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· 분류 : 외국도서 > 컴퓨터 > 프로그래밍 > 마이크로소프트 프로그래밍
· ISBN : 9781484264041
· 쪽수 : 285쪽
· 출판일 : 2020-12-19
목차
Chapter 1 Data Science in the modern enterprise
What is Data Science
The Data Scientists' tools and lingo
Ethics and ethical AI
Significance of Data Science in organizations
Case Studies of applied Data Science
Chapter 2 Most important Statistical Tehniques in Data Science
Top Statistical Tehniques Data Scientists need to know
Supervised Learning
Unsupervised Learning
Regression/Classification/ Forecasting
Bayesian method
Time series analysis
Linear regression
Sampling methods
Reinforcement Learning
Part 2 - Machine Learning in Microsoft Azure
Chapter 3 Basics of data preparation and data engineeringIngesting disparate data sources
Preparing data for analysis
Data Exploration
Feature Engineering
Chapter 4 Introducing Azure Machine LearningAzureML- DataStores/ Datasets
Azure ML Compute/CLusters/inference/batch-realtime
Azure ML Service- Training and Building
Azure ML Service- Deploying
Azure ML Service- Pipelines
Azure ML Studio/Designer
Azure Automated Machine Learning (AutoML)
Hyperparameter Training
Azure ML- Security
Case Study
Part 3 - Azure Databricks
Chapter 5 Spark and Big Data
Spark and Hadoop
What is Big Data?
Why Spark is the platform of choice for Big Data
Challenges with Big Data
Chapter 6 Azure Databricks Basics
What is Azure Databricks
Azure Databricks from the Data Engineers' perspective
Azure Databricks from the Data Scientists' perspective
Chapter 7 Azure Databricks
Deploying the Azure Databricks workspace
Creating and Managing Clusters
Creating and managing users and groups
Managing Databricks Notebooks
Using Databricks Notebooks
DBFS
Connecting to ADLS
Sample Notebook(s)
Part 4 - Operationalizing Data Science
Chapter 8 Machine Learning Operations
Operationalization concepts and DevOps
MLOps in Azure
MLFlow in Azure Databricks
Git
Chapter 9 Practical ML
Introducing use cases in the different industries
Democratizing AI through ML
