How will PySpark help in improving my career opportunities?

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.

What are the hardware/software requirements to run PySpark optimally at home?

A machine with at least 8 GB RAM and a quad-core processor is recommended for smooth local practice. You will need Python 3.x, Java 8 or higher, and Apache Spark installed. Alternatively, you can use Dockerized Spark or virtualized environments for ease of setup.

How large should my datasets be to benefit from using PySpark over local Python tools?

PySpark shines when datasets grow beyond the limits of single-machine processing (typically several gigabytes and above). For very small datasets, libraries like Pandas or scikit-learn may be more efficient.

How likely is it that completing this course will help me get job interviews in Data Engineering / Big Data roles?

While job outcomes vary, the skills taught in PySpark, distributed computing, and big data pipelines are highly valued in Data Engineering and Big Data roles. Completing the course will strengthen your portfolio and significantly improve your chances of securing interviews.

Can I preview a course before enrolling?

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.

When will I have access to the lectures and assignments?

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.

What will I get when I enroll?

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.

When will I receive my 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.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

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.

Machine Learning with PySpark

Grow your skills with Coursera Plus for $239/year (usually $399). Save now.

Machine Learning with PySpark

This course is part of PySpark for Data Science Specialization

Instructor: Edureka

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 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.

Skills you'll gain

Tools you'll learn

PySpark

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

14 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

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
Earn a shareable career certificate

There are 4 modules in this course

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 Who Should take this Course: 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. Prerequisites: 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.

What's included

27 videos5 readings4 assignments3 discussion prompts

27 videos Total 138 minutes

Course Introduction: Machine Learning with PySpark 2 minutes
BigData and Distributed Systems 7 minutes
Introduction to MLlib 4 minutes
Key Features and Applications of MLlib 4 minutes
What is Machine Learning 6 minutes
Types of Machine Learning 7 minutes
Applications of Machine Learning 6 minutes
Why PySpark for ML 7 minutes
Supervised Machine Learning 3 minutes
Applications of Supervised Machine Learning 4 minutes
Unsupervised Machine Learning 7 minutes
Significance of Unsupervised Machine Learning 2 minutes
Semi-supervised Machine Learning 5 minutes
Significance of Semi-supervised Machine Learning 4 minutes
Machine Learning Pipelines 5 minutes
Benefits of ML Pipeline 4 minutes
Linear Regression 5 minutes
Linear Regression Use case 5 minutes
Implementing Linear Regression 7 minutes
Data Ingestion 6 minutes
Logistic Regression 5 minutes
Applications of Logistic Regression 3 minutes
Key Concepts of Logistic Regression 3 minutes
Logistic Regression Use case 6 minutes
Evaluating Logistic Regression 7 minutes
Decision Trees 7 minutes
Decision Trees Use case 6 minutes

5 readings Total 50 minutes

Welcome to Machine Learning with PySpark 10 minutes
Introduction to Machine Learning with PySpark 10 minutes
Machine Learning Algorithms 10 minutes
Importance of Machine Learning with PySpark 10 minutes
Module Summary: Introduction to PySpark Machine Learning 10 minutes

4 assignments Total 38 minutes

Practice Quiz: Overview of PySpark MLlib 6 minutes
Practice Quiz: Introduction to Machine Learning 6 minutes
Practice Quiz: Implementing Machine Learning Algorithms 6 minutes
Knowledge Check: Introduction to PySpark MLlib 20 minutes

3 discussion prompts Total 30 minutes

Introduce Yourself 10 minutes
Working with PySpark 10 minutes
Which Machine Learning algorithm do you think is widely used? 10 minutes

