Machine Learning with Apache Spark 3.0 using Scala

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

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.

Do you want to harness the power of Machine Learning and take your career to new heights? Apache Spark, the industry-leading big data processing engine, is revolutionizing the way organizations implement Machine Learning at scale. By combining its speed and scalability with powerful MLlib libraries, Spark allows you to build and deploy sophisticated Machine Learning models on massive datasets effortlessly.

This course is your ultimate guide to mastering Machine Learning with Apache Spark, designed to equip you with the skills to solve real-world problems and create scalable AI solutions. Through a hands-on, project-driven approach, you’ll learn how to preprocess data, implement algorithms, and evaluate models—empowering you to turn raw data into impactful insights that drive business success.

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.

What You’ll Gain:

• 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.

Who Should Enroll:

This course is ideal for:

• Data Scientists & Machine Learning Engineers looking to scale their models for big data environments.
• Big Data Professionals eager to enhance their analytics workflows with AI-driven insights.
• Developers & IT Experts aiming to future-proof their careers by mastering scalable Machine Learning tools.

Join the ranks of top professionals who are transforming industries with Machine Learning. Enroll now to become an expert in Apache Spark’s MLlib and turn data into intelligence that drives results!