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Introduction to Machine Learning
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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.

Course Cost

₹ 2,699

Intermediate

Skill Level

22 Hours

Self-paced lessons

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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4.7

2,12,530 Enrolled

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David Carlson

David Carlson

Lawrence Carin

Lawrence Carin

English

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olive-leaves-logo

4.7

2,12,530 Enrolled

olive-leaves-logo

English

David Carlson

David Carlson

Lawrence Carin

Lawrence Carin

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

  • Learn about the Transformer network for advanced NLP tasks

  • Gain insights into reinforcement learning concepts and applications

Skills you'll gain

machine learning
neural networks
PyTorch
convolutional neural networks
natural language processing
deep learning
reinforcement learning
data science

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

Fee Structure

Payment options

Financial Aid

Reviews

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.

Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

David Carlson
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4.7

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1,381

Reviews

2,12,207

Students

1

Course

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.
Lawrence Carin
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4.7

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1,400

Reviews

2,15,576

Students

1

Course

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.
Timothy Dunn
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4.7

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1,400

Reviews

2,15,576

Students

1

Course

Dr. Timothy Dunn is a Postdoctoral Associate at Duke University, where he specializes in neuroscience and machine learning, particularly focusing on deep convolutional neural networks. As a Forge Scholar, his research investigates how the brain controls behavior through innovative experimental techniques, including fast whole-brain imaging of neural activity and high-speed monitoring of zebrafish behavior in response to visual stimuli. His work has successfully uncovered the neural circuits responsible for both visually guided and spontaneous swimming behaviors. Currently, Dr. Dunn is developing neural network models aimed at enhancing computer vision capabilities for precise 3D movement tracking and predictive modeling in healthcare, specifically for pain control and traumatic brain injury prognosis. He holds a PhD in Neurobiology from Harvard University and has contributed to advancing the understanding of neural mechanisms through his research and teaching efforts, including courses on platforms like Coursera.
Kevin Liang
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4.7

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1,400

Reviews

2,15,576

Students

1

Course

Kevin Liang is a PhD Candidate in the Electrical and Computer Engineering Department at Duke University, where he specializes in deep learning. Since 2017, he has taught various machine learning courses at Duke, contributing to the education of future engineers and researchers. His research focuses on advancing techniques in machine learning, particularly in areas related to computer vision and threat recognition. Upon completing his PhD, Kevin will join Facebook as a Research Scientist, where he aims to further explore applications of deep learning in real-world scenarios. His work reflects a commitment to integrating cutting-edge technology with practical solutions in the field of artificial intelligence.
Introduction to Machine Learning

Intermediate

Skill Level

22 Hours

Self-paced lessons

Course Cost

₹ 2,699

Completion

CERTIFICATE

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.