Machine Learning with Apache Spark 3.0 using Scala

Machine Learning with Apache Spark 3.0 using Scala

Machine Learning with Apache Spark 3.0 using Scala

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

Language: english

Note: 3.1/5 (12 notes) 4,418 students

Instructor(s): Bigdata Engineer

Last update: 2022-05-10

What you’ll 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

 

Requirements

  • Some programming experience is required and Scala fundamental knowledge is also required.
  • Fundamental Spark Knowledge mandatory

 

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

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.

 

Who this course is for

  • Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist

 

Course content

  • Introduction
    • Introduction
    • Overview
    • What is Spark ML?
    • Introduction to Machine Learning
  • Apache Spark Basics (Optional)
    • Introduction to Spark
    • (Old) Free Account creation in Databricks
    • (New) 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

 

Machine Learning with Apache Spark 3.0 using ScalaMachine Learning with Apache Spark 3.0 using Scala

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