Building Recommender Systems with Machine Learning and AI

Name: Comidoc Review
Rating: 4.6597743
Author: Comidoc

How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.

4.66 (3483 reviews)

Udemy

platform

English

language

Software Engineering

Why take this course?

🌟 Course Title: Building Recommender Systems with Machine Learning and AI

🎓 Course Description:

Updated with Neural Collaborative Filtering (NCF), Tensorflow Recommenders (TFRS) and Generative Adversarial Networks for recommendations (GANs)

Dive into the world of recommendation systems with our comprehensive course, "Building Recommender Systems with Machine Learning and AI." This course is a treasure trove for data scientists, engineers, and enthusiasts looking to master the art of personalized content and product recommendations. 🚀

Why Take This Course?

Industry-Relevant Skills: Learn from Amazon's pioneering work in recommendation systems, as taught by Frank Kane, who led Amazon's personalized product recommendation systems for over nine years.
Real-World Impact: Recommendation algorithms are crucial for tech giants like Netflix, YouTube, and Amazon. Understanding these technologies can significantly enhance your value to potential employers.

Course Highlights:

Hands-On Learning: Engage with practical exercises that allow you to build your own recommendation systems, evaluating and combining various algorithms.
Comprehensive Coverage: From the basics of collaborative filtering to advanced techniques like deep learning and AI, this course leaves no stone unturned.

What You'll Learn:

✅ Building a Recommendation Engine ✅ Evaluating Recommender Systems ✅ Content-based Filtering using item attributes ✅ Neighborhood-based Collaborative Filtering with user-based, item-based, and KNN CF ✅ Model-based Methods including matrix factorization and Singular Value Decomposition (SVD) ✅ Applying Deep Learning & AI to Recommendations ✅ Using Tensorflow Recommenders (TFRS), Amazon Personalize, and the latest frameworks ✅ Session-based Recommendations with Recursive Neural Networks (RNNs) ✅ Building Modern Recommenders with Neural Collaborative Filtering (NCF) ✅ Scaling to Large Data Sets using Apache Spark, AWS SageMaker, and more ✅ Real-World Challenges and Solutions in Recommender Systems ✅ Case Studies from YouTube and Netflix ✅ Building Hybrid, Ensemble Recommenders ✅ Staying Up-to-Date with "Bleeding Edge Alerts"

Course Format:

This course is hands-on and highly interactive. You'll use Python to build your own recommendation engine, evaluating and combining different algorithms. We provide an introduction to Python for beginners and a deep dive into deep learning for those new to AI. 👩‍💻🧙‍♂️

Who is this course for?

This course is designed for:

Data Scientists and Engineers who want to build recommendation systems.
Developers seeking to enhance their machine learning skills with real-world applications.
AI Enthusiasts looking to explore the latest trends in recommenders.

Prerequisites:

Basic knowledge of programming, as the course uses Python for coding exercises.
An understanding of computer algorithms, with a focus on learning new concepts quickly.

Additional Resources:

High-quality closed captions in English are provided to ensure clarity and accessibility.

Join us on this exciting journey into the world of machine learning and AI-driven recommendation systems! 🎉 Enroll now and start building the future of personalized content recommendations.

Course Gallery

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Comidoc Review

Our Verdict

Building Recommender Systems with Machine Learning and AI is an insightful course that offers a wide-ranging exploration of various recommendation algorithms while enriching the learning experience with real-world examples. However, its potential is hindered by the absence of visual aids and adequate code explanation depth, leaving room for improvement in helping learners grasp complex concepts more intuitively.

What We Liked

Comprehensive coverage of various recommendation algorithms and techniques, including collaborative filtering, deep learning, matrix factorization methods, hybrid models, and K-Nearest-Neighbors
Exposes students to real-world learnings from platforms like Netflix and YouTube, enabling better understanding and practical application
Incorporates a wide range of advanced topics, making it an excellent resource for those looking to delve deeper into the field
Provides useful code samples, enabling students to implement and experiment with recommendation systems concepts

Potential Drawbacks

Lacks visual aids and animated cues to facilitate better comprehension, causing some of the explanations to feel complex and lengthy
Important applications such as the 'hybrid model' are briefly covered, leaving students wanting more in-depth treatment
The course might benefit from the inclusion of graded assignments or quizzes to help students better understand the presented algorithms
Code explanations could be improved with more patience and detailed walkthroughs, making it easier for beginners to follow along