MOC 20774
PERFORM CLOUD DATA SCIENCE WITH AZURE MACHINE LEARNING
Duración:
25 horas lectivas.
5 Horas / Día
Detalles
Requisitos previos MOC 20774:
In addition to their professional experience, students who attend this course should have:
- Programming experience using R, and familiarity with common R packages
- Knowledge of common statistical methods and data analysis best practices.
- Basic knowledge of the Microsoft Windows operating system and its core functionality.
- Working knowledge of relational databases.
Ubicación
- Presencial en Madrid y Barcelona
- En oficina de cliente
- Online Direct
Objetivos MOC 20774
El propósito principal del curso es brindar a los estudiantes la capacidad de analizar y presentar datos mediante el aprendizaje automático de Azure y proporcionar una introducción al uso del aprendizaje automático con herramientas de big data como HDInsight y R Services.
Temario MOC 20774
Module 1: Introduction to Machine Learning
- What is machine learning?
- Introduction to machine learning algorithms
- Introduction to machine learning languages
Lab : Introduction to machine Learning
Module 2: Introduction to Azure Machine
- Azure machine learning overview
- Introduction to Azure machine learning studio
- Developing and hosting Azure machine learning applications
Lab : Introduction to Azure machine learning
Module 3: Managing Datasets
- Categorizing your data
- Importing data to Azure machine learning
- Exploring and transforming data in Azure machine learning
Lab : Managing Datasets
Module 4: Preparing Data for use with Azure Machine Learning
- Data pre-processing
- Handling incomplete datasets
Lab : Preparing data for use with Azure machine learning
Module 5: Using Feature Engineering and Selection
- Using feature engineering
- Using feature selection
Lab : Using feature engineering and selection
Module 6: Building Azure Machine Learning Models
- Azure machine learning workflows
- Scoring and evaluating models
- Using regression algorithms
- Using neural networks
Lab : Building Azure machine learning models
Module 7: Using Classification and Clustering with Azure machine learning models
- Using classification algorithms
- Clustering techniques
- Selecting algorithms
Lab : Using classification and clustering with Azure machine learning models
Module 8: Using R and Python with Azure Machine Learning
- Using R
- Using Python
- Incorporating R and Python into Machine Learning experiments
Lab : Using R and Python with Azure machine learning
Module 9: Initializing and Optimizing Machine Learning Models
- Using hyper-parameters
- Using multiple algorithms and models
- Scoring and evaluating Models
Lab : Initializing and optimizing machine learning models
Module 10: Using Azure Machine Learning Models
- Deploying and publishing models
- Consuming Experiments
Lab : Using Azure machine learning models
Module 11: Using Cognitive Services
- Cognitive services overview
- Processing language
- Processing images and video
- Recommending products
Lab : Using Cognitive Services
Module 12: Using Machine Learning with HDInsight
- Introduction to HDInsight
- HDInsight cluster types
- HDInsight and machine learning models
Lab : Machine Learning with HDInsight
Module 13: Using R Services with Machine Learning
- R and R server overview
- Using R server with machine learning
- Using R with SQL Server
Lab : Using R services with machine learning