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.
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 |
- Video: Welcome to Machine Learning With Big Data
- Video: Summary of Big Data Integration and Processing
- Discussion Prompt: Getting to Know You: Tell us about yourself and why you are taking this course.
- Discussion Prompt: Discussion Forum for Course Content Issues
- Video: Machine Learning Overview
- Video: Categories Of Machine Learning Techniques
- Reading: Slides: Machine Learning Overview and Applications
- Discussion Prompt: Machine Learning in Everyday Life
- Video: Machine Learning Process
- Video: Goals and Activities in the Machine Learning Process
- Video: CRISP-DM
- Video: Scaling Up Machine Learning Algorithms
- Video: Tools Used in this Course
- Reading: Downloading, Installing and Using KNIME
- Reading: Downloading and Installing the Cloudera VM Instructions (Windows)
- Reading: Downloading and Installing the Cloudera VM Instructions (Mac)
- Reading: Instructions for Downloading Hands On Datasets
- Reading: Instructions for Starting Jupyter
- Reading: PDFs of Readings for Week 1 Hands-On
- Video: Data Terminology
- Video: Data Exploration
- Video: Data Exploration through Summary Statistics
- Video: Data Exploration through Plots
- Discussion Prompt: What's Wrong with Pie Charts?
- Reading: Slides: Data Exploration Overview and Terminology
- Reading: Description of Daily Weather Dataset
- Reading: Exploring Data with KNIME Plots
- Video: Exploring Data with KNIME Plots
- Reading: Data Exploration in Spark
- Video: Data Exploration in Spark
- Reading: PDFs of Activities for Data Exploration Hands-On Readings
- Video: Data Preparation
- Video: Data Quality
- Discussion Prompt: Quality Issues with Real Data
- Video: Addressing Data Quality Issues
- Video: Feature Selection
- Video: Feature Transformation
- Video: Dimensionality Reduction
- Discussion Prompt: Domain Knowledge in Data Preparation
- Reading: Slides: Data Preparation for Machine Learning
- Reading: Handling Missing Values in KNIME
- Video: Handling Missing Values in KNIME
- Reading: Handling Missing Values in Spark
- Video: Handling Missing Values in Spark
- Reading: PDFs for Data Preparation Hands-On Readings
- Video: Classification
- Video: Building and Applying a Classification Model
- Reading: Slides: What is Classification?
- Video: Classification Algorithms
- Video: k-Nearest Neighbors
- Video: Decision Trees
- Video: Naïve Bayes
- Reading: Slides: Classification Algorithms
- Reading: Classification using Decision Tree in KNIME
- Video: Classification using Decision Tree in KNIME
- Reading: Interpreting a Decision Tree in KNIME
- Reading: Instructions for Changing the Number of Cloudera VM CPUs
- Reading: Classification in Spark
- Video: Classification in Spark
- Discussion Prompt: Why Exclude Relative Humidity?
- Reading: PDFs for Classification Hands-On Readings
- Video: Generalization and Overfitting
- Video: Overfitting in Decision Trees
- Video: Using a Validation Set
- Reading: Slides: Overfitting: What is it and how would you prevent it?
- Video: Metrics to Evaluate Model Performance
- Video: Confusion Matrix
- Discussion Prompt: Model Interpretability vs. Accuracy
- Reading: Slides: Model evaluation metrics and methods
- Reading: Evaluation of Decision Tree in KNIME
- Video: Evaluation of Decision Tree in KNIME
- Reading: Completed KNIME Workflows
- Reading: Evaluation of Decision Tree in Spark
- Video: Evaluation of Decision Tree in Spark
- Reading: Comparing Classification Results for KNIME and Spark
- Reading: PDFs for Evaluation of Machine Learning Models Hands-On Readings
- Video: Regression Overview
- Video: Linear Regression
- Reading: Slides: Regression
- Video: Cluster Analysis
- Video: k-Means Clustering
- Reading: Slides: Cluster Analysis
- Discussion Prompt: Clustering Applications
- Video: Association Analysis
- Video: Association Analysis in Detail
- Reading: Slides: Association Analysis
- Discussion Prompt: Applications of Association Analysis
- Video: Machine Learning With Big Data - Final Remarks
- Reading: Description of Minute Weather Dataset
- Reading: Cluster Analysis in Spark
- Video: Cluster Analysis in Spark
- Reading: PDFs of Cluster Analysis in Spark Hands-On Readings
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
It's been a little difficult but I am happy, I will take a machine learning course with R. I used databricks for processing since my Mac it's old, databricks it's free and nice but I couldn't run the libraries matplotlib parallels plot.
This is a good course to understand how we can apply basic ML algorithm like classification, clustering using KNIME and Spark ML on very high level.
A very good course to begin with.
Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.
EA
Great Course and tools