Machine Learning With Big Data

About this course: 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.

Who is this class for: This course is for those new to data science. Completion of “Big Data Integration and Processing” is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications.

Created by:  University of California, San Diego

• Taught by:  Mai Nguyen, Lead for Data Analytics

  San Diego Supercomputer Center

• Taught by:  Ilkay Altintas, Chief Data Science Officer

  San Diego Supercomputer Center

Basic Info Course 4 of 6 in the Big Data Specialization Commitment 5 Weeks, 3 - 5 hours per week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.5 stars Average User Rating 4.5See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from your peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

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


WEEK 1


Welcome



2 videos expand

1. Video: Welcome to Machine Learning With Big Data
2. Video: Summary of Big Data Integration and Processing
3. Discussion Prompt: Getting to Know You: Tell us about yourself and why you are taking this course.
4. Discussion Prompt: Discussion Forum for Course Content Issues

Introduction to Machine Learning with Big Data



7 videos, 7 readings expand

1. Video: Machine Learning Overview
2. Video: Categories Of Machine Learning Techniques
3. Reading: Slides: Machine Learning Overview and Applications
4. Discussion Prompt: Machine Learning in Everyday Life
5. Video: Machine Learning Process
6. Video: Goals and Activities in the Machine Learning Process
7. Video: CRISP-DM
8. Video: Scaling Up Machine Learning Algorithms
9. Video: Tools Used in this Course
10. Reading: Downloading, Installing and Using KNIME
11. Reading: Downloading and Installing the Cloudera VM Instructions (Windows)
12. Reading: Downloading and Installing the Cloudera VM Instructions (Mac)
13. Reading: Instructions for Downloading Hands On Datasets
14. Reading: Instructions for Starting Jupyter
15. Reading: PDFs of Readings for Week 1 Hands-On

Graded: Machine Learning Overview

WEEK 2


Data Exploration



6 videos, 5 readings expand

1. Video: Data Terminology
2. Video: Data Exploration
3. Video: Data Exploration through Summary Statistics
4. Video: Data Exploration through Plots
5. Discussion Prompt: What's Wrong with Pie Charts?
6. Reading: Slides: Data Exploration Overview and Terminology
7. Reading: Description of Daily Weather Dataset
8. Reading: Exploring Data with KNIME Plots
9. Video: Exploring Data with KNIME Plots
10. Reading: Data Exploration in Spark
11. Video: Data Exploration in Spark
12. Reading: PDFs of Activities for Data Exploration Hands-On Readings

Graded: Data Exploration
Graded: Data Exploration in KNIME and Spark Quiz

Data Preparation



8 videos, 4 readings expand

1. Video: Data Preparation
2. Video: Data Quality
3. Discussion Prompt: Quality Issues with Real Data
4. Video: Addressing Data Quality Issues
5. Video: Feature Selection
6. Video: Feature Transformation
7. Video: Dimensionality Reduction
8. Discussion Prompt: Domain Knowledge in Data Preparation
9. Reading: Slides: Data Preparation for Machine Learning
10. Reading: Handling Missing Values in KNIME
11. Video: Handling Missing Values in KNIME
12. Reading: Handling Missing Values in Spark
13. Video: Handling Missing Values in Spark
14. Reading: PDFs for Data Preparation Hands-On Readings

Graded: Data Preparation
Graded: Handling Missing Values in KNIME and Spark Quiz

WEEK 3


Classification



8 videos, 7 readings expand

1. Video: Classification
2. Video: Building and Applying a Classification Model
3. Reading: Slides: What is Classification?
4. Video: Classification Algorithms
5. Video: k-Nearest Neighbors
6. Video: Decision Trees
7. Video: Naïve Bayes
8. Reading: Slides: Classification Algorithms
9. Reading: Classification using Decision Tree in KNIME
10. Video: Classification using Decision Tree in KNIME
11. Reading: Interpreting a Decision Tree in KNIME
12. Reading: Instructions for Changing the Number of Cloudera VM CPUs
13. Reading: Classification in Spark
14. Video: Classification in Spark
15. Discussion Prompt: Why Exclude Relative Humidity?
16. Reading: PDFs for Classification Hands-On Readings

Graded: Classification
Graded: Classification in KNIME and Spark Quiz

WEEK 4


Evaluation of Machine Learning Models



7 videos, 7 readings expand

1. Video: Generalization and Overfitting
2. Video: Overfitting in Decision Trees
3. Video: Using a Validation Set
4. Reading: Slides: Overfitting: What is it and how would you prevent it?
5. Video: Metrics to Evaluate Model Performance
6. Video: Confusion Matrix
7. Discussion Prompt: Model Interpretability vs. Accuracy
8. Reading: Slides: Model evaluation metrics and methods
9. Reading: Evaluation of Decision Tree in KNIME
10. Video: Evaluation of Decision Tree in KNIME
11. Reading: Completed KNIME Workflows
12. Reading: Evaluation of Decision Tree in Spark
13. Video: Evaluation of Decision Tree in Spark
14. Reading: Comparing Classification Results for KNIME and Spark
15. Reading: PDFs for Evaluation of Machine Learning Models Hands-On Readings

Graded: Model Evaluation
Graded: Model Evaluation in KNIME and Spark Quiz

WEEK 5


Regression, Cluster Analysis, and Association Analysis



8 videos, 6 readings expand

1. Video: Regression Overview
2. Video: Linear Regression
3. Reading: Slides: Regression
4. Video: Cluster Analysis
5. Video: k-Means Clustering
6. Reading: Slides: Cluster Analysis
7. Discussion Prompt: Clustering Applications
8. Video: Association Analysis
9. Video: Association Analysis in Detail
10. Reading: Slides: Association Analysis
11. Discussion Prompt: Applications of Association Analysis
12. Video: Machine Learning With Big Data - Final Remarks
13. Reading: Description of Minute Weather Dataset
14. Reading: Cluster Analysis in Spark
15. Video: Cluster Analysis in Spark
16. Reading: PDFs of Cluster Analysis in Spark Hands-On Readings

Graded: Regression, Cluster Analysis, & Association Analysis
Graded: Cluster Analysis in Spark Quiz