This course is part of the Big Data Specialization

4.6
stars
2,421 ratings

Mai Nguyen +1 more instructor

66,490 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

100% online

Start instantly and learn at your own schedule.

Course 4 of 6 in the

Big Data Specialization

Approx. 22 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.

Course 4 of 6 in the

Big Data Specialization

Approx. 22 hours to complete

English

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

Try Coursera for Business

Instructors

Instructor rating

4.8/5 (235 Ratings)

Mai Nguyen

Lead for Data Analytics

San Diego Supercomputer Center

66,490 Learners

1 Course

Ilkay Altintas

Chief Data Science Officer

San Diego Supercomputer Center

456,290 Learners

13 Courses

Offered by

University of California San Diego

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Syllabus - What you will learn from this course

Content Rating96%(6,726 ratings)

Week

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 Overview8m

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

7 readings

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

1 practice exercise

Machine Learning Overview20m

Week

Week 2

3 hours to complete

Data Exploration

3 hours to complete

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

6 videos

Data Terminology4m

Data Exploration4m

Data Exploration through Summary Statistics7m

Data Exploration through Plots8m

Exploring Data with KNIME Plots9m

Data Exploration in Spark5m

5 readings

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

2 practice exercises

Data Exploration20m

Data 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 Preparation3m

Data Quality4m

Addressing Data Quality Issues4m

Feature Selection5m

Feature Transformation5m

Dimensionality Reduction7m

Handling Missing Values in KNIME5m

Handling Missing Values in Spark5m

4 readings

Slides: Data Preparation for Machine Learning30m

Handling Missing Values in KNIME20m

Handling Missing Values in Spark10m

PDFs for Data Preparation Hands-On Readings10m

2 practice exercises

Data Preparation25m

Handling Missing Values in KNIME and Spark Quiz20m

Week

Week 3

5 hours to complete

Classification

5 hours to complete

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

8 videos

Classification4m

Building and Applying a Classification Model5m

Classification Algorithms2m

k-Nearest Neighbors4m

Decision Trees13m

Naïve Bayes14m

Classification using Decision Tree in KNIME8m

Classification in Spark6m

7 readings

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

2 practice exercises

Classification20m

Classification in KNIME and Spark Quiz30m

Week

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 Overfitting5m

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

7 readings

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

2 practice exercises

Model Evaluation20m

Model Evaluation in KNIME and Spark Quiz30m

Reviews

4.6

509 reviews

5 stars
70.34%
4 stars
23.75%
3 stars
4.13%
2 stars
1.03%
1 star
0.74%

TOP REVIEWS FROM MACHINE LEARNING WITH BIG DATA

by CANov 23, 2020

Fue un buen curso, aprender las nociones básicas de análisis de datos y diferentes tipos de algoritmos mas aun haciendo las practicas con las herramientas

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 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 RRDec 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.

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

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