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.
課程信息
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學生職業成果
42%
完成這些課程後已開始新的職業生涯
41%
通過此課程獲得實實在在的工作福利
14%
加薪或升職
您將獲得的技能
Machine Learning ConceptsKnimeMachine LearningApache Spark
學生職業成果
42%
完成這些課程後已開始新的職業生涯
41%
通過此課程獲得實實在在的工作福利
14%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
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第 4 門課程（共 6 門）
可靈活調整截止日期
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完成時間大約為16 小時
建議：5 Weeks, 3 - 5 hours per week
英語（English）
字幕：英語（English）, 韓語, 中文（簡體）
教學大綱 - 您將從這門課程中學到什麼
完成時間為 24 分鐘
Welcome
完成時間為 24 分鐘
2 個視頻 （總計 14 分鐘）
完成時間為 3 小時
Introduction to Machine Learning with Big Data
完成時間為 3 小時
7 個視頻 （總計 45 分鐘）, 7 個閱讀材料, 1 個測驗
7 個視頻
Categories Of Machine Learning Techniques7分鐘
Machine Learning Process3分鐘
Goals and Activities in the Machine Learning Process10分鐘
CRISP-DM5分鐘
Scaling Up Machine Learning Algorithms5分鐘
Tools Used in this Course5分鐘
7 個閱讀材料
Slides: Machine Learning Overview and Applications25分鐘
Downloading, Installing and Using KNIME1小時
Downloading and Installing the Cloudera VM Instructions (Windows)10分鐘
Downloading and Installing the Cloudera VM Instructions (Mac)10分鐘
Instructions for Downloading Hands On Datasets10分鐘
Instructions for Starting Jupyter10分鐘
PDFs of Readings for Week 1 Hands-On10分鐘
1 個練習
Machine Learning Overview20分鐘
完成時間為 3 小時
Data Exploration
完成時間為 3 小時
6 個視頻 （總計 39 分鐘）, 5 個閱讀材料, 2 個測驗
6 個視頻
Data Exploration4分鐘
Data Exploration through Summary Statistics7分鐘
Data Exploration through Plots8分鐘
Exploring Data with KNIME Plots9分鐘
Data Exploration in Spark5分鐘
5 個閱讀材料
Slides: Data Exploration Overview and Terminology10分鐘
Description of Daily Weather Dataset10分鐘
Exploring Data with KNIME Plots40分鐘
Data Exploration in Spark10分鐘
PDFs of Activities for Data Exploration Hands-On Readings10分鐘
2 個練習
Data Exploration20分鐘
Data Exploration in KNIME and Spark Quiz20分鐘
完成時間為 3 小時
Data Preparation
完成時間為 3 小時
8 個視頻 （總計 42 分鐘）, 4 個閱讀材料, 2 個測驗
8 個視頻
Data Quality4分鐘
Addressing Data Quality Issues4分鐘
Feature Selection5分鐘
Feature Transformation5分鐘
Dimensionality Reduction7分鐘
Handling Missing Values in KNIME5分鐘
Handling Missing Values in Spark5分鐘
4 個閱讀材料
Slides: Data Preparation for Machine Learning30分鐘
Handling Missing Values in KNIME20分鐘
Handling Missing Values in Spark10分鐘
PDFs for Data Preparation Hands-On Readings10分鐘
2 個練習
Data Preparation25分鐘
Handling Missing Values in KNIME and Spark Quiz20分鐘
完成時間為 4 小時
Classification
完成時間為 4 小時
8 個視頻 （總計 60 分鐘）, 7 個閱讀材料, 2 個測驗
8 個視頻
Building and Applying a Classification Model5分鐘
Classification Algorithms2分鐘
k-Nearest Neighbors4分鐘
Decision Trees13分鐘
Naïve Bayes14分鐘
Classification using Decision Tree in KNIME8分鐘
Classification in Spark6分鐘
7 個閱讀材料
Slides: What is Classification?10分鐘
Slides: Classification Algorithms10分鐘
Classification using Decision Tree in KNIME45分鐘
Interpreting a Decision Tree in KNIME20分鐘
Instructions for Changing the Number of Cloudera VM CPUs10分鐘
Classification in Spark45分鐘
PDFs for Classification Hands-On Readings10分鐘
2 個練習
Classification20分鐘
Classification in KNIME and Spark Quiz16分鐘
完成時間為 3 小時
Evaluation of Machine Learning Models
完成時間為 3 小時
7 個視頻 （總計 42 分鐘）, 7 個閱讀材料, 2 個測驗
7 個視頻
Overfitting in Decision Trees3分鐘
Using a Validation Set9分鐘
Metrics to Evaluate Model Performance10分鐘
Confusion Matrix7分鐘
Evaluation of Decision Tree in KNIME3分鐘
Evaluation of Decision Tree in Spark2分鐘
7 個閱讀材料
Slides: Overfitting: What is it and how would you prevent it?10分鐘
Slides: Model evaluation metrics and methods10分鐘
Evaluation of Decision Tree in KNIME30分鐘
Completed KNIME Workflows10分鐘
Evaluation of Decision Tree in Spark20分鐘
Comparing Classification Results for KNIME and Spark10分鐘
PDFs for Evaluation of Machine Learning Models Hands-On Readings10分鐘
2 個練習
Model Evaluation20分鐘
Model Evaluation in KNIME and Spark Quiz16分鐘
審閱
4.6
來自基于大数据的机器学习的熱門評論
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 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 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 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 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 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 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.
關於 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.
關於 大数据 專項課程
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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