Machine Learning with PySpark introduces the power of distributed computing for machine learning, equipping learners with the skills to build scalable machine learning models. Through hands-on projects, you will learn how to use PySpark for data processing, model building, and evaluating machine learning algorithms.
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!
Overview
Syllabus
- Introduction to PySpark Machine Learning
- This module will instruct you on setting up of an environment for the implementation of machine learning algorithms using PySpark MLlib. You will gain a fundamental understanding of the importance of machine learning in the context of big data and explore the implementation of machine learning models using PySpark.
- Advanced PySpark Machine Learning
- In this module, you will be able to explore the foundations of unsupervised machine learning, focusing on techniques for analyzing unlabeled data. You will dive into clustering algorithms like K-means, learning how to group data points based on similarities. Additionally, you will discover the power of Association Rule Mining, uncovering hidden patterns and relationships in datasets without predefined labels.
- Applications and Case-Studies
- The course will equip you with the skills to evaluate machine learning models using various performance metrics and techniques in PySpark MLlib. You will also explore the future scope and potential applications of MLlib in real-world scenarios, gaining insights into how it can be applied to different industries and problem domains. Through case studies, you will analyze practical examples of machine learning implementations.
- Course Wrap-Up and Assessment
- This module is meant to test how well you understand the different ideas and lessons you've learned in this course. You will undertake a project based on these PySpark concepts and complete a comprehensive quiz that will assess your confidence and proficiency in Machine Learning with PySpark.
Taught by
Edureka
