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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
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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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Save Literacy Essentials: Core Concepts Recommender Systems to your list so you can find it easily later:
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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 Literacy Essentials: Core Concepts Recommender Systems with these
activities:
Review Fundamentals of Machine Learning Concepts
Show steps
Brush up on fundamental concepts and techniques in machine learning, such as supervised learning, unsupervised learning, and model evaluation.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Review lecture notes or online resources on fundamental machine learning concepts.
-
Solve practice problems to test your understanding of core algorithms.
Attend industry conferences or meetups on recommendation systems and machine learning
Show steps
Expand knowledge and build professional connections by engaging with industry experts and practitioners in the field of recommendation systems and machine learning.
Browse courses on
Recommendation Systems
Show steps
-
Identify relevant conferences or meetups
-
Register and prepare for the event
-
Attend sessions and presentations
-
Network and exchange ideas with attendees
-
Follow up with connections and explore potential collaborations
Review basic Python syntax
Show steps
Refreshing your understanding of basic Python syntax will greatly benefit you in this course.
Browse courses on
Python Basics
Show steps
-
Review official Python documentation on data types, variables, operators, and control flow.
-
Complete online tutorials or exercises on Python basics.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Write a blog post or technical article on a topic related to recommendation systems
Show steps
Reinforce understanding by clearly explaining a particular concept or aspect of recommendation systems in a blog post or article, showcasing proficiency and deepening knowledge.
Browse courses on
Recommendation Systems
Show steps
-
Identify a specific topic within the scope of recommendation systems
-
Research and gather relevant information
-
Outline and structure the content effectively
-
Write and edit the blog post or article
-
Publish and share the content
Solve exercises on data structures and algorithms in Python
Show steps
Working through exercises will greatly strengthen your understanding of data structures and algorithms.
Browse courses on
Data Structures
Show steps
-
Utilize online platforms like LeetCode or HackerRank for practice problems.
-
Complete coding challenges related to data structures and algorithms.
Explore Python libraries for recommendation systems
Show steps
Review and practice using Python libraries commonly used for building recommender systems to solidify understanding of their functionality and application.
Browse courses on
Python
Show steps
-
Identify and research relevant Python libraries
-
Install and configure the selected libraries
-
Create a sample dataset for testing
-
Implement basic recommendation algorithms using the libraries
-
Evaluate the performance of the algorithms
Solve practice problems on collaborative filtering
Show steps
Engage in targeted practice by solving problems related to collaborative filtering to strengthen understanding of the concepts and algorithms.
Browse courses on
Collaborative Filtering
Show steps
-
Review the concepts of collaborative filtering
-
Solve practice problems of varying difficulty levels
-
Analyze and interpret the results
-
Identify patterns and common pitfalls
-
Seek guidance and feedback from experts or peers
Solve Collaborative Filtering Algorithms
Show steps
Deepen your understanding of collaborative filtering techniques by solving practice problems and coding exercises.
Browse courses on
Collaborative Filtering
Show steps
-
Implement collaborative filtering algorithms, such as user-based or item-based collaborative filtering.
-
Evaluate the performance of your algorithms using metrics like precision and recall.
Create a simple recommendation system from scratch
Show steps
Building a simple recommendation system will provide hands-on experience.
Browse courses on
Recommendation Systems
Show steps
-
Follow tutorials on implementing collaborative filtering using Python.
-
Utilize libraries such as surprise or implicit for building the system.
Build a Recommender System for a Real-World Dataset
Show steps
Apply your knowledge by building a functional recommender system using real-world data, such as movie ratings or user-item interactions.
Show steps
-
Choose a dataset and define the problem statement.
-
Preprocess and explore the data.
-
Implement a recommendation algorithm and evaluate its performance.
-
Deploy and maintain the recommender system.
Build a simple recommendation engine for a specific domain
Show steps
Apply the concepts learned to a practical project by building a custom recommendation engine, enhancing understanding of the development process and challenges.
Browse courses on
Recommendation Systems
Show steps
-
Define the specific domain and use case
-
Gather and prepare the necessary data
-
Design and implement the recommendation algorithm
-
Evaluate and refine the performance of the engine
-
Present and document the project
Review Fundamentals of Machine Learning Concepts
Show steps
Brush up on fundamental concepts and techniques in machine learning, such as supervised learning, unsupervised learning, and model evaluation.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Review lecture notes or online resources on fundamental machine learning concepts.
- Solve practice problems to test your understanding of core algorithms.
Attend industry conferences or meetups on recommendation systems and machine learning
Show steps
Expand knowledge and build professional connections by engaging with industry experts and practitioners in the field of recommendation systems and machine learning.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Identify relevant conferences or meetups
- Register and prepare for the event
- Attend sessions and presentations
- Network and exchange ideas with attendees
- Follow up with connections and explore potential collaborations
Review basic Python syntax
Show steps
Refreshing your understanding of basic Python syntax will greatly benefit you in this course.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review official Python documentation on data types, variables, operators, and control flow.
- Complete online tutorials or exercises on Python basics.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Write a blog post or technical article on a topic related to recommendation systems
Show steps
Reinforce understanding by clearly explaining a particular concept or aspect of recommendation systems in a blog post or article, showcasing proficiency and deepening knowledge.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Identify a specific topic within the scope of recommendation systems
- Research and gather relevant information
- Outline and structure the content effectively
- Write and edit the blog post or article
- Publish and share the content
Solve exercises on data structures and algorithms in Python
Show steps
Working through exercises will greatly strengthen your understanding of data structures and algorithms.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Utilize online platforms like LeetCode or HackerRank for practice problems.
- Complete coding challenges related to data structures and algorithms.
Explore Python libraries for recommendation systems
Show steps
Review and practice using Python libraries commonly used for building recommender systems to solidify understanding of their functionality and application.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify and research relevant Python libraries
- Install and configure the selected libraries
- Create a sample dataset for testing
- Implement basic recommendation algorithms using the libraries
- Evaluate the performance of the algorithms
Solve practice problems on collaborative filtering
Show steps
Engage in targeted practice by solving problems related to collaborative filtering to strengthen understanding of the concepts and algorithms.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Review the concepts of collaborative filtering
- Solve practice problems of varying difficulty levels
- Analyze and interpret the results
- Identify patterns and common pitfalls
- Seek guidance and feedback from experts or peers
Solve Collaborative Filtering Algorithms
Show steps
Deepen your understanding of collaborative filtering techniques by solving practice problems and coding exercises.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Implement collaborative filtering algorithms, such as user-based or item-based collaborative filtering.
- Evaluate the performance of your algorithms using metrics like precision and recall.
Create a simple recommendation system from scratch
Show steps
Building a simple recommendation system will provide hands-on experience.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Follow tutorials on implementing collaborative filtering using Python.
- Utilize libraries such as surprise or implicit for building the system.
Build a Recommender System for a Real-World Dataset
Show steps
Apply your knowledge by building a functional recommender system using real-world data, such as movie ratings or user-item interactions.
Show steps
Show steps
Show steps
- Choose a dataset and define the problem statement.
- Preprocess and explore the data.
- Implement a recommendation algorithm and evaluate its performance.
- Deploy and maintain the recommender system.
Build a simple recommendation engine for a specific domain
Show steps
Apply the concepts learned to a practical project by building a custom recommendation engine, enhancing understanding of the development process and challenges.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Define the specific domain and use case
- Gather and prepare the necessary data
- Design and implement the recommendation algorithm
- Evaluate and refine the performance of the engine
- Present and document the project
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.
Save
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.
Save
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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