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

26 videos Total 134 minutes

What is Unsupervised Learning 6 minutes
Applications of Unsupervised Learning 3 minutes
How Algorithms differ between Supervised and Unsupervised 5 minutes
Unsupervised Machine Learning Algorithms 5 minutes
What is Distance metrics 4 minutes
Significance of Distance metrics 3 minutes
Types of Distance metrics 7 minutes
Labeling techniques 5 minutes
Significance of Data Labeling 5 minutes
One hot encoding 7 minutes
What is Clustering 6 minutes
Types of Clustering 4 minutes
K-means Clustering 6 minutes
Applications of K-means Clustering 4 minutes
Elbow method 5 minutes
Silhouette Method 4 minutes
Demonstration of K-means Clustering 5 minutes
Model Evaluation for K-means Clustering 5 minutes
What is Association Rule Mining 7 minutes
Implementing of FP Growth 4 minutes
Basic concepts of Association Rule Mining 5 minutes
Lift and Frequent Itemsets 4 minutes
Applications of Association Rule Mining 5 minutes
Use cases of Association Rule Mining 3 minutes
Demonstration of Association Rule Mining 7 minutes
Association Rule Mining using FP Growth 7 minutes

6 readings Total 65 minutes

Unsupervised Machine Learning with PySpark 10 minutes
Explore DBSCAN 15 minutes
Dimensionality Reduction 10 minutes
Gaussian Mixture Models (GMM) 10 minutes
FP-Growth Algorithm in PySpark 10 minutes
Module Summary: Advanced PySpark Machine Learning 10 minutes

5 assignments Total 44 minutes

Practice Quiz: Unsupervised Machine Learning 6 minutes
Practice Quiz: Data Labeling 6 minutes
Practice Quiz: Overview of Clustering 6 minutes
Practice Quiz: Overview of Association Rule Mining 6 minutes
Knowledge Check: Overview of Unsupervised Learning 20 minutes

1 discussion prompt Total 10 minutes

Have you ever applied dimensionality reduction techniques to high-dimensional data? 10 minutes

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

18 videos Total 99 minutes

Evaluating ML Models 2 minutes
Steps for Evaluating Models 7 minutes
RMSE 7 minutes
R-squared 6 minutes
Significance of R-Squared 2 minutes
Saving and Loading Models 7 minutes
Future scope of MLlib 7 minutes
Applications of Social Media 6 minutes
Applications of Entertainment 7 minutes
Applications of Business 6 minutes
Applications of Finance 5 minutes
Customer churn prediction 5 minutes
Model Training for Customer Churn Prediction 7 minutes
Model Evaluation for Customer Churn Prediction 5 minutes
Market Basket Analysis 4 minutes
FP Growth for Market Basket Analysis 6 minutes
Predictive Maintenance 4 minutes
Model Evaluation in Predictive Maintainance 5 minutes

2 readings Total 20 minutes

Model Evaluation Techniques in Machine Learning 10 minutes
Module Summary: Applications and Case-Studies 10 minutes

4 assignments Total 38 minutes

Practice Quiz: Machine Learning Model Evaluation 6 minutes
Practice Quiz: Future developments and Applications of MLlib 6 minutes
Practice Quiz: Case-studies of MLlib 6 minutes
Knowledge Check: Applications and Case studies of MLlib 20 minutes

2 discussion prompts Total 20 minutes

Have you had experience applying machine learning in various industries? 10 minutes
In what ways do you think MLlib is utilized in the Health and Medical sector? 10 minutes

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

1 video Total 2 minutes

Summary: Machine Learning with PySpark 2 minutes

2 readings Total 70 minutes

Project - House Price Prediction 60 minutes
Course Overview of Machine Learning with PySpark 10 minutes

1 assignment Total 20 minutes

End Course Knowledge Check: Machine Learning with PySpark 20 minutes

1 discussion prompt Total 10 minutes

Describe your Learning Journey 10 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka

160 Courses 149,681 learners

Offered by

Edureka

Explore more from Machine Learning

EDUCBA
PySpark: Apply & Evaluate Predictive ML Models
Course
Edureka
Introduction to PySpark
Course
EDUCBA
Spark and Python for Big Data with PySpark
Specialization
EDUCBA
PySpark & Python: Hands-On Guide to Data Processing
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

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.