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Machine Learning with Apache Spark 3.0 using Scala
Machine Learning with Apache Spark 3.0 using Scala with Examples and 4 Projects
This Course Includes
udemy
4 (65 reviews )
8h 20m 
english
Online - Self Paced
professional certificate
Udemy
About Machine Learning with Apache Spark 3.0 using Scala
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
.
What You Will Learn?
- Understand the fundamentals of Machine Learning and its types (supervised, unsupervised, classification, regression, clustering). .
- Learn the basics of Apache Spark 3.0 and how it supports large-scale data processing. .
- Work hands-on with Spark RDDs, DataFrames, and Datasets using Scala. .
- Explore Spark MLlib – the machine learning library in Spark – and how it enables scalable ML solutions. .
- Build end-to-end Machine Learning pipelines using Spark, from data ingestion to model evaluation. .
- Gain practical experience with real-world datasets such as predict rain in Australia, Iris flower classification, ad click prediction, and mall customer segment .
- Learn how to work with different data sources like CSV, JSON, Parquet, Avro, LIBSVM, and images. .
- Master feature engineering techniques such as TF-IDF, Word2Vec, CountVectorizer, PCA, n-grams, StringIndexer, OneHotEncoder, VectorAssembler, and more. .
- Implement various classification models including Decision Trees, Logistic Regression, Naive Bayes, Random Forests, Gradient-Boosted Trees, Linear SVM, .
- Apply different regression models such as Linear Regression, Decision Trees, Random Forests, and Gradient-Boosted Trees. .
- Work with clustering algorithms like KMeans for customer segmentation. .
- Understand the concepts behind machine learning pipelines and how to use Spark’s pipeline API effectively. .
- Get tips, tricks, and best practices for writing efficient and production-ready ML models in Spark using Scala. Show moreShow less.