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January 28, 2025
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This course is part of PySpark for Data Science Specialization
Instructor: Edureka
Included with
Recommended experience
Intermediate level
Prior experience with Python and basic machine learning concepts is recommended. Familiarity with distributed computing will be helpful.
Recommended experience
Intermediate level
Prior experience with Python and basic machine learning concepts is recommended. Familiarity with distributed computing will be helpful.
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.
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October 2024
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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!
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.
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.
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.
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.
1 video2 readings1 assignment1 discussion prompt
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This course assumes basic knowledge of Python programming, SQL, and an understanding of machine learning concepts. Familiarity with big data and distributed systems will be helpful but is not mandatory.
PySpark MLlib is Apache Spark’s scalable machine learning library, designed for large-scale data processing. Learning PySpark MLlib helps you implement machine learning algorithms in a distributed computing environment, making it essential for big data applications.
While the course provides a foundation in PySpark and machine learning, it is more suitable for learners who have a basic understanding of machine learning concepts and Python programming.
PySpark is specifically designed for distributed computing and big data processing, making it suitable for handling large datasets across multiple machines. Scikit-learn, on the other hand, is used for smaller datasets and single-machine environments. PySpark’s MLlib leverages Apache Spark for parallel processing, while scikit-learn is more focused on traditional machine learning workflows.
PySpark is a highly in-demand skill in the field of big data analytics and machine learning. Proficiency in PySpark opens up career opportunities in data engineering, data science, and machine learning roles, particularly in organizations dealing with large-scale data.
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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