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Ilkay Altintas
and
Julian McAuley
In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets.
Read more
In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets.
Read more
In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets.
This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/hdf30i/deploying
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What's inside
Syllabus
Introduction
Welcome to the first week of Deploying Machine Learning Models! We will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of recommender systems and differentiate it from other types of machine learning
Read more
Syllabus
Introduction
Welcome to the first week of Deploying Machine Learning Models! We will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of recommender systems and differentiate it from other types of machine learning
Read more
Implementing Recommender Systems
This week, we will learn how to implement a similarity-based recommender, returning predictions similar to an user's given item. We will cover how to optimize these models based on gradient descent and Jaccard similarity.
Deploying Recommender Systems
This week, we will learn about Python web server frameworks and the overall structure of interactive Python data applications. We will also cover some tips for best practices on deploying and monitoring your applications.
Project 4: Recommender System
For this final project, you will build a recommender system of your own. Find a dataset, clean it, and create a predictive system from the dataset. This will help prepare you for the upcoming capstone, where you will harness your skills from all courses of this specialization into one single project!
Capstone
Time to put all your hard work to the test! This capstone project consists of four components, each drawing from a separate course in this specialization. It's time to show off everything you've learned from this specialization.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches standard industry technology
Develops skills that are directly applicable in professional settings and projects
Provides practice in implementing and deploying your models in the real world
Builds on concepts and skills learned in the previous courses in the specialization
Course content is relevant to both academic and professional contexts
Suitable for learners of all levels, thanks to its real-world hands-on approach
Save this course
Save Deploying Machine Learning Models to your list so you can find it easily later:
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Reviews summary
Deployment of machine learning models
Learners say this course can be summarized as a good course with a focus on building recommender systems. The grading process is reliant on others, which can be a negative experience. Students notice a lack of participants which can make peer grading a slow process. Other complaints surround a cursory approach to the more complex topics in the course. It is unclear if this course delivers what the title suggests, as some state that what is offered is not reflected in the title.
Grading relies on peers
"The evaluation process is terrible, it depends on someone that can evaluate your project"
"Some of the questions in the quizzes were not so useful in my opinion"
Projects in this course can be too complex
"Recommender system is complex and need more in depth teaching"
"Topics are hastily rushed"
"You need to find a dataset to answer final capstone project which can be hard to find to fulfill the grading criteria"
"Deployment was not even taught. misleading title, underwhelming content."
Content is misleading
"Deployment was not even taught. misleading title, underwhelming content"
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 Deploying Machine Learning Models with these
activities:
Review Python basics and data structures
Show steps
Ensure a strong foundation by refreshing your knowledge of Python programming fundamentals and commonly used data structures.
Browse courses on
Python Basics
Show steps
-
Go through an online tutorial or review Python documentation to refresh your understanding of data types, operators, and control flow.
-
Practice working with lists, dictionaries, and sets to reinforce your understanding of data structures.
Review the Primer on Machine Learning by Alpaydin
Show steps
Understand the fundamental concepts of machine learning, such as supervised and unsupervised learning, feature selection, model evaluation, and overfitting.
View
Machine Learning, revised and updated edition...
on Amazon
Show steps
-
Read Chapters 1-3 to establish a strong foundation in the basics of machine learning.
-
Work through the practice problems at the end of each chapter to reinforce your understanding.
-
Consider creating a simple machine learning model using a programming language of your choice.
Follow the Scikit-learn tutorials
Show steps
Gain hands-on experience with a popular Python library for machine learning, covering topics such as data preprocessing, model training, and evaluation.
Browse courses on
scikit-learn
Show steps
-
Complete the Scikit-learn tutorial on data preprocessing.
-
Walk through the tutorial on model training and evaluation for a specific machine learning task.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve practice problems on LeetCode or Kaggle
Show steps
Develop your problem-solving and coding skills by working through practice problems related to recommender systems and data analysis.
Browse courses on
Coding Challenges
Show steps
-
Choose a problem set focused on recommender systems or data manipulation.
-
Attempt to solve the problems on your own, referring to documentation or online resources as needed.
-
Review your solutions and identify areas for improvement.
Write a blog post or article on best practices for recommender system evaluation
Show steps
Enhance your critical thinking and communication skills by writing a blog post or article that shares your insights on best practices for evaluating the effectiveness of recommender systems.
