Build a Movie Recommendation System Using Python
Learn to build a movie recommendation system using Python, covering system types, implementation steps, and a hands-on demo. Gain practical skills in data manipulation and analysis for creating personalized movie suggestions.
Recommendation Systems
Learn to build diverse recommendation systems using Python, from hybrid techniques to collaborative filtering, exploring key concepts like cosine similarity for practical applications.
Machine Learning
Comprehensive introduction to key machine learning concepts, algorithms, and applications, covering testing, error metrics, recommendation systems, and various classification methods.
Python Projects for Beginners - Great Learning
Comprehensive tutorial covering 13 Python projects for beginners, from basic concepts to advanced applications like face recognition and machine learning, with hands-on demonstrations and examples.
Understanding Structure in Data - Week 3
Explore data structure analysis using Julia, covering matrix rank, data visualization, PCA, and building a simple recommendation engine. Gain practical insights into advanced data science concepts.
Building a Personalised Attrition Recommendation System
Comprehensive guide to building an attrition recommendation system, covering data analysis, predictive modeling, and strategy development to prevent employee churn and improve workforce retention.
Empfehlungssysteme und KI - Von Pommesbuden bis zu Online-Shops
Einblick in Empfehlungssysteme und KI: Von Online-Shops bis zur Pommesbude. Prof. Schildgen erklärt Funktionsweisen, Möglichkeiten und überträgt Konzepte auf alltägliche Situationen.
ShadeWatcher: Recommendation-Guided Cyber Threat Analysis Using System Audit Records
Explore recommendation-guided cyber threat analysis using system audit records, enhancing detection effectiveness and efficiency through innovative connectivity modeling and learning techniques.
Economics and Learning
Explore the intersection of economics and machine learning, focusing on game theory, recommendation systems, and decentralized models in data science.
Facebook's Toolkit for Safety and Security - F8 2019
Explore Facebook's systematic approach to balancing user expression with safety and inclusivity, featuring design principles and strategies for creating secure online communities.
Michael I. Jordan on the Future of AI and Machine Learning - ODSC West 2019
Renowned AI expert Michael Jordan discusses the future of machine learning, exploring potential billion-dollar industries and addressing key challenges in the field of data science and AI.
Building a Knowledge Graph with Spark and NLP for Novel Drug Recommendations
Explore drug discovery using knowledge graphs, NLP, and Spark. Learn techniques for efficient data processing, entity recognition, and relationship extraction to enhance pharmaceutical research.
Bala Krishnamoorthy - Steinhaus Filtration and Stable Paths in the Mapper
Explore Steinhaus filtration and stable paths in Mapper, a topological data analysis tool. Learn applications in recommendation systems and explainable machine learning for data-driven insights.
Content Curation in Online Platforms - Impact and Improvement Strategies
Explore the impact of content curation on online platforms, examining moderation practices, user behavior, and the potential of academic research to guide policy and improve digital experiences.
Evaluating Recommendation Algorithms - Offline and Online Approaches
Discover how Delivery Hero optimizes recommendation algorithms through offline evaluations and A/B testing, enhancing user experience while maintaining business metrics.
Analyzing User Engagement with TikTok's Short Format Video Recommendations
Explore user engagement on TikTok through data donations, revealing insights on daily usage, attention span, and video preferences in short-format content recommendations.
Building Robust and Scalable Recommendation Engines for Online Food Delivery
Unlock the power of recommendation engines for food delivery. Learn data preprocessing, feature engineering, and scalable algorithms to create personalized recommendations using the DHRD dataset.
Recommendation Systems for Digital Out of Home Advertising
Explore probabilistic modeling for digital out-of-home advertising content recommendations. Learn strategies for effective ad distribution across digital screens and gain insights into data-driven decision-making for content selection and placement.
Accelerators Made Easy - Improving MLOps at Loblaw Digital Using Vertex AI
Leverage Vertex AI for efficient matrix multiplication in MLOps. Build scalable recommendation engines using Google Cloud Platform's tools for improved product relevance.
How to Monitor Machine Learning Stacks - Detecting and Addressing Special Issues
Discover advanced monitoring techniques for ML stacks, addressing unique challenges beyond traditional metrics to ensure robust performance and early issue detection.
Knowledge Graph Recommendation Systems for COVID-19
Explore knowledge graph recommendation systems for COVID-19, leveraging Python skills to enhance understanding of pandemic-related data and insights.