Practical Machine Learning for Cloudera Platform, United Kingdom

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Cloudera University’s one-day Introduction to Machine Learning with Spark ML and MLlib will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

There are no prerequisites for this course.

This course is intended for software engineers who have basic Linux experience in addition to experience with either the Scala or Python programming languages (code examples and exercises are presented in both languages, so students can choose whichever language they prefer).

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.

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

Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

Upcoming Trainings

Join our public courses in our United Kingdom facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

Classroom / Virtual Classroom

16 January 2025