Edureka
Machine Learning with PySpark
Edureka

Machine Learning with PySpark

Edureka

Instructor: Edureka

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

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.

Details to know

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Recently updated!

October 2024

Assessments

14 assignments

Taught in English

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This course is part of the PySpark for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

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.

What's included

27 videos5 readings4 assignments3 discussion prompts

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.

What's included

26 videos6 readings5 assignments1 discussion prompt

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.

What's included

18 videos2 readings4 assignments2 discussion prompts

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.

What's included

1 video2 readings1 assignment1 discussion prompt

Instructor

Edureka
Edureka
47 Courses40,291 learners

Offered by

Edureka

Recommended if you're interested in Machine Learning

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