstructured data work | Deriving Insights from Unstructured Data using ML

Curso: Google Cloud Big Data and Machine Learning Fundamentals
Idioma:   Course LanguageDificuldade:  
Básico

Descrição:
This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud.

Progresso:

0. Google Cloud Big Data and Machine Learning Fundamentals | Introduction to the Google Cloud
1.1 Welcome to Google Cloud Big Data and Machine Learning Fundamentals | Introduction to the Google
1.2 Introduction to Google Cloud | Introduction to the Google Cloud Big Data and Machine Learning
1.3 Compute Power for Analytic and ML Workloads | Introduction to the Google Cloud Big Data and ML
1.4 Demo Creating a VM on Compute Engine | Introduction to the Google Cloud Big Data and ML
1.5 Elastic Storage with Google Cloud Storage | Introduction to the Google Cloud Big Data and ML
1.6 Build on Google's Global Network | Introduction to the Google Cloud Big Data and ML
1.7 Security On premise vs Cloud native | Introduction to the Google Cloud Big Data and ML
1.8 Evolution of Google Cloud Big Data Tools | Introduction to the Google Cloud Big Data and ML
1.9 Google Cloud Public Datasets program | Introduction to the Google Cloud Big Data and ML
1.10 Labs Exploring a BigQuery Public Dataset | Introduction to the Google Cloud Big Data and ML
1.11 Choosing the right approach | Introduction to the Google Cloud Big Data and Machine Learning
1.12 What you can do with Google Cloud | Introduction to the Google Cloud Big Data and ML
1.13 Activity Explore real customer solution architectures | Introduction to the Google Cloud
1.14 Key roles in a data driven organization | Introduction to the Google Cloud Big Data and ML
1.15 Module Review | Introduction to the Google Cloud Big Data and Machine Learning Fundamentals
2.1 How businesses use recommendation systems | Recommending Products using Cloud SQL and Spark
2.2 Introduction to machine learning | Recommending Products using Cloud SQL and Spark
2.3 Challenge ML for recommending housing rentals | Recommending Products using Cloud SQL and Spark
2.4 Approach Move from on premise to Google Cloud | Recommending Products using Cloud SQL and Spark
2.5 Demo From zero to an Apache Spark job in 10 minutes or less | Recommending Products using Cloud
2.6 Challenge Utilizing & tuning on premise clusters | Recommending Products using Cloud SQL & Spark
2.7 Move storage off cluster with Google Cloud Storage | Recommending Product using Cloud SQL&Spark
2.8 Lab Intro Recommending Products Using Cloud SQL and Spark | Recommending Products
3.1 Introduction to BigQuery | Predict Visitor Purchases Using BigQuery ML | Google Cloud Big Data
3.2 Demo Query 2 billion lines of Github code in less than 30 seconds | Predict Visitor Purchases
3.3 BigQuery Fast SQL Engine | Predict Visitor Purchases Using BigQuery ML
3.4 Demo Exploring bike share data with SQL | Predict Visitor Purchases Using BigQuery ML
3.5 Data quality | Predict Visitor Purchases Using BigQuery ML | Google Cloud Big Data and ML
3.6 BigQuery Managed Storage | Predict Visitor Purchases Using BigQuery ML
3.7 Insights from geographic data | Predict Visitor Purchases Using BigQuery ML
3.8 Demo Analyzing lightning strikes with BigQuery GIS | Predict Visitor Purchases Using BigQuery ML
3.9 Choosing a ML model type for structured data | Predict Visitor Purchases Using BigQuery ML
3.10 Predicting customer lifetime value | Predict Visitor Purchases Using BigQuery ML | Google Cloud
3.11 BigQuery ML Create models with SQL | Predict Visitor Purchases Using BigQuery ML | Google Clou
3.12 Phases in ML model lifecycle | Predict Visitor Purchases Using BigQuery ML | Google Cloud Big
3.13 BigQuery ML key features walkthrough | Predict Visitor Purchases Using BigQuery ML | Google
4.1 Modern data pipeline challenges | Real-time IoT Dashboards with PubSub, Dataflow, & Data Studio
4.2 Message oriented architectures with Pub Sub | Real-time IoT Dashboards with PubSub, Dataflow
4.3 Designing streaming pipelines with Apache Beam | Real-time IoT Dashboards with PubSub, Dataflow
4.4 Implementing Streaming Pipelines on Cloud Dataflow | Real-time IoT Dashboards with PubSub
4.5 Visualizing Insights with Data Studio | Real-time IoT Dashboards with PubSub, Dataflow, and
4.6 Creating charts with Data Studio | Real-time IoT Dashboards with PubSub, Dataflow, & Data Studio
4.7 Data Studio walkthrough | Real-time IoT Dashboards with PubSub, Dataflow, and Data Studio
4.8 Lab Intro Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow | Real-tim
5.1 Where is unstructured ML used in business | Deriving Insights from Unstructured Data using ML
5.2 How does ML on unstructured data work | Deriving Insights from Unstructured Data using ML
5.3 Demo ML built into Google Photos | Deriving Insights from Unstructured Data using ML
5.4 Comparing approaches to ML | Deriving Insights from Unstructured Data using Machine Learning
5.5 Demo Using ML building blocks | Deriving Insights from Unstructured Data using Machine Learning
5.6 Using pre built AI to create a chatbot | Deriving Insights from Unstructured Data using ML
5.7 Customizing Pre built models with AutoML | Deriving Insights from Unstructured Data using ML
5.8 Lab Intro Classifying Images of Clouds in the Cloud with AutoML Vision | Deriving Insights
5.10 Building a Custom Model | Deriving Insights from Unstructured Data using Machine Learning
5.11 Demo Text classification done three ways | Deriving Insights from Unstructured Data using ML
6. Course Summary | Summary | Google Cloud Big Data and Machine Learning Fundamentals Course

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