本课程是大数据专项课程专项课程的一部分

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2,395 个评分

Mai Nguyen 另外 +1 位授课教师

64,614 人已注册

提供方

大数据专项课程加州大学圣地亚哥分校

课程信息

42,105 次近期查看

Want to make sense of the volumes of data you have collected?  Need to incorporate data-driven decisions into your process?  This course provides an overview of machine learning techniques to explore, analyze, and leverage data.  You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
At the end of the course, you will be able to:
•	Design an approach to leverage data using the steps in the machine learning process.
•	Apply machine learning techniques to explore and prepare data for modeling.
•	Identify the type of machine learning problem in order to apply the appropriate set of techniques.
•	Construct models that learn from data using widely available open source tools.
•	Analyze big data problems using scalable machine learning algorithms on Spark.

Software Requirements: 
Cloudera VM, KNIME, Spark

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第 4 门课程（共 6 门）

大数据专项课程

完成时间大约为22 小时

英语（English）

字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 中文（简体）, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）

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体验 Coursera 企业版

您将获得的技能

Machine Learning Concepts
Knime
Machine Learning
Apache Spark

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

第 4 门课程（共 6 门）

大数据专项课程

完成时间大约为22 小时

英语（English）

对员工进行热门技能培训能否为您的公司带来益处？

体验 Coursera 企业版

授课教师

授课教师评分

4.8/5 （234 个评分）

Mai Nguyen

Lead for Data Analytics

San Diego Supercomputer Center

64,614 个学生

1 门课程

Ilkay Altintas

Chief Data Science Officer

San Diego Supercomputer Center

447,365 个学生

12 门课程

提供方

加州大学圣地亚哥分校

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.

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授课大纲 - 您将从这门课程中学到什么

内容评分96%（6,523 个评分）

周

周 1

完成时间为 1 小时

Welcome

完成时间为 1 小时

2 个视频（总计 14 分钟）

完成时间为 4 小时

Introduction to Machine Learning with Big Data

完成时间为 4 小时

7 个视频（总计 45 分钟）, 7 个阅读材料, 1 个测验

7 个视频

Machine Learning Overview8分钟

Categories Of Machine Learning Techniques7分钟

Machine Learning Process3分钟

Goals and Activities in the Machine Learning Process10分钟

CRISP-DM5分钟

Scaling Up Machine Learning Algorithms5分钟

Tools Used in this Course5分钟

7 个阅读材料

Slides: Machine Learning Overview and Applications25分钟

Downloading, Installing and Using KNIME1小时

Downloading and Installing the Cloudera VM Instructions (Windows)10分钟

Downloading and Installing the Cloudera VM Instructions (Mac)10分钟

Instructions for Downloading Hands On Datasets10分钟

Instructions for Starting Jupyter10分钟

PDFs of Readings for Week 1 Hands-On10分钟

1 个练习

Machine Learning Overview20分钟

周

周 2

完成时间为 3 小时

Data Exploration

完成时间为 3 小时

6 个视频（总计 39 分钟）, 5 个阅读材料, 2 个测验

6 个视频

Data Terminology4分钟

Data Exploration4分钟

Data Exploration through Summary Statistics7分钟

Data Exploration through Plots8分钟

Exploring Data with KNIME Plots9分钟

Data Exploration in Spark5分钟

5 个阅读材料

Slides: Data Exploration Overview and Terminology10分钟

Description of Daily Weather Dataset10分钟

Exploring Data with KNIME Plots40分钟

Data Exploration in Spark10分钟

PDFs of Activities for Data Exploration Hands-On Readings10分钟

2 个练习

Data Exploration20分钟

Data Exploration in KNIME and Spark Quiz20分钟

完成时间为 3 小时

Data Preparation

完成时间为 3 小时

8 个视频（总计 42 分钟）, 4 个阅读材料, 2 个测验

8 个视频

Data Preparation3分钟

Data Quality4分钟

Addressing Data Quality Issues4分钟

Feature Selection5分钟

Feature Transformation5分钟

Dimensionality Reduction7分钟

Handling Missing Values in KNIME5分钟

Handling Missing Values in Spark5分钟

4 个阅读材料

Slides: Data Preparation for Machine Learning30分钟

Handling Missing Values in KNIME20分钟

Handling Missing Values in Spark10分钟

PDFs for Data Preparation Hands-On Readings10分钟

2 个练习

Data Preparation25分钟

Handling Missing Values in KNIME and Spark Quiz20分钟

周

周 3

完成时间为 5 小时

Classification

完成时间为 5 小时

8 个视频（总计 60 分钟）, 7 个阅读材料, 2 个测验

8 个视频

Classification4分钟

Building and Applying a Classification Model5分钟

Classification Algorithms2分钟

k-Nearest Neighbors4分钟

Decision Trees13分钟

Naïve Bayes14分钟

Classification using Decision Tree in KNIME8分钟

Classification in Spark6分钟

7 个阅读材料

Slides: What is Classification?10分钟

Slides: Classification Algorithms10分钟

Classification using Decision Tree in KNIME45分钟

Interpreting a Decision Tree in KNIME20分钟

Instructions for Changing the Number of Cloudera VM CPUs10分钟

Classification in Spark45分钟

PDFs for Classification Hands-On Readings10分钟

2 个练习

Classification20分钟

Classification in KNIME and Spark Quiz30分钟

周

周 4

完成时间为 3 小时

Evaluation of Machine Learning Models

完成时间为 3 小时

7 个视频（总计 42 分钟）, 7 个阅读材料, 2 个测验

7 个视频

Generalization and Overfitting5分钟

Overfitting in Decision Trees3分钟

Using a Validation Set9分钟

Metrics to Evaluate Model Performance10分钟

Confusion Matrix7分钟

Evaluation of Decision Tree in KNIME3分钟

Evaluation of Decision Tree in Spark2分钟

7 个阅读材料

Slides: Overfitting: What is it and how would you prevent it?10分钟

Slides: Model evaluation metrics and methods10分钟

Evaluation of Decision Tree in KNIME30分钟

Completed KNIME Workflows10分钟

Evaluation of Decision Tree in Spark20分钟

Comparing Classification Results for KNIME and Spark10分钟

PDFs for Evaluation of Machine Learning Models Hands-On Readings10分钟

2 个练习

Model Evaluation20分钟

Model Evaluation in KNIME and Spark Quiz30分钟

审阅

4.6

507 条评论

5 stars
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4 stars
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3 stars
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2 stars
1.03%
1 star
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来自基于大数据的机器学习的热门评论

由 RR 提供Dec 24, 2018

The Course was great giving a good overview of all Machine Learning Concepts. It is good starting point to understand Basics and Deep dive into Learning.

由 JK 提供Jun 27, 2021

Good content and delivery. Just make sure to follow instructions on cloudera VM config based on inputs in the forum. Hopefully the contents and tools will get updated.

由 JB 提供Oct 30, 2018

Reasonable overview. The VM environment is a major challenge for my hardware. Takes more time to make it work than it should. I am wondering if a cloud solution e.g. GCP would be better.

由 LB 提供Oct 18, 2017

This is by far the most enjoyable course of the big data specialization, i loved the teaching approach and enjoyed all the hands on exercises. Great job!

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关于大数据专项课程

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.

********* Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

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