PyTorch Basics for ML
Master PyTorch fundamentals and implement classical machine learning algorithms in this comprehensive introductory course.
Master PyTorch fundamentals and implement classical machine learning algorithms in this comprehensive introductory course.
This foundational course teaches PyTorch basics and machine learning implementation. Students learn tensor operations, automatic differentiation, and integration with Pandas and NumPy. The curriculum progresses through linear regression, logistic regression, and multiple regression models while focusing on PyTorch's key features like custom modules, optimizers, and data loaders. Students gain hands-on experience in building machine learning pipelines, handling large datasets, and implementing various training techniques including gradient descent and early stopping.
3.6
(23 ratings)
15,385 already enrolled
Instructors:
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What you'll learn
Build and optimize machine learning pipelines using PyTorch
Master tensor operations and automatic differentiation
Implement linear and logistic regression models
Handle large datasets efficiently with PyTorch data loaders
Integrate PyTorch with NumPy and Pandas
Apply gradient descent and optimization techniques
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams, Final project
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 5 modules in this course
This comprehensive course introduces students to PyTorch fundamentals and machine learning implementation. The curriculum covers tensor operations, automatic differentiation, and integration with essential Python libraries. Students learn to build and optimize various machine learning models, from linear regression to logistic regression, while mastering PyTorch's key features including custom modules, optimizers, and data loaders. The course emphasizes practical implementation through hands-on exercises and projects.
Tensors and Fundamentals
Module 1
Linear Regression Fundamentals
Module 2
Advanced Training Techniques
Module 3
Multiple Regression
Module 4
Classification and Final Project
Module 5
Fee Structure
Instructor
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
Testimonials
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3.6 course rating
23 ratings
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