Course Review: Building Recommender Systems with Machine Learning and AI

The course content will cover several things like building recommender systems, evaluating them, content-based filtering using item attributes, model-based methods that include matrix factorization and SVD, and more. It also contains things like applying deep learning, AI, and artificial neural networks to recommendations, session-based recommendations with recursive neural networks, and so much more. If you’re looking to delve into the world of AI, consider checking out our comprehensive guide on Reinforcement Learning Courses for expert insights and recommendations. This course is filled with knowledge with hands-on learning experiences so that learners can practice the teachings easily and apply these concepts to real-world data.

TTC Course Analysis:

Following are the results of comprehensive analysis of “Building Recommender Systems with Machine Learning and AI” online course by our team of experts.

TakeThisCourse Sentiment Analysis Results:

In order to facilitate our learners with real user experience, we performed sentiment analysis and text mining techniques that generates following results:

• TTC analyzed a total of 339 reviews for this online course.
• The analysis indicates that around 79% reviews were positive while around 21% of reviews had negative sentiment.
• Sundog Education’s online course received a total score of 3.9 out of 5, based on user opinions related to 4 effectiveness factors including content, engagement, quality practice and career benefit.

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TTC Course Effectiveness:

Based on learner reviews we believe;

• The content of this course is comprehensive and clear.
• The lectures are engaging and the instructor has an interactive delivery style.
• The course contains exercises that help users practice whatever they have learned so far. It also contains additional resources so learners can gain extra knowledge.
• The course is beneficial for software developers, engineers, and computer scientists who are willing to advance in their careers.

Pros & Cons:

Pros:

• The instructor is a highly experienced individual.
• The course contains hands-on activities.
• The course is detailed.

Cons:

• Less information on code.

What Learners Are Saying About this Course:

This section contains feedback that has been given by online learners about this course. Note that we have divided these reviews based on our main points mentioned below;

Content:

• The course discusses the main ideas about building a recommender system in the real world. It has more content than you would expect. It goes through simple topics as well as advanced ones. Most importantly Frank provides us with all the codes and explanations about the functionalities of those codes. (Ricardo Alexander H, ★★★★★)
• Well-structured syllabus and concise explanation of tech terms along with great use of cases. I really found the content to be useful and relevant. (Andrea L, ★★★★★)
• The content taught is really good and has helped me in understanding recommender systems in an interesting way with references given to research papers. The content was not too overwhelming and provided us with great detail on the topic. (Tushar Sandeep G, ★★★★★)
• This course provides excellent insight into various algorithms and goes a little deeper into some of them. A few algorithms and terminologies like SVD, KNN, RNN, softmax, classification and segmentation, and binary vs multiple classifications are explained at the right levels to get an overall understanding of how these can be used in recommender systems. Overall, this is a great course. Got a very good insight and would love to revisit this course multiple times and try all examples explained. (Ganesh G, ★★★★★)
• Very good content! It covers the main aspects around Recommender Systems with a good framework in Python that enables quick testing of the algorithms and evaluating the outcomes. It also provides an introduction to Deep Learning which is very useful to understand some concepts that are currently being applied to Recommender Systems. (Leandro Correa G, ★★★★★)
• Great course for someone with basic knowledge of machine learning, but needing an understanding of how that applies to recommender systems. Thorough exercises are available to explain different concepts. (Jenny M, ★★★★★)

Engagement:

• The instructor guides you throughout the entire course and the course itself is well-structured. The bleeding-edge alerts constitute an important part of the education process here. Now I feel ready to start reading more advanced stuff. The instructor gives you many paths you can follow after taking this course. His style of teaching is very engaging. (Luis Dernando R, ★★★★★)
• I love the way, Recommender Systems are explained in this training. It has touched upon every possible area and given a glimpse of future research too. And most importantly, the experience of the instructor in the same field has made this training even more informative. He has excellent teaching skills that allow me to learn new materials easily. (Santosh)
• The course content is presented clearly and concisely. I especially appreciated the practical wisdom that the instructorshares with the case studies. The course is very well-paced and the materials are excellent. (Marsh N, ★★★★★)
• This is a clear, engaging,and practical course. It was definitely well worth the money. The lectures kept me focused and intrigued me which motivated me to keep on learning. (Dmitry B, ★★★★★)
• This is a well-developed and planned curriculum. It contains engaginglectures with clear and step-by-step instructions. It also has relevant and practical examples that allowed me to memorize and learn different things. (Jarod J, ★★★★★)

Quality Practice:

• This course provides great explanations on different topics and gives us access to easy-to-follow hands-on exercises which makes it easy for us learners to practice whatever we learn in this course. (Shuria S, ★★★★★)

Learner’s Career Benefits:

• I was looking for a way to implement a specific solution to my need. I got something very, very different – a very thorough explanation of what I need to think about when implementing a solution to my problem, what options I may look at, and how I should evaluate results. This course is wonderful and was a joy to take part in. Highly recommended with a disclaimer that you may not get what you are looking for, but something much bigger. (Jan S, ★★★★★)
• This course was really helpful. I’m currently working on a recommender system and I can safely vouch for the in-depth detailing this course offers. (Abirami R, ★★★★★)

Learner’s Suggestions/Recommendations:

• At certain points, it seemed like the instructor was reading a book and his tone got monotonous. It would be helpful if the instructor opted for a more engaging and efficient style of teaching that would keep us hooked and intrigued.
• The course did not contain much information on code. I was looking forward to learning more about it but could not do so because of the lack of information. It would be appreciated if the instructor could expand on coding as well, as many of us are beginners.

Is this Course worth taking?

We believe that this course contains all the necessary information you would require when learning about recommender systems and how to create them using AI. It expands greatly on each topic and makes sure to discover everything in a detailed manner. It also contains hands-on activities along with additional resources to help you practice and review the materials. This course is perfect for engineers and developers who want to learn more about recommender systems.

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