Overview:

This course includes video lectures, assessments, and hands-on exercise access. The course provides an introduction to Machine Learning, including coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Delivery Method and Course Duration:

OnDemand: 180 days

Classroom: 1 day

Objectives:

Through instructor-led discussion, as well as hands-on exercises, participants will learn topics including:

• Data types, statistics support, feature extraction, transforming vectors, using the StandardScaler class
• An overview of dimensionality reduction
• Machine learning models, regression, linear regression support, and regularization. 
• Finally, the course discusses machine learning with Spark ML topics such as using data frames, transformers and estimators, an introduction to pipelines, using pipelines to generate models, and regularization

Intended Audience & Prerequisites:

Introduction to Machine Learning does not have prerequisites, but student must know Python or Scala to understand the material covered. .

Please note that this course does not teach big data concepts, nor does it cover how to use Cloudera software. Instead, it is meant as a follow up to our Developer Training for Spark and Hadoop course

Course outlines:

1.      Machine Learning Overview

• Introduction
• Collaborative Filtering
• Clustering
• Classification
• Relationship of Algorithms and Data Volume

2.      Machine Learning with Spark MLlib

• Introduction
• Data Types
• Basic Statistics
• Feature Extraction
• Dimensionality Reduction
• Models
• Regression

3.      Machine Learning with Spark ML

• Overview of Spark ML
• DataFrames
• Transformers and Estimators
• Pipelines
• Decision Tree Classifiers
• k-Means Clustering