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Literacy Essentials
Core Concepts Recommender Systems
Biswanath Halder
Biswanath Halder
This course will teach you different types of recommendation techniques to suggest new items based on users' past interaction history in order to improve the overall user experience and increase the sales and revenue for the enterprise.
Read more
This course will teach you different types of recommendation techniques to suggest new items based on users' past interaction history in order to improve the overall user experience and increase the sales and revenue for the enterprise.
Read more
This course will teach you different types of recommendation techniques to suggest new items based on users' past interaction history in order to improve the overall user experience and increase the sales and revenue for the enterprise.
Data is the new fuel of the modern world and artificial intelligence is the accelerator. Almost every aspect of our modern life is influenced by data-driven systems. A recommender system is one such system that leverages historical usage data to provide personalized recommendations to customers improving overall customer experience and increasing the sales and revenue for the enterprise. In this course, Literacy Essentials: Core Concepts Recommender Systems, you’ll learn to build recommendation engines with the help of Python. First, you’ll learn what recommendation systems are and explore how to evaluate them. Next, you’ll discover different types of recommendation techniques. Then, you'll explore collaborative filtering in detail. Finally, you’ll cover how to build state-of-the-art recommendations systems for a global enterprise using Python. When you’re finished with this course, you’ll have the skills and knowledge of Literacy Essentials: Core Concepts Recommender Systems needed to enhance the sales as well as the user experience based on appropriate product suggestions.
Register for this course and see more details by visiting:
OpenCourser.com/course/2cgpvb/literacy
What's inside
Syllabus
Course Overview
Introduction to Recommendation Systems
Collaborative-filtering Based Recommendation Systems
Build a Product Recommendation System for Globomantics Using Python
Read more
Syllabus
Course Overview
Introduction to Recommendation Systems
Collaborative-filtering Based Recommendation Systems
Build a Product Recommendation System for Globomantics Using Python
Read more
Other Approaches to Generate Recommendations
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops collaborative filtering skills, which are highly relevant in recommendation system development
Taught by Biswanath Halder, who is recognized for their work in recommendation systems
May require learners to have prior knowledge of recommendation systems or data science
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Activities
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Literacy Essentials: Core Concepts Recommender Systems.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Literacy Essentials: Core Concepts Recommender Systems will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze data and develop models to help organizations make better decisions. They typically have a strong background in statistics, computer science, and business. LinkedIn reports that Data Scientists with recommendation systems skills can earn a 10-20% salary premium, indicating strong demand for this niche expertise. This course provides a solid foundation in recommendation system techniques, which can be beneficial for those seeking to enter or advance as Data Scientists.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help organizations understand their customers, improve their products and services, and make better decisions. A strong understanding of recommendation systems can enhance a Data Analyst's ability to develop more accurate and useful data-driven insights for organizations. This course provides a comprehensive overview of different recommendation system techniques, helping Data Analysts build a foundation for success in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve business problems. They typically have a strong background in computer science, statistics, and data analysis. Recommendation systems are a type of machine learning model, and this course can help Machine Learning Engineers build the necessary skills to develop and deploy successful recommendation systems.
UX Designer
UX Designer
UX Designers focus on the user experience of products and services. They typically have a strong background in human-computer interaction, psychology, and design. Recommendation systems can play a significant role in improving the user experience, and this course provides UX Designers with the knowledge and skills to design and implement effective recommendation systems.
Product Manager
Product Manager
Product Managers are responsible for the development and success of a product. They typically have a strong understanding of customer needs, market trends, and technology. A solid grasp of recommendation systems can help Product Managers make better decisions about product features, pricing, and marketing strategies. This course provides a practical understanding of how recommendation systems work and how to use them to improve the user experience of products.
Business Analyst
Business Analyst
Business Analysts help organizations improve their business processes and make better decisions. They typically have a strong understanding of business, technology, and data analysis. This course can help Business Analysts develop a deeper understanding of recommendation systems and how to use them to improve business outcomes.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns to promote and sell products and services. A strong grasp of recommendation systems can help Marketing Managers personalize marketing campaigns and increase conversion rates. This course provides Marketing Managers with a practical understanding of how recommendation systems work and how to use them to improve their marketing efforts.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and managing sales teams to achieve sales goals. A sound understanding of recommendation systems can help Sales Managers identify and target potential customers, develop effective sales strategies, and improve customer relationships. This course provides Sales Managers with a practical understanding of how recommendation systems work and how to use them to improve their sales performance.
