Building Recommender Systems with Machine Learning and AI faq

learnersLearners: 33,700
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This course provides an introduction to building recommender systems using machine learning and AI. Students will gain an understanding of how to use deep learning, neural networks, and machine learning to create personalized recommendations for users.

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Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free Trial

providerProvider:

LinkedIn Learning

certificateCertificate:

No Information

languageLanguage:

English

start dateStart Date:

Self Paced

Course Overview

❗The content presented here is sourced directly from LinkedIn Learning platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

This course, Building Recommender Systems with Machine Learning and AI, provides an overview of the various recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks. Participants will gain an understanding of the real-world challenges of applying these algorithms at a large scale with real-world data. Additionally, participants will learn how to develop their own framework to test algorithms and build their own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow.

[Applications]
After completing this course, students will be able to apply their knowledge to develop their own recommender systems. They will be able to use neighborhood-based collaborative filtering and more modern techniques, such as matrix factorization and deep learning with artificial neural networks. Additionally, they will be able to develop their own frameworks to test algorithms and build their own neural networks using technologies such as Amazon DSSTNE, AWS SageMaker, and TensorFlow.

[Career Paths]
1. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover trends and insights. They use a variety of techniques, including machine learning and AI, to develop models and algorithms that can be used to make predictions and recommendations. Data Scientists are in high demand and the field is expected to continue to grow in the coming years.

2. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models and algorithms. They use a variety of technologies, such as Amazon DSSTNE, AWS SageMaker, and TensorFlow, to build and deploy models. Machine Learning Engineers are in high demand and the field is expected to continue to grow in the coming years.

3. AI Engineer: AI Engineers are responsible for developing and deploying AI-based solutions. They use a variety of technologies, such as natural language processing, computer vision, and deep learning, to build and deploy AI-based solutions. AI Engineers are in high demand and the field is expected to continue to grow in the coming years.

4. Recommendation System Engineer: Recommendation System Engineers are responsible for developing and deploying recommendation systems. They use a variety of techniques, such as neighborhood-based collaborative filtering and matrix factorization, to build and deploy recommendation systems. Recommendation System Engineers are in high demand and the field is expected to continue to grow in the coming years.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and natural language processing. With the increasing demand for AI and machine learning professionals, this degree path is becoming increasingly popular.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of AI systems and their applications. It covers topics such as machine learning, natural language processing, computer vision, robotics, and more. It also provides students with the opportunity to develop their own AI projects and gain hands-on experience.

3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and predictive analytics. It also provides students with the opportunity to develop their own data science projects and gain hands-on experience.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of advanced machine learning algorithms and their applications. It covers topics such as deep learning, reinforcement learning, and natural language processing. It also provides students with the opportunity to develop their own machine learning projects and gain hands-on experience.

Course Syllabus

Install Anaconda, review course materials, and create movie recommendations

Course roadmap

Understanding you through implicit and explicit ratings

Top-N recommender architecture

Review the basics of recommender systems

 

Course Provider

Provider LinkedIn Learning's Stats at AZClass

Building Recommender Systems with Machine Learning and AI describes how to build recommender systems using machine learning and artificial intelligence. Learners will understand the real-world challenges of applying these algorithms at scale using real-world data. They will also learn how to develop their own frameworks to test algorithms and build their own neural networks using technologies such as Amazon DSSTNE, AWS SageMaker, and TensorFlow. This course is ideal for those interested in data science, machine learning, and artificial intelligence, as it provides a comprehensive overview of the fundamentals of building recommender systems.

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faq FAQ for Machine Learning Courses

Q1: What is the purpose of this online course on Building Recommender Systems with Machine Learning and AI?

This online course is designed to provide an introduction to the fundamentals of building recommender systems using machine learning and artificial intelligence. It covers topics such as data pre-processing, feature engineering, model selection, and evaluation. The course also provides hands-on experience with popular recommender system algorithms such as collaborative filtering, content-based filtering, and hybrid models.

Q2: What skills will I learn from this course on Building Recommender Systems with Machine Learning and AI?

This course will provide you with the skills to build and evaluate recommender systems using machine learning and artificial intelligence. You will learn about data pre-processing, feature engineering, model selection, and evaluation. You will also gain hands-on experience with popular recommender system algorithms such as collaborative filtering, content-based filtering, and hybrid models. Additionally, you will gain an understanding of the fundamentals of data science and machine learning.

Q3: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q4: How many people have enrolled in this course?

So far, a total of 33700 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q5: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
Watch the video preview to understand the course content.
(Please note that the following steps should be performed on LinkedIn Learning's official site.)
Find the course description and syllabus for detailed information.
Explore teacher profiles and student reviews.
Add your desired course to your cart.
If you don't have an account yet, sign up while in the cart, and you can start the course immediately.
Once in the cart, select the course you want and click "Enroll."
LinkedIn Learning may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Machine Learning courses and certifications, our extensive collection at azclass.net will help you.

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