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.
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About this Course
최근 조회 61,981회
학습자 경력 결과
42%
이 강좌를 수료한 후 새로운 경력 시작하기
41%
이 강좌를 통해 확실한 경력상 이점 얻기
14%
급여 인상 또는 승진하기
귀하가 습득할 기술
Machine Learning ConceptsKnimeMachine LearningApache Spark
학습자 경력 결과
42%
이 강좌를 수료한 후 새로운 경력 시작하기
41%
이 강좌를 통해 확실한 경력상 이점 얻기
14%
급여 인상 또는 승진하기
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 6개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
완료하는 데 약 16시간 필요
권장: 5 Weeks, 3 - 5 hours per week
영어
자막: 영어, 중국어 (간체자)
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 24분 필요
Welcome
완료하는 데 24분 필요
2개 동영상 (총 14분)
완료하는 데 3시간 필요
Introduction to Machine Learning with Big Data
완료하는 데 3시간 필요
7개 동영상 (총 45분), 7 readings, 1 quiz
7개의 동영상
Categories Of Machine Learning Techniques7m
Machine Learning Process3m
Goals and Activities in the Machine Learning Process10m
CRISP-DM5m
Scaling Up Machine Learning Algorithms5m
Tools Used in this Course5m
7개의 읽기 자료
Slides: Machine Learning Overview and Applications25m
Downloading, Installing and Using KNIME1h
Downloading and Installing the Cloudera VM Instructions (Windows)10m
Downloading and Installing the Cloudera VM Instructions (Mac)10m
Instructions for Downloading Hands On Datasets10m
Instructions for Starting Jupyter10m
PDFs of Readings for Week 1 Hands-On10m
1개 연습문제
Machine Learning Overview20m
완료하는 데 3시간 필요
Data Exploration
완료하는 데 3시간 필요
6개 동영상 (총 39분), 5 readings, 2 quizzes
6개의 동영상
Data Exploration4m
Data Exploration through Summary Statistics7m
Data Exploration through Plots8m
Exploring Data with KNIME Plots9m
Data Exploration in Spark5m
5개의 읽기 자료
Slides: Data Exploration Overview and Terminology10m
Description of Daily Weather Dataset10m
Exploring Data with KNIME Plots40m
Data Exploration in Spark10m
PDFs of Activities for Data Exploration Hands-On Readings10m
2개 연습문제
Data Exploration20m
Data Exploration in KNIME and Spark Quiz20m
완료하는 데 3시간 필요
Data Preparation
완료하는 데 3시간 필요
8개 동영상 (총 42분), 4 readings, 2 quizzes
8개의 동영상
Data Quality4m
Addressing Data Quality Issues4m
Feature Selection5m
Feature Transformation5m
Dimensionality Reduction7m
Handling Missing Values in KNIME5m
Handling Missing Values in Spark5m
4개의 읽기 자료
Slides: Data Preparation for Machine Learning30m
Handling Missing Values in KNIME20m
Handling Missing Values in Spark10m
PDFs for Data Preparation Hands-On Readings10m
2개 연습문제
Data Preparation25m
Handling Missing Values in KNIME and Spark Quiz20m
완료하는 데 4시간 필요
Classification
완료하는 데 4시간 필요
8개 동영상 (총 60분), 7 readings, 2 quizzes
8개의 동영상
Building and Applying a Classification Model5m
Classification Algorithms2m
k-Nearest Neighbors4m
Decision Trees13m
Naïve Bayes14m
Classification using Decision Tree in KNIME8m
Classification in Spark6m
7개의 읽기 자료
Slides: What is Classification?10m
Slides: Classification Algorithms10m
Classification using Decision Tree in KNIME45m
Interpreting a Decision Tree in KNIME20m
Instructions for Changing the Number of Cloudera VM CPUs10m
Classification in Spark45m
PDFs for Classification Hands-On Readings10m
2개 연습문제
Classification20m
Classification in KNIME and Spark Quiz16m
완료하는 데 3시간 필요
Evaluation of Machine Learning Models
완료하는 데 3시간 필요
7개 동영상 (총 42분), 7 readings, 2 quizzes
7개의 동영상
Overfitting in Decision Trees3m
Using a Validation Set9m
Metrics to Evaluate Model Performance10m
Confusion Matrix7m
Evaluation of Decision Tree in KNIME3m
Evaluation of Decision Tree in Spark2m
7개의 읽기 자료
Slides: Overfitting: What is it and how would you prevent it?10m
Slides: Model evaluation metrics and methods10m
Evaluation of Decision Tree in KNIME30m
Completed KNIME Workflows10m
Evaluation of Decision Tree in Spark20m
Comparing Classification Results for KNIME and Spark10m
PDFs for Evaluation of Machine Learning Models Hands-On Readings10m
2개 연습문제
Model Evaluation20m
Model Evaluation in KNIME and Spark Quiz16m
검토
4.6
MACHINE LEARNING WITH BIG DATA의 최상위 리뷰
JC 제공Dec 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.
MM 제공Jan 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.
PR 제공Jul 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.
JA 제공May 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.
BK 제공Mar 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.
DD 제공Sep 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.
TA 제공Dec 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.
BC 제공Feb 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.
SN 제공Dec 3, 2017
The precise definitions for many commonly used terms were very helpful. You do not find these details in many books or documents. Also, using KNIME was also interesting
NP 제공Jul 2, 2019
This course provides you starting point to start working in KNIME and helps you in explaining basic statistics concepts along with its application in KNIME and Spark
LO 제공Feb 27, 2017
Very well explained course on Machine Learning. I am grateful for the highly insightful course like this. I will recommend this course for all data enthusiasts.
PU 제공Aug 24, 2018
This course strikes the right balance between theory and practice of introductory ML. The instructors have done a tremendous job of presenting the material.
LB 제공Oct 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!
RR 제공Dec 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.
NJ 제공Mar 25, 2018
Very good blend of Machine learning and BigData. I like KNIME usage during this course, this helped me to practice without getting too much in to coding
VC 제공Sep 11, 2017
This is a good course to understand how we can apply basic ML algorithm like classification, clustering using KNIME and Spark ML on very high level.
BP 제공Apr 19, 2017
Really fun, glad I finally finished. I got so distracted running off and learning things. I got familiar with scala and python along the way too.
AK 제공Aug 18, 2017
Love this course , given me good understanding of Machine Learning , Also introduced two tools - KNIME and Spark Mlib with really good hands on
FG 제공Jul 25, 2017
Wonderful teacher! Excellent course! But if you really want to learn Machine learning with Big Data, you have to work yourself on a project
WC 제공Sep 24, 2019
Excellent course!!!!\n\nI learned a lot. The course structure, topics, explanations and activities were very precise. Thanks to the tutors.
캘리포니아 샌디에고 대학교 정보
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.
빅 데이터 전문 분야 정보
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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