Machine Learning with Apache Spark 3.0 using Scala
Course Overview
About This Free Course
Do you want to master Machine Learning at scale using one of the most powerful Big Data frameworks in the world? This course will teach you Machine Learning with Apache Spark 3.0 and Scala, step by step, through real-world projects and hands-on coding examples.
Apache Spark is the industry-standard framework for processing and analyzing large datasets. Its MLlib (Machine Learning Library) provides scalable implementations of machine learning algorithms, making it possible to train, evaluate, and deploy models on massive amounts of data efficiently. Combined with Scala, the native language of Spark, you’ll learn how to build and optimize end-to-end machine learning pipelines.
This course is designed for beginners to intermediate learners who want to get practical experience in applying machine learning techniques in Spark. You’ll start with Big Data and Spark basics, then move on to core machine learning concepts, and finally apply them to real-world datasets through hands-on projects like rain prediction, ad click prediction, iris flower classification, and customer segmentation.
By the end of this course, you will have the skills and confidence to build scalable machine learning models using Spark 3.0 and Scala—skills that are highly in-demand in industries such as finance, e-commerce, telecom, and technology.
What You Will Learn
Introduction to Machine Learning & Spark MLlib
Basics of machine learning, types (supervised, unsupervised, classification, regression, clustering).
What is Spark ML? How Spark MLlib simplifies building ML models at scale.
Apache Spark Basics (Optional Section)
Get familiar with Spark fundamentals: RDD, DataFrames, and Datasets.
Set up Spark environment using Databricks.
Learn notebook basics, cluster provisioning, and working with Scala.
Data Handling & Preparation
Work with different data sources: CSV, JSON, LIBSVM, Images, Avro, and Parquet.
Understand the Machine Learning data pipeline in Spark.
Practice feature extraction, transformation, and selection techniques.
Feature Engineering in Spark ML
Learn popular feature extractors like TF-IDF, Word2Vec, CountVectorizer, FeatureHasher.
Apply transformers such as Tokenizer, StopWordsRemover, n-gram, PCA, StringIndexer, OneHotEncoder.
Use feature selectors like RFormula and ChiSqSelector.
Build and connect them into end-to-end ML pipelines.
Machine Learning Models with Spark
Classification Models: Decision Trees, Logistic Regression, Naive Bayes (Iris Prediction), Random Forest, Gradient-Boosted Trees, Linear SVM, One-vs-Rest.
Regression Models: Linear Regression, Decision Tree Regression, Random Forest Regression, Gradient-Boosted Tree Regression, Predict Ads Clicks project.
Clustering: KMeans (Customer Segmentation Project).
Hands-On Projects
Rain Prediction in Australia (complete ML pipeline).
Iris Flower Classification using Naive Bayes.
Customer Segmentation using KMeans.
Ad Click Prediction using Linear Regression.
Multiple other classification and regression use cases with step-by-step Scala implementations.
Spark MLlib in Practice
Understand how to train, evaluate, and optimize ML models at scale.
Explore key concepts like shuffling, correlation, pipeline components, and evaluation metrics.
Who Should Take This Course
"Machine Learning with Apache Spark 3.0 using Scala" is aimed at people who want a practical, structured introduction to development without paying full price for it. It's a solid fit if you're starting out in development and want a guided course rather than piecing tutorials together yourself, if you've tried free YouTube content on the topic and want something more organized in around 3, or if you already work in a related area and want a refresher you can finish at your own pace. Since enrollment happens on Udemy itself, you keep full access to view the lectures, download any provided resources, and revisit the material later — this isn't a stripped-down or time-limited version of the course.
Why This Course Is Worth Taking
Our take: this listing earns a spot on FreeWebCart because the coupon we verified actually brings the price to $0, not just a token discount, and the course carries a 4/5 rating on Udemy from 68+ students who've already enrolled. That combination — real reviews plus a working 100% OFF code — is what we look for before publishing a development course. It won't replace hands-on experience or a full degree program, but as a low-risk way to test whether development is worth pursuing further, or to pick up one specific skill, the free price tag makes it an easy yes while the coupon lasts.
Pros & Cons
👍 Pros
- 100% free to enroll via this coupon (normally $59.99)
- Lifetime access on Udemy once enrolled, even after the coupon expires
- Rated 4/5 by past students on Udemy
- Self-paced — no fixed schedule or live sessions to attend
👎 Cons
- Coupon is time-limited and can expire before you enroll
- No live instructor support — questions go through Udemy's Q&A, not us
- Certificate is a Udemy completion certificate, not an accredited qualification
Frequently Asked Questions
Is "Machine Learning with Apache Spark 3.0 using Scala" really free?
Yes — we verified a 100% OFF Udemy coupon for this development course before publishing it. Enroll directly on Udemy using the button below; no credit card is needed while the coupon is active.
How long will this coupon last?
Udemy coupons typically last 1–3 days or expire after roughly 1,000 enrollments, whichever comes first. If the price on Udemy no longer shows $0 when you click through, the coupon has expired since we last checked it.
Do I keep access after the coupon expires?
Yes. Once you enroll while the coupon is live, "Machine Learning with Apache Spark 3.0 using Scala" is yours to keep on Udemy — including any future updates the instructor makes — even after the coupon runs out.
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