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Machine Learning With Big Data
  • Level Foundation
  • Duration 22 hours
  • Course by University of California San Diego
  • Offered by

About

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

Modules

Machine Learning with Big Data

    • 2 Videos
    • 2 Discussion
Show info about module content

2 Discussions

  • Getting to Know You: Tell us about yourself and why you are taking this course.
  • Discussion Forum for Course Content Issues

2 Videos

  • Welcome to Machine Learning With Big Data
  • Summary of Big Data Integration and Processing

Machine Learning Overview and Applications

    • 2 Videos
    • 1 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Machine Learning in Everyday Life

2 Videos

  • Machine Learning Overview
  • Categories Of Machine Learning Techniques

1 Readings

  • Slides: Machine Learning Overview and Applications

Machine Learning Process

    • 3 Videos
Show info about module content

3 Videos

  • Machine Learning Process
  • Goals and Activities in the Machine Learning Process
  • CRISP-DM

Scalability and Tools

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Machine Learning Overview

2 Videos

  • Scaling Up Machine Learning Algorithms
  • Tools Used in this Course

Hands-On: Setting Up Your Software Environment

    • 5 Readings
Show info about module content

5 Readings

  • Downloading and Installing Docker Desktop Instructions
  • Instroduction to Jupyter Notebooks
  • Downloading Hands-On Materials
  • Basic terminal shell commands
  • Downloading, Installing and Using KNIME

Data Exploration

    • 4 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Exploration

1 Discussions

  • What's Wrong with Pie Charts?

4 Videos

  • Data Terminology
  • Data Exploration
  • Data Exploration through Summary Statistics
  • Data Exploration through Plots

1 Readings

  • Slides: Data Exploration Overview and Terminology

Hands-On: Activities for Data Exploration

    • 2 Videos
    • 3 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Exploration in KNIME and Spark Quiz

2 Videos

  • Exploring Data with KNIME Plots
  • Data Exploration in Spark

3 Readings

  • Description of Daily Weather Dataset
  • Exploring Data with KNIME Plots
  • Data Exploration in Spark

Data Preparation for Machine Learning

    • 6 Videos
    • 1 Readings
    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Preparation

2 Discussions

  • Quality Issues with Real Data
  • Domain Knowledge in Data Preparation

6 Videos

  • Data Preparation
  • Data Quality
  • Addressing Data Quality Issues
  • Feature Selection
  • Feature Transformation
  • Dimensionality Reduction

1 Readings

  • Slides: Data Preparation for Machine Learning

Hands-On

    • 2 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Handling Missing Values in KNIME and Spark Quiz

2 Videos

  • Handling Missing Values in KNIME
  • Handling Missing Values in Spark

2 Readings

  • Handling Missing Values in KNIME
  • Handling Missing Values in Spark

What is Classification?

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Classification
  • Building and Applying a Classification Model

1 Readings

  • Slides: What is Classification?

Classification Algorithms

    • 4 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Classification

4 Videos

  • Classification Algorithms
  • k-Nearest Neighbors
  • Decision Trees
  • Naïve Bayes

1 Readings

  • Slides: Classification Algorithms

Hands-On

    • 2 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Classification in KNIME and Spark Quiz

1 Discussions

  • Why Exclude Relative Humidity?

2 Videos

  • Classification using Decision Tree in KNIME
  • Classification in Spark

3 Readings

  • Classification using Decision Tree in KNIME
  • Interpreting a Decision Tree in KNIME
  • Classification in Spark

Overfitting: What is it and how would you prevent it?

    • 3 Videos
    • 1 Readings
Show info about module content

3 Videos

  • Generalization and Overfitting
  • Overfitting in Decision Trees
  • Using a Validation Set

1 Readings

  • Slides: Overfitting: What is it and how would you prevent it?

Model evaluation metrics and methods

    • 2 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Model Evaluation

1 Discussions

  • Model Interpretability vs. Accuracy

2 Videos

  • Metrics to Evaluate Model Performance
  • Confusion Matrix

1 Readings

  • Slides: Model evaluation metrics and methods

Hands-On

    • 2 Videos
    • 4 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Model Evaluation in KNIME and Spark Quiz

2 Videos

  • Evaluation of Decision Tree in KNIME
  • Evaluation of Decision Tree in Spark

4 Readings

  • Evaluation of Decision Tree in KNIME
  • Completed KNIME Workflows
  • Evaluation of Decision Tree in Spark
  • Comparing Classification Results for KNIME and Spark

Regression

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Regression Overview
  • Linear Regression

1 Readings

  • Slides: Regression

Cluster Analysis

    • 2 Videos
    • 1 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Clustering Applications

2 Videos

  • Cluster Analysis
  • k-Means Clustering

1 Readings

  • Slides: Cluster Analysis

Association Analysis

    • 3 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Regression, Cluster Analysis, & Association Analysis

1 Discussions

  • Applications of Association Analysis

3 Videos

  • Association Analysis
  • Association Analysis in Detail
  • Machine Learning With Big Data - Final Remarks

1 Readings

  • Slides: Association Analysis

Hands-On

    • 1 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Cluster Analysis in Spark Quiz

1 Videos

  • Cluster Analysis in Spark

2 Readings

  • Description of Minute Weather Dataset
  • Cluster Analysis in Spark

Auto Summary

Embark on a journey into the world of data science with the "Machine Learning With Big Data" course, designed to help you harness the power of your data. This foundational course, created by Coursera, demystifies machine learning techniques and equips you with the skills to explore, analyze, and leverage data effectively. Guided by industry experts, you'll delve into the machine learning process, from designing data-driven approaches to applying sophisticated algorithms. Learn to prepare data for modeling, identify the right machine learning techniques, and construct models using widely available open-source tools. Additionally, you will gain expertise in scaling these models to tackle big data challenges using Spark. This comprehensive course spans 1320 minutes of immersive content, making it ideal for those new to data science and AI. Whether you're looking to start with the basics or deepen your understanding, flexible subscription options—Starter, Professional, and Paid—ensure you can learn at your own pace. Targeted at data enthusiasts eager to integrate machine learning into their decision-making processes, this course requires Cloudera VM, KNIME, and Spark software, ensuring you have the practical tools to succeed. Join now and transform the way you handle big data!

Mai Nguyen
Instructors

Mai Nguyen

Ilkay Altintas
Instructors

Ilkay Altintas