Recommender Systems with Machine Learning from Coursera

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Syllabus

Introduction

In this module, we will introduce you to the field of AI Sciences and recommender systems. You will meet the instructor, explore the course layout, understand the basics of recommender systems, and preview the exciting projects you will undertake.

Motivation for Recommender System

In this module, we will delve into the motivations behind recommender systems. You will learn about their processes, historical evolution, and the critical role AI plays. We'll also cover practical applications and the challenges faced in real-world scenarios.

Basic of Recommender Systems

In this module, we will cover the foundational aspects of recommender systems. You will study the taxonomy, data matrices, evaluation techniques, and filtering methods, equipping you with a solid understanding of how these systems function and are assessed.

Machine Learning for Recommender System

In this module, we will focus on leveraging machine learning for recommender systems. You will gain insights into data preparation, explore filtering methods, and implement machine learning algorithms like tf-idf and KNN, enhancing the recommendation process.

Project 1: Song Recommendation System Using Content-Based Filtering

In this module, we will guide you through building a song recommendation system using content-based filtering. You will work on dataset management, genre exploration, and implement advanced techniques like tf-idf and FuzzyWuzzy to create effective song recommendations.

Project 2: Movie Recommendation System Using Collaborative Filtering

In this module, we will take you through developing a movie recommendation system using collaborative filtering. You will learn to analyze user and movie data, create collaborative filters, and apply KNN to generate accurate movie recommendations, culminating the course with practical applications.

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Provides hands-on experience through projects, allowing learners to apply theoretical knowledge to real-world scenarios and build practical skills in recommender systems

Covers essential taxonomies, data matrices, and evaluation techniques, providing a solid foundation for understanding how recommender systems function and are assessed

Teaches how to leverage machine learning algorithms like tf-idf and KNN, which are widely used in the field of recommender systems and data science

Requires only basic Python knowledge, making it accessible to individuals with limited programming experience who want to enter the field of machine learning

Explores the motivations behind recommender systems, their historical evolution, and the critical role AI plays, offering a comprehensive understanding of their significance

Employs Python, a versatile language used in machine learning, to evaluate datasets based on user ratings, choices, genres, and release years, which is standard practice

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Recommender Systems with Machine Learning with these activities:

Review Basic Python Syntax

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Reinforce your understanding of Python syntax to ensure a smooth start with the course's coding exercises.

Browse courses on Python Syntax

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Review data types, loops, and conditional statements.
Practice writing simple Python scripts.

Brush Up on Machine Learning Fundamentals

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Revisit key machine learning concepts to better grasp the application of ML in recommender systems.

Browse courses on Machine Learning

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Review the concepts of supervised and unsupervised learning.
Understand the basics of model evaluation and validation.

Read 'Recommender Systems: An Introduction'

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Gain a deeper understanding of recommender systems by studying a comprehensive introductory text.

View Recommender Systems on Amazon

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Read the chapters on collaborative filtering and content-based filtering.
Take notes on key concepts and algorithms.

Four other activities

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Implement KNN in Python

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Practice implementing the KNN algorithm to solidify your understanding of collaborative filtering techniques.

Browse courses on KNN

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Use scikit-learn to implement KNN.
Experiment with different distance metrics.

Build a Simple Movie Recommender

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Apply the concepts learned in the course to build a functional movie recommender system.

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Gather movie data from a public dataset.
Implement content-based filtering using TF-IDF.
Evaluate the performance of your recommender.

Study 'Programming Collective Intelligence'

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Explore advanced techniques and real-world applications of recommender systems.

View Programming the Semantic Web: Build Flexible... on Amazon

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Read the chapters on collaborative filtering and search engine algorithms.
Implement some of the examples in Python.

Write a Blog Post on Recommender Systems

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Solidify your understanding by explaining recommender systems concepts in a clear and concise manner.

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Choose a specific topic within recommender systems.
Research the topic and gather relevant information.
Write a blog post explaining the topic and its applications.

Career center

Learners who complete Recommender Systems with Machine Learning will develop knowledge and skills that may be useful to these careers:

Recommendation Systems Specialist

A recommendation systems specialist focuses on designing, implementing, and optimizing recommendation systems. This course provides the essential theoretical concepts and practical skills that are directly applicable to this role. It covers the taxonomy of recommender systems, evaluation techniques, and filtering methods, which are all crucial for a recommendation system specialist. The course is particularly useful because it teaches how to use Python to create content-based filtering and collaborative filtering systems, incorporating machine learning models. Projects to build a song and a movie recommendation system are valuable for any recommendation system specialist looking to refine their understanding.

See salaries and explore the career path for Recommendation Systems Specialist

Machine Learning Engineer

A machine learning engineer develops, tests, and implements machine learning models, and a solid understanding of recommender systems is often valuable. This course helps build a foundation in key aspects of machine learning, including how to evaluate datasets based on user data, and implement machine learning algorithms. This is helpful for a machine learning engineer so they can build and refine recommender systems. The course covers crucial concepts such as overfitting, underfitting, bias, and variance, which are essential to any machine learning role. Moreover the projects allow for hands-on experience with content based and collaborative filtering techniques using Python. By learning to implement these techniques and building custom systems, a machine learning engineer can improve their skills and knowledge.

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Data Scientist

Data scientists use data to solve problems and identify opportunities, and the ability to develop and deploy recommender systems can be a valuable asset for them. This course teaches the theoretical concepts and fundamental knowledge of various recommender systems. It also teaches how to use machine learning techniques in Python to evaluate datasets and build content-based and collaborative filtering models. A data scientist may find it particularly valuable to understand how to apply techniques like tf-idf and KNN within a recommender system context, as these methods can be applied in many other use cases. This course also allows for hands-on experience by building projects that implement content based and collaborative filtering.

