The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques
InstructorLazy Programmer Inc.The curriculum emphasizes building algorithms from scratch using various tools like Numpy and Keras, which provides a strong foundation for understanding underlying mechanics.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide introduces the course's in-depth approach to algorithm derivation, displaying a complex mathematical formula for calculating weights involving summations and matrix operations.
This slide introduces the topic of user retention on YouTube by displaying a classic interface screenshot of the recommended video feed.
This slide illustrates the application of recommender systems across various industries by displaying examples from e-commerce and streaming platforms like Amazon, Netflix, and Spotify.
This slide illustrates how various content feeds from platforms like LinkedIn, Google, and Reddit relate to statistical concepts, specifically showing a Bayesian probability graph.
Instruction spans multiple environments, including distributed computing with Spark on AWS/EC2 and deep learning via Keras and Tensorflow.
Mathematical expressions are explained thoroughly in some areas, but other sections like Restricted Boltzmann Machines may require external resources for sufficient intuition. Note that one such signal was recorded before the displayed update date; as the update label does not prove a correction, this remains an uncertain signal.
The curriculum can feel disconnected due to frequent references to the instructor's other works, and some learners report that difficult concepts are occasionally skipped.
The curriculum aligns with the technical requirements for advanced sections, though some learners find the course relies too heavily on prior knowledge from other courses.
