Introduction to Machine Learning
Learn machine learning fundamentals with PyTorch. Master models, and applications in image recognition and NLP. Gain hands-on experience in data science.
Learn machine learning fundamentals with PyTorch. Master models, and applications in image recognition and NLP. Gain hands-on experience in data science.
This course offers a comprehensive introduction to machine learning, covering fundamental models and their applications across various industries. Students will gain hands-on experience implementing machine learning algorithms using PyTorch, a popular open-source library. The curriculum includes logistic regression, multilayer perceptrons, convolutional neural networks, and natural language processing. Practical exercises provide real-world application experience, preparing learners for careers in data science and AI.
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What you'll learn
Understand fundamental machine learning models and their applications
Implement machine learning algorithms using PyTorch
Develop skills in logistic regression and multilayer perceptrons
Master convolutional neural networks for image analysis
Apply natural language processing techniques to text data
Explore recurrent neural networks and LSTM models
Skills you'll gain
This course includes:
10.38 Hours PreRecorded video
24 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This course provides a comprehensive introduction to machine learning, covering a wide range of topics from basic concepts to advanced techniques. Students will learn about logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, and reinforcement learning. The course emphasizes practical implementation using PyTorch, a popular open-source library used by leading tech companies. Through hands-on exercises and real-world examples, learners will gain the skills to apply machine learning algorithms to solve complex problems in various industries, including medical diagnostics, image recognition, and text prediction.
Simple Introduction to Machine Learning
Module 1 · 7 Hours to complete
Basics of Model Learning
Module 2 · 3 Hours to complete
Image Analysis with Convolutional Neural Networks
Module 3 · 3 Hours to complete
Recurrent Neural Networks for Natural Language Processing
Module 4 · 4 Hours to complete
The Transformer Network for Natural Language Processing
Module 5 · 2 Hours to complete
Introduction to Reinforcement Learning
Module 6 · 3 Hours to complete
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Instructors
Assistant Professor of Civil and Environmental Engineering
David Carlson is an Assistant Professor at Duke University in both the Department of Civil and Environmental Engineering and the Department of Biostatistics and Bioinformatics. His research centers on machine learning and data-driven science, exploring how contemporary machine learning and statistical techniques can be applied not only to analyze large datasets but also to design innovative experiments that enhance scientific understanding. Carlson has created algorithms and analytical methods for various engineering and health-related applications, and he regularly publishes his groundbreaking algorithms and methods in the machine learning literature. He earned his Ph.D., M.S., and B.S.E. from Duke University, where he received awards for both his academic scholarship and teaching excellence.
Provost of King Abdullah University of Science and Technology
Dr. Lawrence Carin is the Provost of King Abdullah University of Science and Technology (KAUST), where he leads academic and research initiatives. Prior to his role at KAUST, he was the James L. Meriam Distinguished Professor of Electrical and Computer Engineering at Duke University, where he also held the position of Vice Provost for Research. Dr. Carin is recognized as one of the leading figures in machine learning and artificial intelligence, with an extensive publication record in top-tier academic journals and conferences. His work has significantly advanced the understanding and application of these technologies across various domains, making him a prominent contributor to the fields of engineering and computer science.
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4.7 course rating
3,575 ratings
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