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Machine Learning with Apache Spark 3.0 using Scala

Created by Bigdata Engineer, Last Updated 15-Jan-2021, Language:English

Machine Learning with Apache Spark 3.0 using Scala

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

Created by Bigdata Engineer, Last Updated 15-Jan-2021, Language:English

What Will I Get ?

  • 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 (Free Service) in this course.
  • 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.

Course Content

Bigdata Engineer

I am Solution Architect with 12+ year’s of experience in Banking, Telecommunication and Financial Services industry across a diverse range of roles in Credit Card, Payments, Data Warehouse and Data Center programmes

My role as Bigdata and Cloud Architect to work as part of Bigdata team to provide Software Solution.

Responsibilities includes,

- Support all Hadoop related issues
- Benchmark existing systems, Analyse existing system challenges/bottlenecks and Propose right solutions to eliminate them based on various Big Data technologies
- Analyse and Define pros and cons of various technologies and platforms
- Define use cases, solutions and recommendations
- Define Big Data strategy
- Perform detailed analysis of business problems and technical environments
- Define pragmatic Big Data solution based on customer requirements analysis
- Define pragmatic Big Data Cluster recommendations
- Educate customers on various Big Data technologies to help them understand pros and cons of Big Data
- Data Governance
- Build Tools to improve developer productivity and implement standard practices

I am sure the knowledge in these courses can give you extra power to win in life.

All the best!!