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.
Offered By
Big Data SpecializationUniversity of California San DiegoAbout this Course
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Learner Career Outcomes
42%
started a new career after completing these courses
41%
got a tangible career benefit from this course
14%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 22 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
Skills you will gain
Machine Learning ConceptsKnimeMachine LearningApache Spark
Learner Career Outcomes
42%
started a new career after completing these courses
41%
got a tangible career benefit from this course
14%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 22 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
Offered by
University of California San Diego
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.
Syllabus - What you will learn from this course
1 hour to complete
Welcome
1 hour to complete
2 videos (Total 14 min)
2 videos
Summary of Big Data Integration and Processing10m
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
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
3 hours to complete
Data Exploration
3 hours to complete
6 videos (Total 39 min), 5 readings, 2 quizzes
6 videos
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 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
5 hours to complete
Classification
5 hours to complete
8 videos (Total 60 min), 7 readings, 2 quizzes
8 videos
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
3 hours to complete
Evaluation of Machine Learning Models
3 hours to complete
7 videos (Total 42 min), 7 readings, 2 quizzes
7 videos
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
TOP REVIEWS FROM MACHINE LEARNING WITH BIG DATA
by JGOct 24, 2020
Excellent course. It teaches the basics with a great method and with practical exercises, involving real data. The sctructure is clear and it covers a good amount of topics. Well done San Diego!
by JCDec 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.
by MMJan 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.
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.
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.
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