Show steps
-
Research and gather information on evaluation metrics and techniques.
-
Develop a clear and concise outline for your blog post or article.
-
Write and edit your content, ensuring clarity and flow.
Attend a workshop on recommender system deployment
Show steps
Gain insights from experts and engage in hands-on exercises to learn best practices for deploying and monitoring recommender systems in real-world environments.
Browse courses on
Machine Learning Deployment
Show steps
-
Research and identify a relevant workshop.
-
Register and attend the workshop.
-
Actively participate in the sessions and ask questions.
Review Python basics and data structures
Show steps
Ensure a strong foundation by refreshing your knowledge of Python programming fundamentals and commonly used data structures.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Go through an online tutorial or review Python documentation to refresh your understanding of data types, operators, and control flow.
- Practice working with lists, dictionaries, and sets to reinforce your understanding of data structures.
Review the Primer on Machine Learning by Alpaydin
Show steps
Understand the fundamental concepts of machine learning, such as supervised and unsupervised learning, feature selection, model evaluation, and overfitting.
View
Machine Learning, revised and updated edition...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to establish a strong foundation in the basics of machine learning.
- Work through the practice problems at the end of each chapter to reinforce your understanding.
- Consider creating a simple machine learning model using a programming language of your choice.
Follow the Scikit-learn tutorials
Show steps
Gain hands-on experience with a popular Python library for machine learning, covering topics such as data preprocessing, model training, and evaluation.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Complete the Scikit-learn tutorial on data preprocessing.
- Walk through the tutorial on model training and evaluation for a specific machine learning task.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve practice problems on LeetCode or Kaggle
Show steps
Develop your problem-solving and coding skills by working through practice problems related to recommender systems and data analysis.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Choose a problem set focused on recommender systems or data manipulation.
- Attempt to solve the problems on your own, referring to documentation or online resources as needed.
- Review your solutions and identify areas for improvement.
Write a blog post or article on best practices for recommender system evaluation
Show steps
Enhance your critical thinking and communication skills by writing a blog post or article that shares your insights on best practices for evaluating the effectiveness of recommender systems.
Show steps
Show steps
Show steps
- Research and gather information on evaluation metrics and techniques.
- Develop a clear and concise outline for your blog post or article.
- Write and edit your content, ensuring clarity and flow.
Attend a workshop on recommender system deployment
Show steps
Gain insights from experts and engage in hands-on exercises to learn best practices for deploying and monitoring recommender systems in real-world environments.
Browse courses on
Machine Learning Deployment
Show steps
Show steps
Show steps
- Research and identify a relevant workshop.
- Register and attend the workshop.
- Actively participate in the sessions and ask questions.
Career center
Learners who complete Deploying Machine Learning Models will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you will use your knowledge of machine learning models to help businesses make better decisions. This course will teach you how to deploy machine learning models, which is a critical skill for Data Scientists. By taking this course, you will be able to build and deploy machine learning models that can help businesses improve their operations and make more informed decisions.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will use your knowledge of machine learning models to design and develop machine learning systems. This course will teach you how to deploy machine learning models, which is a critical skill for Machine Learning Engineers. By taking this course, you will be able to build and deploy machine learning models that can solve real-world problems.
Software Engineer
Software Engineer
As a Software Engineer, you will use your knowledge of machine learning models to develop software applications. This course will teach you how to deploy machine learning models, which is a critical skill for Software Engineers. By taking this course, you will be able to build and deploy machine learning models that can improve the functionality of software applications.
Data Analyst
Data Analyst
As a Data Analyst, you will use your knowledge of machine learning models to analyze data and extract insights. This course will teach you how to deploy machine learning models, which is a critical skill for Data Analysts. By taking this course, you will be able to build and deploy machine learning models that can help businesses make better decisions.
Business Analyst
Business Analyst
As a Business Analyst, you will use your knowledge of machine learning models to help businesses improve their operations. This course will teach you how to deploy machine learning models, which is a critical skill for Business Analysts. By taking this course, you will be able to build and deploy machine learning models that can help businesses make better decisions.
Product Manager
Product Manager
As a Product Manager, you will use your knowledge of machine learning models to develop and manage products. This course will teach you how to deploy machine learning models, which is a critical skill for Product Managers. By taking this course, you will be able to build and deploy machine learning models that can improve the functionality of products.
