Recommender Systems and Deep Learning in Python

Designed for intermediate Python developers and data scientists, this comprehensive course by Lazy Programmer Inc. demystifies the mechanics behind modern recommender systems. Students will dive deep into both classical collaborative filtering and cutting-edge deep learning approaches. You will start with the mathematical foundations of matrix factorization and Singular Value Decomposition (SVD) using pure NumPy, then scale up your solutions using big data technologies like Apache Spark on AWS EC2 clusters. The curriculum transitions smoothly into modern deep learning techniques, leveraging Keras to build complex matrix factorization models, deep neural networks, residual networks, and autoencoders. Additionally, you will explore advanced architectures like Restricted Boltzmann Machines (RBM) using TensorFlow. This course is ideal for software engineers, machine learning practitioners, and AI enthusiasts who want to implement high-performance, industry-grade recommendation engines from scratch. By the end of this program, you will possess a robust portfolio of algorithms capable of delivering accurate, personalized user experiences at scale.