How to Build a Production-Grade Movie Recommender in Python

How to Build a Production-Grade Movie Recommender in Python, available at Free, with 5 lectures, and has 393 subscribers.

You will learn about Understand the Basics of Machine Learning: Learn the fundamental concepts of machine learning and how they apply to building recommendation systems. Master Data Manipulation Using Python: Gain proficiency in using Python and the pandas library to import, clean, and manipulate large datasets effectively. Apply Text Processing Techniques: Learn to use techniques such as CountVectorizer and TF-IDF for processing textual data to enhance the quality of recommendatio Implement Machine Learning Algorithms: Utilize scikit-learn to implement algorithms that enable personalized movie recommendations based on user preferences. Design and Build a User Interface: Create an intuitive user interface using Streamlit, making the system accessible and easy to use for end-users. Evaluate and Interpret Model Performance: Understand how to evaluate the effectiveness of recommendation models using metrics like cosine similarity. Serialize and Deploy Machine Learning Models: Learn how to use serialization with pickle to save and load trained models, enabling their use in real-world appli Incorporate Feedback and Improve the System: Understand methods for incorporating user feedback into the recommendation system This course is ideal for individuals who are Aspiring Data Scientists: Individuals looking to enter the field of data science and wish to gain practical experience in building real-world machine learning applications. or Software Developers: Programmers who want to diversify their skills by learning about machine learning and data-driven application development. or Students in Computer Science: University students or lifelong learners seeking to enhance their understanding of applied machine learning. or Tech Enthusiasts: Anyone with a general interest in technology and how machine learning can be applied to solve problems in innovative ways. It is particularly useful for Aspiring Data Scientists: Individuals looking to enter the field of data science and wish to gain practical experience in building real-world machine learning applications. or Software Developers: Programmers who want to diversify their skills by learning about machine learning and data-driven application development. or Students in Computer Science: University students or lifelong learners seeking to enhance their understanding of applied machine learning. or Tech Enthusiasts: Anyone with a general interest in technology and how machine learning can be applied to solve problems in innovative ways.

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Summary

Title: How to Build a Production-Grade Movie Recommender in Python

Price: Free

Number of Lectures: 5

Number of Published Lectures: 5

Number of Curriculum Items: 5

Number of Published Curriculum Objects: 5

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

• Understand the Basics of Machine Learning: Learn the fundamental concepts of machine learning and how they apply to building recommendation systems.
• Master Data Manipulation Using Python: Gain proficiency in using Python and the pandas library to import, clean, and manipulate large datasets effectively.
• Apply Text Processing Techniques: Learn to use techniques such as CountVectorizer and TF-IDF for processing textual data to enhance the quality of recommendatio
• Implement Machine Learning Algorithms: Utilize scikit-learn to implement algorithms that enable personalized movie recommendations based on user preferences.
• Design and Build a User Interface: Create an intuitive user interface using Streamlit, making the system accessible and easy to use for end-users.
• Evaluate and Interpret Model Performance: Understand how to evaluate the effectiveness of recommendation models using metrics like cosine similarity.
• Serialize and Deploy Machine Learning Models: Learn how to use serialization with pickle to save and load trained models, enabling their use in real-world appli
• Incorporate Feedback and Improve the System: Understand methods for incorporating user feedback into the recommendation system

Who Should Attend

• Aspiring Data Scientists: Individuals looking to enter the field of data science and wish to gain practical experience in building real-world machine learning applications.
• Software Developers: Programmers who want to diversify their skills by learning about machine learning and data-driven application development.
• Students in Computer Science: University students or lifelong learners seeking to enhance their understanding of applied machine learning.
• Tech Enthusiasts: Anyone with a general interest in technology and how machine learning can be applied to solve problems in innovative ways.

Target Audiences

• Aspiring Data Scientists: Individuals looking to enter the field of data science and wish to gain practical experience in building real-world machine learning applications.
• Software Developers: Programmers who want to diversify their skills by learning about machine learning and data-driven application development.
• Students in Computer Science: University students or lifelong learners seeking to enhance their understanding of applied machine learning.
• Tech Enthusiasts: Anyone with a general interest in technology and how machine learning can be applied to solve problems in innovative ways.