Project Manager
Project Manager
As a Project Manager, you will use your knowledge of machine learning models to manage projects. This course will teach you how to deploy machine learning models, which is a critical skill for Project Managers. By taking this course, you will be able to build and deploy machine learning models that can help projects succeed.
Data Engineer
Data Engineer
As a Data Engineer, you will use your knowledge of machine learning models to build and manage data pipelines. This course will teach you how to deploy machine learning models, which is a critical skill for Data Engineers. By taking this course, you will be able to build and deploy machine learning models that can improve the efficiency of data pipelines.
Database Administrator
Database Administrator
As a Database Administrator, you will use your knowledge of machine learning models to manage databases. This course will teach you how to deploy machine learning models, which is a critical skill for Database Administrators. By taking this course, you will be able to build and deploy machine learning models that can improve the performance of databases.
System Administrator
System Administrator
As a System Administrator, you will use your knowledge of machine learning models to manage systems. This course will teach you how to deploy machine learning models, which is a critical skill for System Administrators. By taking this course, you will be able to build and deploy machine learning models that can improve the performance of systems.
Network Administrator
Network Administrator
As a Network Administrator, you will use your knowledge of machine learning models to manage networks. This course will teach you how to deploy machine learning models, which is a critical skill for Network Administrators. By taking this course, you will be able to build and deploy machine learning models that can improve the performance of networks.
Security Analyst
Security Analyst
As a Security Analyst, you will use your knowledge of machine learning models to analyze security data and identify threats. This course will teach you how to deploy machine learning models, which is a critical skill for Security Analysts. By taking this course, you will be able to build and deploy machine learning models that can help protect organizations from cyberattacks.
Risk Analyst
Risk Analyst
As a Risk Analyst, you will use your knowledge of machine learning models to identify and assess risks. This course will teach you how to deploy machine learning models, which is a critical skill for Risk Analysts. By taking this course, you will be able to build and deploy machine learning models that can help organizations make better decisions.
Compliance Analyst
Compliance Analyst
As a Compliance Analyst, you will use your knowledge of machine learning models to ensure that organizations comply with regulations. This course will teach you how to deploy machine learning models, which is a critical skill for Compliance Analysts. By taking this course, you will be able to build and deploy machine learning models that can help organizations meet regulatory requirements.
Auditor
Auditor
As an Auditor, you will use your knowledge of machine learning models to audit organizations' financial statements. This course will teach you how to deploy machine learning models, which is a critical skill for Auditors. By taking this course, you will be able to build and deploy machine learning models that can help auditors detect fraud and errors.
For more career information including salaries, visit:
OpenCourser.com/course/hdf30i/deploying
Reading list
We've selected 15 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
Deploying Machine Learning Models.
Provides a practical guide to machine learning using Python and the scikit-learn, Keras, and TensorFlow libraries. It covers a wide range of topics, from data preparation and feature engineering to model selection and evaluation.
Save
Provides a comprehensive overview of deep learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning in Python, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of data science, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning in Python, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of recommender systems. It covers the fundamentals of recommender systems, as well as the specific techniques used for building and deploying recommender systems.
Provides a practical overview of machine learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Save
Provides a comprehensive overview of deep learning for recommender systems. It covers the fundamentals of deep learning, as well as the specific techniques used for building and deploying deep learning recommender systems.
Provides a comprehensive overview of machine learning in Python, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning in R, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of deep learning in Python, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of natural language processing in Python, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of computer vision in Python, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of time series analysis in Python, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of reinforcement learning in Python, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hdf30i/deploying
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Via
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Institution
University of California San Diego
Instructors
Ilkay Altintas
Julian McAuley
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Python Data Products for Predictive Analytics. It's comprised of four courses:
- Design Thinking and Predictive Analytics for Data Products
- Meaningful Predictive Modeling
- Basic Data Processing and Visualization
- Deploying Machine Learning Models (viewing)
Good to know
Teaches standard industry technology
Develops skills that are directly applicable in professional settings and projects
Provides practice in implementing and deploying your models in the real world
Builds on concepts and skills learned in the previous courses in the specialization
Course content is relevant to both academic and professional contexts
Suitable for learners of all levels, thanks to its real-world hands-on approach
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