🔥Artificial Intelligence [2026 Updated] | Artificial Intelligence Course | Artificial Intelligence And Machine Learning Tutorials | Simplilearn - Movie Recommender System Using Machine Learning | Movie Recommender System Project | Simplilearn

"️🔥 Michigan Engineering - Professional Certificate in AI and Machine Learning - https://www.simplilearn.com/professional-aiml-program?utm_campaign=lXcO3y_YGOM&utm_medium=Comments&utm_source=Youtube ️🔥IITK - Professional Certificate Course in Generative AI and Machine Learning - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=lXcO3y_YGOM&utm_medium=Comments&utm_source=Youtube ️🔥Microsoft AI Engineer Program - https://www.simplilearn.com/ai-engineer-course?utm_campaign=lXcO3y_YGOM&utm_medium=Comments&utm_source=Youtube ️️🔥 Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utm_campaign=lXcO3y_YGOM&utm_medium=Comments&utm_source=Youtube" In this video on Movie Recommender System Using Machine Learning,we are going to cover how to build a movie recommendation system using Python. This video will help you to understand the introduction of the recommendation system and hands-on lab in which we will do Filtering Strategies of the recommendation system - Filtering based on content, and Collaboration filtering. Below are the topics covered in this Movie recommender system project:- 00:00 Movie recommendation system using machine learning 00:02 Introduction to the recommender system 02:34 Hands-on lab demo ➡️ How does the movie recommendation system work? Every recommender system primarily consists of two components: users and items. Users receive movie predictions from the system, and the actual movies are the products. Filtering and predicting only the movies that a matching user is most likely to wish to see is the main objective of movie recommendation systems. The user information from the system's database is used by the ML algorithms for these recommendation systems. Based on information from the past, this data is used to forecast the user in question's behavior in the future. ➡️ Filtering Strategies of recommendation system - Filtering based on content A method of filtering movies in movie recommendation systems that takes advantage of the items' data (movies). This information, which is taken from just one user, is quite important in this case. This technique uses an ML algorithm to suggest movies that are comparable to the user's past choices. Therefore, the information about the prior movie choices and likes of just one person is used to generate similarity in content-based filtering. Link for the datasets: https://drive.google.com/drive/folders/10P7C-suaBAA5_ZAi4oEO_Pmsb7kG6JMZ?usp=sharing ✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the Artificial Intelligence training videos: https://youtube.com/playlist?list=PLEiEAq2VkUULa5aOQmO_al2VVmhC-eqeI #Project #ML #ArtificialIntelligence #AIML #MachineLearning #Simplilearn ➡️ About Artificial Intelligence Engineer This Artificial Intelligence Engineer course Created in partnership with IBM, this course introduces students to blended learning and prepares them to be AI and Data Science specialists. In Armonk, New York, IBM is a significant cognitive service and integrated cloud solution firm that provides many technology and consulting solutions. IBM is a leader in AI and Machine Learning technology verticals for 2021. This AI masters course will prepare students for Artificial Intelligence and Data Analytics careers. ✅ Key Features - Add the IBM Advantage to your Learning - 25 Industry-relevant Projects and Integrated labs - Immersive Learning Experience - Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Tools Covered - ChatGPT - Flask - Matplotlib - django - Python - Numpy - Pandas - SciPy - Keras - OpenCV - And Many More… 👉 Learn More At: https://www.simplilearn.com/professional-aiml-program?utm_campaign=27June2023MovieRecommenderSystemUsingMachineLearning&utm_medium=Description&utm_source=youtube