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Autoencoders and Regularization - Learn and Implement

IntermediateCourse

In this course, you'll explore how Autoencoders compress, denoise, and derive valuable features from data. You'll also delve into regularization to curb overfitting and boost model generalizability. Autoencoders play roles in image enhancement, anomaly spotting, recommendation engines, and generative modelling. Meanwhile, regularization is essential in nearly every machine-learning endeavor. This training will equip you to address practical challenges across various applications.

4.4 (49 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 302

Skills You Will Learn

  • Artificial Intelligence, Data Science, Keras, Deep Learning

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 3 hours

Platform

  • SkillsNetwork

Last Update

  • May 10, 2025
About this Course
 In today's data-driven world, professionals with expertise in data preprocessing and model optimization are in high demand.  Autoencoders are a powerful tool for data compression and feature extraction. In an era where data is abundant but often noisy or high-dimensional, knowing how to effectively process and represent data is crucial. In addition, overfitting is a common challenge in machine learning. Regularization techniques are your shield against this problem. By learning how to apply regularization effectively, you'll be able to build models that generalize well to new, unseen data.

IBM has a special offer for watsonx.ai a studio for new foundation models, generative AI and machine learning. To take advantage of this offer visit watsonx.ai homepage. 

Enroll and don't miss the chance to be at the forefront of AI innovation. 

Course Syllabus

Topics that will be covered in the course
  • Regularization
  • Autoencoders
  • Variational Autoencoders 

Recommended Skills Prior to Taking this Course

A good understanding of Keras, Linear Regression and Classification and Neural Networks Principles in addition to Python programming language.

Instructors

Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Kopal Garg

Data Scientist Intern at IBM

I am a Data Scientist Intern at IBM, and a Masters student in Computer Science at the University of Toronto. I am passionate about building data science, and machine learning-based systems for improving various aspects of life.

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Roxanne Li

Data Scientist at IBM

I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!

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Contributors

Roodra Kanwar

Data Scientist at IBM

I am a data scientist by day, superhero by night. Psych! I wish I was that cool. Only the former part is true which is still pretty cool! I believe in constant learning and it is an essential part of being a productive data enthusiast. I am also pursuing my masters in computer science from Simon Fraser University specializing in Big Data. Moreover, knowledge is transfer learning (pun intended!) and what I have gained, I plan on reflecting it back to the data community.

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