Detalles

 

 

Requisitos previos:

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

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.

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