Ementorhub

What you'll learn

Implement machine learning models using PySpark MLlib.

Implement linear and logistic regression models for predictive analysis.

Apply clustering methods to group unlabeled data using algorithms like K-means.

Explore real-world applications of PySpark MLlib through practical examples.

Skills you'll gain

Big Data

Unsupervised Learning

PySpark

Distributed Computing

Machine Learning Algorithms

Supervised Learning

Data Processing

Machine Learning

Regression Analysis

Feature Engineering

Apache Spark

Scalability

There are 4 modules in this course

By the end of this course, you will be able to: - Understand the fundamentals of PySpark and its architecture - Load, process, and manipulate large-scale datasets using PySpark’s DataFrame and RDD APIs Build machine learning models with PySpark’s MLlib, covering classification, regression, and clustering techniques - Optimize and tune machine learning models for better performance - Apply techniques for feature engineering, model evaluation, and hyperparameter tuning in a distributed environment This course is ideal for data professionals, aspiring data engineers, and machine learning enthusiasts who want to use PySpark to handle large-scale data and build machine learning models. Some prior knowledge of Python and machine learning concepts is recommended. Join us to enhance your data processing and machine learning skills with PySpark and take your expertise to the next level!

Advanced PySpark Machine Learning

Applications and Case-Studies

Course Wrap-Up and Assessment