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.
提供方
课程信息
60,391 次近期查看
学生职业成果
42%
完成这些课程后已开始新的职业生涯
41%
通过此课程获得实实在在的工作福利
14%
加薪或升职
您将获得的技能
Machine Learning ConceptsKnimeMachine LearningApache Spark
学生职业成果
42%
完成这些课程后已开始新的职业生涯
41%
通过此课程获得实实在在的工作福利
14%
加薪或升职
100% 在线
立即开始，按照自己的计划学习。
第 4 门课程（共 6 门）
可灵活调整截止日期
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完成时间大约为16 小时
建议：5 Weeks, 3 - 5 hours per week
英语（English）
字幕：英语（English）, 中文（简体）
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 24 分钟
Welcome
完成时间为 24 分钟
2 个视频 （总计 14 分钟）
完成时间为 3 小时
Introduction to Machine Learning with Big Data
完成时间为 3 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
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
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 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 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
完成时间为 4 小时
Classification
完成时间为 4 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
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 Quiz16分钟
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
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 Quiz16分钟
审阅
4.6
来自基于大数据的机器学习的热门评论
由 JC 提供Dec 8, 2018
Interesting material. Ran into several issues with the hands on that could have been avoided. Loved learning more about Neo4J. The section on Spark needed more time and additional descriptions.
由 MM 提供Jan 29, 2017
This is a valuable course in learning the basics of machine learning. but I was hoping to to get more hands on practice in this course. but overall, i learned a lot with the existing content.
由 JA 提供May 9, 2017
Great overview about the machine learning in general. There are still lots not covered specially the Neural Network algorithms. Learning Spark MLlib was great advantage of this course.
由 PR 提供Jul 18, 2018
Excellent course, I learned a lot about machine learning with big data, but most importantly I feel ready to take it into more complex level although I realized there is lots to learn.
由 BK 提供Mar 5, 2020
This is starting course for Machine Learning. Very well explained and after finishing this course, one will get interest in continuing and exploring further in Machine Learning field.
由 TA 提供Dec 26, 2019
The fact that the assignments are graded means that there’s incentive to work on them, solve problems, and ask questions. Traditional online courses don’t offer that incentive.
由 DD 提供Sep 6, 2017
Hands n exercises and corresponding quizzes are great !Content could be more detailed, but may be I felt it so given my past exposure to ML. I enjoyed learning Knime and Spark.
由 BC 提供Feb 15, 2020
This has given me foundational knowledge in Machine Learning for someone who is not a practitioner. Thank you Mai Nguyen for the excellent course presentation. Keep it up.
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.
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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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