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Unsupervised Learning, Recommenders, Reinforcement Learning
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Unsupervised Learning, Recommenders, Reinforcement Learning

Learn advanced machine learning techniques including clustering, recommender systems, and reinforcement learning for real-world AI applications.

Course Cost

Free course

Beginner

Skill Level

27 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

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4.9

2,22,611 Enrolled

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English

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olive-leaves-logo

4.9

2,22,611 Enrolled

olive-leaves-logo

English

What you'll learn

  • Implement unsupervised learning algorithms

  • Build collaborative filtering recommender systems

  • Develop content-based filtering systems

  • Create deep reinforcement learning models

  • Apply anomaly detection techniques

Skills you'll gain

Unsupervised Learning
Reinforcement Learning
Recommender Systems
Clustering
Anomaly Detection
K-means
Collaborative Filtering
Deep Learning
Machine Learning
TensorFlow

This course includes:

7.2 Hours PreRecorded video

8 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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Certificate
Certificate

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

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PROVIDED BY

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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.

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There are 3 modules in this course

This comprehensive course, taught by Andrew Ng, explores advanced machine learning concepts beyond supervised learning. Students learn to implement clustering algorithms, build recommender systems using collaborative filtering and content-based approaches, and develop reinforcement learning models. The curriculum includes hands-on projects like anomaly detection systems and a deep Q-learning neural network for lunar landing simulation.

Unsupervised learning

Module 1 · 9 Hours to complete

Recommender systems

Module 2 · 10 Hours to complete

Reinforcement learning

Module 3 · 8 Hours to complete

Fee Structure

Reviews

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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.

Unsupervised Learning, Recommenders, Reinforcement Learning

Beginner

Skill Level

27 Hours

Self-paced lessons

Course Cost

Free course

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.