This course is part of the Big Data Specialization

4.6
stars
2,423 ratings

Mai Nguyen +1 more instructor

67,455 already enrolled

Offered By

Big Data SpecializationUniversity of California San Diego

About this Course

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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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 4 of 6 in the

Big Data Specialization

Approx. 21 hours to complete

English

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Skills you will gain

Machine Learning Concepts
Knime
Machine Learning
Apache Spark

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 6 in the

Big Data Specialization

Approx. 21 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.79/5 (236 Ratings)

Mai Nguyen

Lead for Data Analytics

San Diego Supercomputer Center

67,455 Learners

1 Course

Ilkay Altintas

Chief Data Science Officer

San Diego Supercomputer Center

459,991 Learners

13 Courses

Offered by

University of California San Diego

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Syllabus - What you will learn from this course

Content Rating96%(6,751 ratings)

Week1

Week 1

1 hour to complete

Welcome

1 hour to complete

2 videos (Total 14 min)

4 hours to complete

Introduction to Machine Learning with Big Data

4 hours to complete

7 videos (Total 45 min), 7 readings, 1 quiz

7 videos

Machine Learning Overview8mCategories Of Machine Learning Techniques7mMachine Learning Process3mGoals and Activities in the Machine Learning Process10mCRISP-DM5mScaling Up Machine Learning Algorithms5mTools Used in this Course5m

7 readings

Slides: Machine Learning Overview and Applications25mDownloading, Installing and Using KNIME1hDownloading and Installing the Cloudera VM Instructions (Windows)10mDownloading and Installing the Cloudera VM Instructions (Mac)10mInstructions for Downloading Hands On Datasets10mInstructions for Starting Jupyter10mPDFs of Readings for Week 1 Hands-On10m

1 practice exercise

Machine Learning Overview20m

Week2

Week 2

3 hours to complete

Data Exploration

3 hours to complete

6 videos (Total 39 min), 5 readings, 2 quizzes

6 videos

Data Terminology4mData Exploration4mData Exploration through Summary Statistics7mData Exploration through Plots8mExploring Data with KNIME Plots9mData Exploration in Spark5m

5 readings

Slides: Data Exploration Overview and Terminology10mDescription of Daily Weather Dataset10mExploring Data with KNIME Plots40mData Exploration in Spark10mPDFs of Activities for Data Exploration Hands-On Readings10m

2 practice exercises

Data Exploration20mData Exploration in KNIME and Spark Quiz20m

3 hours to complete

Data Preparation

3 hours to complete

8 videos (Total 42 min), 4 readings, 2 quizzes

8 videos

Data Preparation3mData Quality4mAddressing Data Quality Issues4mFeature Selection5mFeature Transformation5mDimensionality Reduction7mHandling Missing Values in KNIME5mHandling Missing Values in Spark5m

4 readings

Slides: Data Preparation for Machine Learning30mHandling Missing Values in KNIME20mHandling Missing Values in Spark10mPDFs for Data Preparation Hands-On Readings10m

2 practice exercises

Data Preparation25mHandling Missing Values in KNIME and Spark Quiz20m

Week3

Week 3

5 hours to complete

Classification

5 hours to complete

8 videos (Total 60 min), 7 readings, 2 quizzes

8 videos

Classification4mBuilding and Applying a Classification Model5mClassification Algorithms2mk-Nearest Neighbors4mDecision Trees13mNaïve Bayes14mClassification using Decision Tree in KNIME8mClassification in Spark6m

7 readings

Slides: What is Classification?10mSlides: Classification Algorithms10mClassification using Decision Tree in KNIME45mInterpreting a Decision Tree in KNIME20mInstructions for Changing the Number of Cloudera VM CPUs10mClassification in Spark45mPDFs for Classification Hands-On Readings10m

2 practice exercises

Classification20mClassification in KNIME and Spark Quiz30m

Week4

Week 4

3 hours to complete

Evaluation of Machine Learning Models

3 hours to complete

7 videos (Total 42 min), 7 readings, 2 quizzes

7 videos

Generalization and Overfitting5mOverfitting in Decision Trees3mUsing a Validation Set9mMetrics to Evaluate Model Performance10mConfusion Matrix7mEvaluation of Decision Tree in KNIME3mEvaluation of Decision Tree in Spark2m

7 readings

Slides: Overfitting: What is it and how would you prevent it?10mSlides: Model evaluation metrics and methods10mEvaluation of Decision Tree in KNIME30mCompleted KNIME Workflows10mEvaluation of Decision Tree in Spark20mComparing Classification Results for KNIME and Spark10mPDFs for Evaluation of Machine Learning Models Hands-On Readings10m

2 practice exercises

Model Evaluation20mModel Evaluation in KNIME and Spark Quiz30m

Reviews

4.6

509 reviews

5 stars
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4 stars
23.77%
3 stars
4.12%
2 stars
1.03%
1 star
0.74%

TOP REVIEWS FROM MACHINE LEARNING WITH BIG DATA

by JAMay 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.

by BCFeb 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.

by JBOct 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.

by TADec 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.

View all reviews

About the Big Data Specialization

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Machine Learning With Big Data

About this Course

Skills you will gain