Scala and Spark for Big Data and Machine Learning

Scala and Spark for Big Data and Machine Learning, available at $69.99, has an average rating of 4.63, with 80 lectures, based on 5398 reviews, and has 31934 subscribers.

You will learn about Use Scala for Programming Use Spark 2.0 DataFrames to read and manipulate data Use Spark to Process Large Datasets Understand hot to use Spark on AWS and DataBricks This course is ideal for individuals who are Someone who already knows how to program and is interested in learning Big Data Technologies or Interested in using Spark with Scala for Machine Learning with Large Data Sets It is particularly useful for Someone who already knows how to program and is interested in learning Big Data Technologies or Interested in using Spark with Scala for Machine Learning with Large Data Sets.

Enroll now: Scala and Spark for Big Data and Machine Learning

Summary

Title: Scala and Spark for Big Data and Machine Learning

Price: $69.99

Average Rating: 4.63

Number of Lectures: 80

Number of Published Lectures: 80

Number of Curriculum Items: 80

Number of Published Curriculum Objects: 80

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

Use Scala for Programming

Use Spark 2.0 DataFrames to read and manipulate data

Use Spark to Process Large Datasets

Understand hot to use Spark on AWS and DataBricks

Who Should Attend

Someone who already knows how to program and is interested in learning Big Data Technologies

Interested in using Spark with Scala for Machine Learning with Large Data Sets

Target Audiences

Someone who already knows how to program and is interested in learning Big Data Technologies

Interested in using Spark with Scala for Machine Learning with Large Data Sets

Learn how to utilize some?of the most valuable tech skills on the market today,?Scala and Spark! In this course we will show you how to use Scala and Spark to analyze Big Data.

Scala and Spark are two of the most in demand skills right now, and with this course you can learn them quickly and easily! This course comes packed with content:

Crash Course in Scala Programming

Spark and Big Data Ecosystem Overview

Using Spark’s MLlib for Machine Learning?

Scale up Spark jobs using Amazon Web?Services

Learn how to use Databrick’s Big Data Platform

and much more!

This course comes with full projects for you including topics such as?analyzing financial data or using machine learning to classify?Ecommerce customer behavior! We teach the latest methodologies of?Spark 2.0 so you can learn how to use SparkSQL, Spark DataFrames, and Spark’s MLlib!

After completing this course you will feel comfortable putting Scala and Spark on your resume!

Thanks and I will see you inside the course!

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction

Lecture 2: Course FAQs

Lecture 3: Scala and Spark Overview

Chapter 2: Scala IDE Options and Overview

Lecture 1: ScalaIDE Overview

Lecture 2: Computer Set-up Time!

Chapter 3: Windows Scala and Spark Set-up and Installation

Lecture 1: Windows Introduction

Lecture 2: Quick note about Windows Installation.

Lecture 3: Windows Scala and Spark Installation

Lecture 4: Atom Windows Installation

Lecture 5: Terminal Exericse

Chapter 4: Mac OS Setup and Installation

Lecture 1: Mac OS Installation and Setup

Chapter 5: Linux (Ubuntu) Setup and Installation

Lecture 1: Installing Scala and Spark on Linux (Ubuntu)

Chapter 6: Scala Programming: Level One

Lecture 1: Arithmetic and Numbers

Lecture 2: Values and Variables

Lecture 3: Booleans and Comparison Operators

Lecture 4: Strings and Basic Regex

Lecture 5: Tuples

Lecture 6: Scala Basics – Assessment Test Exercises

Lecture 7: Scala Basics Assessment Test Questions

Lecture 8: Scala Basics – Assessment Test Solutions

Chapter 7: Collections

Lecture 1: Intro to Collections

Lecture 2: Lists

Lecture 3: Arrays

Lecture 4: Sets

Lecture 5: Maps

Lecture 6: Collections – Assessment Test Exercise

Lecture 7: Scala Collections Assessment Test

Lecture 8: Collections Assessment Test – Solutions

Chapter 8: Scala Programming: Level Two

Lecture 1: Flow Control

Lecture 2: For Loops

Lecture 3: While Loops

Lecture 4: Functions

Lecture 5: Scala Programming Exercises

Lecture 6: Scala Programming Exercises – Solutions

Chapter 9: Spark DataFrames with Scala

Lecture 1: Quick Note for Windows Users!

Lecture 2: Introduction to Spark DataFrames

Lecture 3: DataFrames Overview

Lecture 4: Spark DataFrame Operations

Lecture 5: GroupBy and Aggregate Functions

Lecture 6: Missing data

Lecture 7: Date and Timestamps

Lecture 8: Quick Note on DataFrame Project

Lecture 9: DataFrame Project Exercises

Lecture 10: DataFrame Project – Solutions

Chapter 10: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Machine Learning with Spark

Lecture 3: IntelliJ IDEA Installation Overview

Chapter 11: Regression with Spark

Lecture 1: Introduction to Linear Regression

Lecture 2: Introduction to Regression Section

Lecture 3: Linear Regression Documentation Example

Lecture 4: Alternate Linear Regression Data CSV File

Lecture 5: Linear Regression Walkthrough Part 1

Lecture 6: Linear Regression Walkthrough Part 2

Lecture 7: Linear Regression Exercise Project

Lecture 8: Linear Regression Project Solutions

Chapter 12: Classification with Spark

Lecture 1: Introduction to Classification

Lecture 2: Classification Documentation Example

Lecture 3: Spark Classification – Logistic Regression Example – Part 1

Lecture 4: Spark Classification – Logistic Regression Example – Part 2

Lecture 5: Logistic Regression Project Exercise

Lecture 6: Classification Project Solutions

Chapter 13: Model Evaluation

Lecture 1: Model Evaluation Overview

Lecture 2: Spark Model Evaluation – Documentation Example

Lecture 3: Spark – Model Evaluation – Regression Example

Chapter 14: Clustering with Spark

Lecture 1: Introduction to Clustering with Spark

Lecture 2: KMeans Theory Lecture

Lecture 3: Note on Kmeans

Lecture 4: Example of KMeans with Spark

Lecture 5: Clustering Project Exercise Overview

Lecture 6: Clustering Project Exercises – Solutions

Chapter 15: PCA with Spark

Lecture 1: PCA Theory Overview

Lecture 2: PCA with Spark – Documentation Example

Lecture 3: PCA with Spark – Project Exercise

Lecture 4: PCA Spark Exercise – Solutions

Chapter 16: DataBricks and Spark

Lecture 1: Databricks Overview

Lecture 2: Introduction to Spark Recommendation Systems

Lecture 3: Spark Recommender System Implementation

Lecture 4: Zeppelin Notebooks on AWS Elastic MapReduce

Lecture 5: So whats next?

Chapter 17: BONUS SECTION: THANK YOU!

Lecture 1: Bonus Lecture:

Instructors

Jose Portilla
Head of Data Science at Pierian Training

Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 45 votes

2 stars: 95 votes

3 stars: 546 votes

4 stars: 1998 votes

5 stars: 2714 votes

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