Ementorhub

What you'll learn

Understand the basics of AI-integrated recommender systems

Analyze the impact of overfitting, underfitting, bias, and variance

Apply machine learning and Python to build content-based recommender systems

Create and model a KNN-based recommender engine for applications

Skills you'll gain

Data Cleansing

Feature Engineering

Applied Machine Learning

Data Analysis

Machine Learning

Artificial Intelligence and Machine Learning (AI/ML)

Data Manipulation

Predictive Modeling

Unsupervised Learning

Machine Learning Algorithms

Taxonomy

Data Processing

Supervised Learning

AI Personalization

There are 6 modules in this course

You'll learn to use Python to evaluate datasets based on user ratings, choices, genres, and release years. Practical approaches will help you build content-based and collaborative filtering techniques. As you progress, you'll cover necessary concepts for applied recommender systems and machine learning models, with projects included for hands-on experience. Key learnings include AI-integrated basics, taxonomy, overfitting, underfitting, bias, variance, and building content-based and item-based systems with ML and Python, including KNN-based engines. The course is suitable for beginners and those with some programming experience, aiming to advance ML skills and build customized recommender systems. No prior knowledge of recommender systems, ML, data analysis, or math is needed, only basic Python. By the end, you'll relate theories to various domains, implement ML models for real-world recommendation systems, and evaluate them.

Motivation for Recommender System

Basic of Recommender Systems

Machine Learning for Recommender System

Project 1: Song Recommendation System Using Content-Based Filtering

Project 2: Movie Recommendation System Using Collaborative Filtering