- 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.
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.