Welcome-Machine Learning with Big Data
Welcome to Machine Learning With Big Data
()
Summary of Big Data Integration and Processing
()
Introduction to Machine Learning with Big Data-Machine Learning Overview and Applications
Machine Learning Overview
()
Categories Of Machine Learning Techniques
()
Slides: Machine Learning Overview and Applications
Introduction to Machine Learning with Big Data-Machine Learning Process
Machine Learning Process
()
Goals and Activities in the Machine Learning Process
()
CRISP-DM
()
Introduction to Machine Learning with Big Data-Scalability and Tools
Scaling Up Machine Learning Algorithms
()
Tools Used in this Course
()
Introduction to Machine Learning with Big Data-Hands-On: Setting Up Your Software Environment
Downloading, Installing and Using KNIME
Downloading and Installing the Cloudera VM Instructions (Windows)
Downloading and Installing the Cloudera VM Instructions (Mac)
Instructions for Downloading Hands On Datasets
Instructions for Starting Jupyter
PDFs of Readings for Week 1 Hands-On
Data Exploration-Data Exploration
Data Terminology
()
Data Exploration
()
Data Exploration through Summary Statistics
()
Data Exploration through Plots
()
Slides: Data Exploration Overview and Terminology
Data Exploration-Hands-On: Activities for Data Exploration
Description of Daily Weather Dataset
Exploring Data with KNIME Plots
Exploring Data with KNIME Plots
()
Data Exploration in Spark
Data Exploration in Spark
()
PDFs of Activities for Data Exploration Hands-On Readings
Data Preparation-Data Preparation for Machine Learning
Data Preparation
()
Data Quality
()
Addressing Data Quality Issues
()
Feature Selection
()
Feature Transformation
()
Dimensionality Reduction
()
Slides: Data Preparation for Machine Learning
Data Preparation-Hands-On
Handling Missing Values in KNIME
Handling Missing Values in KNIME
()
Handling Missing Values in Spark
Handling Missing Values in Spark
()
PDFs for Data Preparation Hands-On Readings
Classification-What is Classification?
Classification
()
Building and Applying a Classification Model
()
Slides: What is Classification?
Classification-Classification Algorithms
Classification Algorithms
()
k-Nearest Neighbors
()
Decision Trees
()
Naïve Bayes
()
Slides: Classification Algorithms
Classification-Hands-On
Classification using Decision Tree in KNIME
Classification using Decision Tree in KNIME
()
Interpreting a Decision Tree in KNIME
Instructions for Changing the Number of Cloudera VM CPUs
Classification in Spark
Classification in Spark
()
PDFs for Classification Hands-On Readings
Evaluation of Machine Learning Models-Overfitting: What is it and how would you prevent it?
Generalization and Overfitting
()
Overfitting in Decision Trees
()
Using a Validation Set
()
Slides: Overfitting: What is it and how would you prevent it?
Evaluation of Machine Learning Models-Model evaluation metrics and methods
Metrics to Evaluate Model Performance
()
Confusion Matrix
()
Slides: Model evaluation metrics and methods
Evaluation of Machine Learning Models-Hands-On
Evaluation of Decision Tree in KNIME
Evaluation of Decision Tree in KNIME
()
Completed KNIME Workflows
Evaluation of Decision Tree in Spark
Evaluation of Decision Tree in Spark
()
Comparing Classification Results for KNIME and Spark
PDFs for Evaluation of Machine Learning Models Hands-On Readings
Regression, Cluster Analysis, and Association Analysis-Regression
Regression Overview
()
Linear Regression
()
Slides: Regression
Regression, Cluster Analysis, and Association Analysis-Cluster Analysis
Cluster Analysis
()
k-Means Clustering
()
Slides: Cluster Analysis
Regression, Cluster Analysis, and Association Analysis-Association Analysis
Association Analysis
()
Association Analysis in Detail
()
Slides: Association Analysis
Machine Learning With Big Data - Final Remarks
()
Regression, Cluster Analysis, and Association Analysis-Hands-On
Description of Minute Weather Dataset
Cluster Analysis in Spark
Cluster Analysis in Spark
()
PDFs of Cluster Analysis in Spark Hands-On Readings