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Applied Scientist

An applied scientist applies scientific knowledge to practical problems and may work on machine learning or recommender system problems. This course provides the fundamental knowledge of recommender systems including the taxonomy of the field and various filtering methods. The applied scientist may find it particularly useful that the course covers how to use machine learning models like KNN and to implement content-based and collaborative filtering in Python. This will help further their understanding of how to apply ML to real-world problems. This course also covers the concept of overfitting and underfitting.

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AI Software Developer

An AI software developer writes code for AI applications, and this course may be useful for a developer working on recommendation capabilities. This course provides an understanding of the AI behind recommender systems, including the essential taxonomies as well as practical approaches to building content-based and collaborative filtering techniques. An AI software developer would benefit from the practical Python projects included in the course, that help build hands on experience with different types of recommender systems. This will help their ability to integrate these methods into real world applications. The course’s focus on machine learning models and their implementation with Python can provide valuable skills to an AI software developer.

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Machine Learning Consultant

A machine learning consultant helps organizations implement machine learning solutions. A consultant with knowledge of recommender systems is helpful to them. This course covers the basic theory behind recommender systems, as well as machine learning techniques for building and implementing them. A machine learning consultant may find it helpful to learn about content based and collaborative filtering techniques, and how to implement them via Python. The projects will be particularly helpful to gain hands on experience. This course also covers concepts such as bias, variance, overfitting, and underfitting, which are necessary for any machine learning consultant.

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Research Scientist

A research scientist investigates and develops new scientific knowledge, often in the field of artificial intelligence, and may find that the development and deployment of recommender systems is a valuable skill. This course helps build a foundation in machine learning as applied to recommender systems, including key concepts such as bias and variance. A research scientist may find it particularly helpful to gain hands-on experience through the projects included in the course, that allow a learner to evaluate datasets using various data points. These projects can also help them better understand how to build and evaluate recommender systems. Those pursuing an advanced degree will likely find this course helpful.

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Algorithm Developer

An algorithm developer creates and refines algorithms for various applications, and recommender systems are an area needing novel algorithms. This course provides a strong foundation in machine learning and recommender systems including content-based and collaborative filtering techniques. This would help an algorithm developer better design algorithms for this specific use case. The practical Python projects in the course allow for hands-on experience with building recommendation systems. An algorithm developer may find that the course’s coverage of concepts such as overfitting and underfitting are particularly valuable. By integrating these concepts into their work, they can improve their designs.

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AI Consultant

An AI consultant advises organizations on how to leverage artificial intelligence, and an understanding of recommender systems is useful to them. This course introduces the concepts and fundamental knowledge of recommender systems, including taxonomies and various machine learning models. An AI consultant may find that the course’s discussion of the basic of recommender systems, and how they are evaluated, is particularly valuable. The understanding of content-based and collaborative filtering techniques, as well as the practical experience given through the hands-on projects, can help an AI consultant improve their skill set. This also helps them provide well informed advice on the topic of AI.

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Data Engineer

A data engineer builds and maintains the infrastructure needed for data processing and analysis, and understanding how data is used in recommender systems is valuable to them. This course teaches how datasets are evaluated for building recommender systems, using Python. This includes how data points such as user ratings, choices, genres, and release years are used in recommendation engines. A data engineer may find that the course’s hands on projects, which allow learners to implement content based and collaborative filtering techniques, are particularly helpful. The course’s coverage of machine learning models, as they relate to recommender systems, may also be relevant to a data engineer.

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Data Analyst

A data analyst interprets data to identify trends and patterns, and understanding recommender systems may be quite valuable for them. This course covers how data is used in recommender systems and provides practical experience in evaluating datasets using Python. The course highlights key evaluation techniques, as well as machine learning models used in recommender systems. A data analyst may find that the course’s focus on the usage of data including user ratings, choices, genres, and release years to build recommendation systems is particularly informative. The course’s practical projects allow a learner to implement content based and collaborative filtering techniques on data.

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Business Intelligence Analyst

A business intelligence analyst uses data to inform business decisions, and knowledge of recommender systems may be useful to them. This course teaches how to use machine learning in recommender systems to evaluate datasets and make predictions. A business intelligence analyst may find the course helpful, particularly for developing an intuition about how data can be leveraged to personalize user experiences. The course includes projects that provide hands-on experience building recommendation systems, and evaluating them in Python. This helps build a practical skill set for a business intelligence analyst.

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Software Engineer

A software engineer designs and develops software, and this course may be useful for one who works on systems that involve recommendation algorithms. This course helps build a foundation in the various techniques used in recommender systems, including content-based and collaborative filtering. This course also teaches Python, which is a language that many software engineers use. The course’s hands on projects involve building and implementing these techniques, which can be useful to a software engineer. It also covers important concepts like overfitting and underfitting that help with producing high quality software.

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Product Manager

A product manager guides the development and success of a product. This course may be useful to a product manager looking to create a product that features some type of recommendation engine. Understanding recommender systems may help them make informed decisions about what to build in a product. The course covers the basics of recommender systems, different techniques such as content-based and collaborative filtering, and projects that help the learner create their own. This course also covers the taxonomy of recommender systems. This can help a product manager better guide their engineering teams.

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Quantitative Analyst

A quantitative analyst uses mathematical and statistical models for data analysis. This course teaches machine learning models that are used for recommender systems, which may be useful to them. This course covers content based and collaborative filtering techniques, and evaluation techniques that are used in recommender systems. A quantitative analyst may find that learning more about this specific application of machine learning can improve their skill set. This course teaches how to use Python to implement and evaluate these techniques and includes practical projects.

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Recommender Systems with Machine Learning

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