Digital Marketing Specialist
Digital Marketing Specialist
Digital Marketing Specialists are responsible for developing and executing digital marketing campaigns to promote and sell products and services. A sound understanding of recommendation systems can help Digital Marketing Specialists personalize marketing campaigns and increase conversion rates. This course provides Digital Marketing Specialists with a practical understanding of how recommendation systems work and how to use them to improve their digital marketing efforts.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are successful with a company's products and services. A strong grasp of recommendation systems can help Customer Success Managers identify and address customer needs, develop personalized onboarding and training programs, and increase customer retention. This course provides Customer Success Managers with a practical understanding of how recommendation systems work and how to use them to improve their customer success efforts.
Business Development Manager
Business Development Manager
Business Development Managers are responsible for identifying and developing new business opportunities for a company. A strong grasp of recommendation systems can help Business Development Managers identify and target potential customers, develop effective sales strategies, and improve customer relationships. This course provides Business Development Managers with a practical understanding of how recommendation systems work and how to use them to improve their business development efforts.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and completing projects. They typically have a strong background in project management, leadership, and communication. While this course may not directly teach project management skills, it can be beneficial for Project Managers who want to develop a deeper understanding of recommendation systems and how to use them to improve project outcomes.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines and infrastructure. They typically have a strong background in computer science, data analysis, and software engineering. While this course may not directly teach data engineering skills, it can be beneficial for Data Engineers who want to develop a deeper understanding of recommendation systems and how to use them to improve data pipelines and infrastructure.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They typically have a strong background in computer science, software engineering, and programming. While this course may not directly teach software engineering skills, it can be beneficial for Software Engineers who want to develop a deeper understanding of recommendation systems and how to use them to improve software systems.
Data Architect
Data Architect
Data Architects design and manage data systems and architectures. They typically have a strong background in data management, data warehousing, and data integration. While this course may not directly teach data architecture skills, it can be beneficial for Data Architects who want to develop a deeper understanding of recommendation systems and how to use them to improve data systems and architectures.
For more career information including salaries, visit:
OpenCourser.com/course/2cgpvb/literacy
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Literacy Essentials: Core Concepts Recommender Systems.
This textbook provides a comprehensive overview of recommender systems, covering various techniques, evaluation methods, and applications. It serves as an excellent resource for both students and practitioners.
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Presents a comprehensive survey of recommender system methods and algorithms. It provides a deeper understanding of the theoretical foundations and practical aspects of building effective recommender systems.
For those interested in a more focused study of collaborative filtering techniques used in recommender systems, this book provides a comprehensive treatment of the subject.
Provides a practical introduction to data analysis with Python. It covers the fundamentals of data analysis, and it includes numerous examples and exercises to help readers apply their knowledge. It will be a useful reference to readers who are new to data analysis, and it can also serve as a supplement to this course.
Provides a practical introduction to data mining and machine learning using Python. It covers the fundamental concepts and techniques used in data mining and machine learning, and it includes numerous examples and exercises to help readers apply their knowledge. It will be a useful reference to readers who are new to data mining and machine learning, and it can also serve as a supplement to this course.
Dives deep into natural language processing, an essential field in the broader scope of data science. The book features helpful references for natural language processing concepts and techniques that enhance recommender systems.
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Provides a comprehensive survey of deep learning, including the latest advances in the field. It covers both the theoretical foundations of deep learning and its practical applications. Readers who want to gain a deeper understanding of the cutting-edge techniques of deep learning will find this book to be an invaluable resource.
Provides a comprehensive introduction to reinforcement learning, including both the theoretical foundations and practical applications. Readers who want to gain a deeper understanding of the techniques of reinforcement learning will find this book to be an invaluable resource.
Provides a comprehensive introduction to pattern recognition and machine learning, including both the theoretical foundations and practical applications. Readers who want to gain a deeper understanding of the techniques of pattern recognition and machine learning will find this book to be an invaluable resource.
Provides a comprehensive introduction to statistical learning, including both the theoretical foundations and practical applications. Readers who want to gain a deeper understanding of the techniques of statistical learning will find this book to be an invaluable resource.
Focuses on Bayesian reasoning and its applications in machine learning. Readers who want to enhance their statistical background in machine learning with a specific focus on Bayesian methods will find this book useful.
Provides detailed information retrieval techniques, including approaches commonly used in recommender systems. It's an excellent resource for background knowledge on search engine technologies and text mining.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2cgpvb/literacy
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Time
63 hours
Level
Introductory
Via
Pluralsight
Instructor
Biswanath Halder
Language
English
Good to know
Develops collaborative filtering skills, which are highly relevant in recommendation system development
Taught by Biswanath Halder, who is recognized for their work in recommendation systems
May require learners to have prior knowledge of recommendation systems or data science
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