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This specialization will teach you to build advanced recommender systems using machine learning and AI. You will begin by learning Python to evaluate datasets and create content-based and collaborative filtering systems. The specialization covers essential concepts like overfitting, bias, and variance, and introduces you to models such as KNN for building recommendation engines. As you progress, you'll dive deeper into deep learning models like RNNs, GRUs, and LSTMs, using TensorFlow to solve real-world problems.
You’ll also learn how to apply advanced techniques like Restricted Boltzmann Machines (RBM) and Autoencoders in recommendation systems. The specialization includes practical projects, such as the Amazon Product Recommendation System, where you’ll analyze data, prepare it, and develop recommendation models using deep learning approaches.
By the end of the specialization, you will be able to design and implement content-based and collaborative filtering recommender systems, apply deep learning models such as RNNs, and develop recommendation engines with TensorFlow. Ideal for aspiring data scientists and ML engineers. Basic Python knowledge and familiarity with neural networks and deep learning are helpful.
Applied Learning Project
In this specialization, you will apply your skills to build real-world recommender systems, including projects like an Amazon Product Recommendation System. You'll explore collaborative filtering, content-based techniques, and deep learning models like RNNs, and gain practical experience using TensorFlow and Autoencoders.
