Name: Machine Learning with Apache Spark 3.0 using Scala
Availability: InStock

What You'll 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.

Requirements

Basic programming knowledge – familiarity with any programming language (Scala, Java, Python, or C++) will be helpful.
Scala basics – prior exposure to Scala is recommended, but the course also covers essential Scala concepts needed for Spark ML.
Basic math & statistics – understanding of concepts like mean, median, variance, probability, and linear algebra will make learning ML easier.
No prior Spark experience required – the course includes an optional section on Apache Spark basics, making it beginner-friendly.
A computer with internet access to create a free Databricks account or run Spark locally.
Enthusiasm to learn Machine Learning and Big Data technologies hands-on!

Beginners in Machine Learning who want to understand ML concepts and implement them using Apache Spark and Scala.
Data Engineers & Big Data Developers looking to expand their skills into machine learning pipelines with Spark MLlib.
Software Developers & Programmers who want to transition into the field of Data Science and AI using distributed computing.
Data Scientists interested in leveraging Spark’s scalability for large datasets and production-grade ML models.
Students & Researchers eager to apply machine learning concepts in real-world, big data environments.
Professionals preparing for interviews or career transitions in Big Data, Spark, or ML-related roles.
Anyone curious about building end-to-end ML projects using Spark’s powerful ecosystem.

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22 Courses

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