Machine Learning with PySpark
Master distributed ML with PySpark. Build scalable models and implement classification, regression, clustering algorithms, while optimizing performance.
Master distributed ML with PySpark. Build scalable models and implement classification, regression, clustering algorithms, while optimizing performance.
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. The course covers both supervised and unsupervised learning techniques, including linear regression, logistic regression, decision trees, and clustering methods like K-means. You'll also explore association rule mining and learn how to evaluate model performance using various metrics. By the end of this course, you'll understand PySpark's architecture, be able to load and process large-scale datasets, build and optimize machine learning models with PySpark's MLlib, and apply these skills to real-world case studies in different industries. The course strikes a balance between theoretical knowledge and practical application, making it perfect for data professionals who want to leverage distributed computing for machine learning tasks. You'll gain hands-on experience through numerous assignments and projects, culminating in a comprehensive house price prediction project.
Instructors:
English
Not specified
Powered by
What you'll learn
Implement machine learning models using PySpark MLlib
Build and optimize classification and regression models for predictive analysis
Apply clustering methods to group unlabeled data using K-means
Master association rule mining techniques for pattern discovery
Evaluate model performance using various metrics like RMSE and R-squared
Process and manipulate large-scale datasets with PySpark's DataFrame API
Skills you'll gain
This course includes:
6.2 Hours PreRecorded video
14 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This comprehensive course on Machine Learning with PySpark equips learners with the skills to implement scalable machine learning models using Apache Spark's Python API. The curriculum begins with an introduction to PySpark MLlib and machine learning fundamentals before delving into both supervised and unsupervised learning techniques. Students learn to implement linear and logistic regression models, decision trees, K-means clustering, and association rule mining with FP-Growth. The course emphasizes practical applications through industry-specific case studies, including customer churn prediction, market basket analysis, and predictive maintenance. Throughout the modules, students gain hands-on experience with data processing, model building, evaluation metrics, and optimization techniques in a distributed computing environment. The program culminates with a project on house price prediction, allowing learners to apply their newfound skills to a real-world scenario.
Introduction to PySpark Machine Learning
Module 1 · 4 Hours to complete
Advanced PySpark Machine Learning
Module 2 · 4 Hours to complete
Applications and Case-Studies
Module 3 · 2 Hours to complete
Course Wrap-Up and Assessment
Module 4 · 1 Hours to complete
Instructor
Inspiring the Next Generation of Tech Professionals
Edureka is dedicated to providing high-quality, instructor-led online training, empowering professionals to enhance their skills in various domains. The platform features a diverse team of experienced instructors who are passionate about teaching and possess extensive industry knowledge. These instructors facilitate a wide range of courses covering topics such as data science, artificial intelligence, machine learning, and cloud computing. Edureka's commitment to education is reflected in its innovative approach to learning, which includes interactive sessions, real-world projects, and 24/7 support for students. By fostering a collaborative learning environment, Edureka ensures that learners not only acquire technical skills but also develop critical thinking and problem-solving abilities essential for success in today's fast-paced job market.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.