数据科学和机器学习基础

Data Science and Machine Learning Essentials

Learn key concepts of data science and machine learning with examples on how to build a cloud data science solution with R, Python and Azure Machine Learning from the Cortana Analytics Suite.

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数据科学和机器学习基础
  • 分类: 计算机
  • 平台: edX
  • 语言: 英语

课程概况

Demand for Data science talent is exploding. Learn these essentials with experts from M.I.T and the industry, partnering with Microsoft to help develop your career as a data scientist. By the end of this course, you will know how to build and derive insights from data science and machine learning models. You will learn key concepts in data acquisition, preparation, exploration and visualization along with examples on how to build a cloud data science solution using Azure Machine Learning, R & Python.

Data Science is an essential skill for analyzing and deriving useful insights from data, big and small. McKinsey estimates that by 2018, a 500,000 strong workforce of data scientists will be needed in US alone. The resulting talent gap must be filled by a new generation of data scientists.

This course is organized into 5 weekly modules each concluding with a quiz. By achieving a passing grade in the final course assessment you will receive a certificate demonstrating that you have acquired data science skills and knowledge. Apart from answering your questions on the forum, faculty will host an office hour to address questions you may have while undertaking this course.

Get an ID verified certificate to demonstrate your data science knowledge and share on Linked-in.

学习内容

The data science process
Overview of data science theory
Data acquisition, ingestion, sampling, quantization, cleaning and transformation
Building data science workflows with Azure ML
Data science tools including R, Python and SQL
Data exploration and visualization
Building and evaluating machine learning models
Publishing machine learning models with the Azure ML
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课程大纲

Module I Introduction
Introduction to Data Science
Overview of the Data Science process
Introduction to Data Science technologies
Introduction to Machine Learning
Regressions
Classification
Clustering
Recommendation

Module 2: Working with Data in Azure ML
Data Acquisition
Data Ingestion and Ingress
Data Sampling and Quantization
Data Cleaning and Transformation

Module 3: Building and Evaluation of Models
Data Exploration and Visualization
Business Metrics and Cost-Based Metrics
Model Evaluation, Comparison and Selection

Module 4: Models in Azure ML, Part 1
Regression Models
Classification Models
Unsupervised Learning Models

Module 5: Models in Azure ML, Part 2
Recommendation Models
Publishing AML Models
Course Exam

主讲教师

Dr. Steve Elston
Cynthia Rudin

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