About 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
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. 20 hours to complete
English
Subtitles: English, Korean, Chinese (Simplified)

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. 20 hours to complete
English
Subtitles: English, Korean, Chinese (Simplified)

Offered by

University of California San Diego logo

University of California San Diego

Syllabus - What you will learn from this course

Content RatingThumbs Up96%(5,400 ratings)Info
Week
1

Week 1

24 minutes to complete

Welcome

24 minutes to complete
2 videos (Total 14 min)
2 videos
Summary of Big Data Integration and Processing10m
3 hours to complete

Introduction to Machine Learning with Big Data

3 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
Week
2

Week 2

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
Week
3

Week 3

4 hours to complete

Classification

4 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 Quiz16m
Week
4

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
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 Quiz16m

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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....
Big Data

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