LearnFlat · AI Applications (Interdisciplinary) · AI for Marketing & Sales

AI-Powered Personalization

Implement AI-driven personalization engines to deliver unique customer experiences across websites, emails, and apps, including product recommendations and dynamic content.

94 courses

Designing Recommender Systems with Python and Machine Learning

Build and evaluate personalized recommendation engines using collaborative filtering, matrix factorization, and deep learning techniques in Python.

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Building Recommender Systems for Modern Applications

Learn the foundational principles of personalized content delivery and how to build effective recommendation engines for digital products.

Building Recommendation Systems in Python

Master the fundamentals of recommendation engines by building content-based and collaborative filtering systems using Python and modern data science tools.

Foundations of Recommender Systems: Non-Personalized and Content-Based

Learn to build foundational recommendation engines using summary statistics, product associations, and modern content-based filtering techniques.

Machine Learning Recommendation Engines on Cloud Platform

Learn to design, build, and deploy scalable recommendation systems using collaborative filtering, embeddings, and modern machine learning pipelines on Cloud Platform.

Building Recommendation Systems with Collaborative Filtering

Learn to implement user-user and item-item nearest neighbor algorithms to build personalized recommendation engines using Python.

Building Recommender Systems with Matrix Factorization

Learn how to design, build, and evaluate collaborative filtering models and hybrid recommendation engines using Python, even if you are new to machine learning.

Customer Experience Strategy for Data and Digital Professionals

Learn to transition from basic customer satisfaction metrics to modern, data-driven CX strategies that align automation and digital touchpoints with business success.

Introduction to Recommendation Systems in Python

Learn the fundamentals of collaborative filtering, content-based filtering, and modern vector embeddings to build your first movie recommendation engine.

Foundations of Recommendation Systems

Learn how to design, build, and evaluate recommendation engines using collaborative filtering, content-based filtering, and vector similarity techniques.

Vertex AI Search for Retail: Building Smart E-Commerce Search

Configure Vertex AI Search for Retail to deliver highly relevant, personalized product search results and elevate the digital shopping experience for your customers.

Building Recommendation Systems with Python

Learn to design and implement collaborative filtering, content-based, and hybrid recommendation algorithms using Python to deliver personalized user experiences.

Introduction to Recommender Systems

Learn the fundamentals of collaborative and content-based filtering to build and evaluate your first recommendation engines.

Designing and Evaluating Recommender Systems: A Practical Project Guide

Learn to analyze, design, and evaluate recommendation algorithms through a comprehensive case study approach, perfect for building your first portfolio project.

Python Recommender Systems: Build a Personalized Book Engine

Master the fundamentals of recommendation engines by building a functional book suggestion system using Python, collaborative filtering, and modern data techniques.

Building Modern Recommender Systems with Machine Learning

Learn to design, evaluate, and deploy intelligent recommendation engines using collaborative filtering, hybrid models, and modern vector search techniques.

Python Data Science Project: Build a Book Recommendation Engine

Learn how to design, code, and evaluate a personalized book recommendation system using Python, pandas, and content-based filtering techniques.

Building Movie Recommendation Engines with Python

Learn to design and build collaborative, content-based, and popularity-driven recommendation systems using Python and modern data analysis libraries.

Designing Recommender Systems with Machine Learning

Learn to build collaborative filtering, content-based, and deep learning recommendation models to deliver personalized user experiences.

Recommendation Systems: A Beginner's Implementation Guide

Learn to design and build personalized recommendation algorithms using machine learning and deep learning techniques through structured, text-based examples.

Showing 20 of 94 courses