Machine Learning with Apache Spark 3.0 using Scala

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Machine Learning with Apache Spark 3.0 using Scala with Examples and 4 Projects

What you will learn

Fundamental knowledge on Machine Learning with Apache Spark using Scala

Learn and master the art of Machine Learning through hands-on projects, and then execute them up to run on Databricks cloud computing services

You will Build Apache Spark Machine Learning Projects (Total 4 Projects)

Explore Apache Spark and Machine Learning on the Databricks platform.

Launching Spark Cluster

Create a Data Pipeline

Process that data using a Machine Learning model (Spark ML Library)

Hands-on learning

Real-time Use Case

Description

Machine Learning with Apache Spark 3.0 using Scala with Examples and Project

“Big data” analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, eBay, NASA, Yahoo, and many more. All are using Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You’ll learn those same techniques, using your own Operating system right at home.

So, What are we going to cover in this course then?

Learn and master the art of Machine Learning through hands-on projects, and then execute them up to run on Databricks cloud computing services (Free Service) in this course. Well, the course is covering topics:

1) Overview

2) What is Spark ML

3) Types of Machine Learning

4) Steps Involved in the Machine learning program

5) Basic Statics

6) Data Sources

7) Pipelines


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8) Extracting, transforming and selecting features

9) Classification and Regression

10) Clustering

Projects:

1) Will it Rain Tomorrow in Australia

2) Railway train arrival delay prediction

3) Predict the class of the Iris flower based on available attributes

4) Mall Customer Segmentation (K-means) Cluster

In order to get started with the course And to do that you’re going to have to set up your environment.

So, the first thing you’re going to need is a web browser that can be (Google Chrome or Firefox, or Safari, or Microsoft Edge (Latest version)) on Windows, Linux, and macOS desktop

This is completely Hands-on Learning with the Databricks environment.

English
language

Content

Introduction

Introduction
Overview
What is Spark ML?
Introduction to Machine Learning

Apache Spark Basics (Optional)

Introduction to Spark
Free Account creation in Databricks
Provisioning a Spark Cluster
Basics about notebooks
Why we should learn Apache Spark?
Spark RDD (Create and Display Practical)
Spark Dataframe (Create and Display Practical)
Anonymus Functions in Scala
Extra (Optional on Spark DataFrame)
Extra (Optional on Spark DataFrame) in Details
Spark Datasets (Create and Display Practical)

Apache Spark Machine Learning

Types of Machine Learning
Steps Involved in Machine Learning Program
Spark MLlib
Importing Notebook and Data Upload
Basic statistics Correlation
Data Sources
Data Source CSV File
Data Source JSON File
Data Source LIBSVM File
Data Source Image File
Data Source Arvo File
Data Source Parquet File
Machine Learning Data Pipeline Overview
Machine Learning Project as an Example (Just for Basic Idea)
Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 1
Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 2
Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 3
Components of a Machine Learning Pipeline
Extracting, transforming and selecting features
TF-IDF (Feature Extractor)
Word2Vec (Feature Extractor)
CountVectorizer (Feature Extractor)
FeatureHasher (Feature Extractor)
Tokenizer (Feature Transformers)
StopWordsRemover (Feature Transformers)
n-gram (Feature Transformers)
Binarizer (Feature Transformers)
PCA (Feature Transformers)
Polynomial Expansion (Feature Transformers)
Discrete Cosine Transform (DCT) (Feature Transformers)
StringIndexer (Feature Transformers)
IndexToString (Feature Transformers)
OneHotEncoder (Feature Transformers)
SQLTransformer (Feature Transformers)
VectorAssembler (Feature Transformers)
RFormula (Feature Selector)
ChiSqSelector (Feature Selector)
Classification Model
Decision tree classifier Project
Logistic regression Model (Classification Model It has regression in the name)
Naive Bayes Project (Iris flower class prediction)
Random Forest Classifier Project
Gradient-boosted tree classifier Project
Linear Support Vector Machine Project
One-vs-Rest classifier (a.k.a. One-vs-All) Project
Regression Model
Linear Regression Model Project
Decision tree regression Model Project
Random forest regression Model Project
Gradient-boosted tree regression Model Project
Clustering KMeans Project (Mall Customer Segmentation)
Explanation of few terms used in Model

Download Resources

Download Resources
Important Lecture
Bonus Lecture
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