Machine Learning with Apache Spark 3.0 using Scala

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

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 Machine Learning Fundamentals: Understand the core concepts of supervised, unsupervised, and recommendation algorithms with practical applications. Scalable Model Building: Learn how to leverage Spark MLlib to preprocess data, train models, and optimize performance on large-scale datasets. Real-World Projects: Gain hands-on experience by solving real-world problems, from predictive analytics to recommendation systems. Big Data Integration: Discover how to integrate Machine Learning workflows seamlessly into your big data pipelines for maximum efficiency.

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.

Requirement:

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