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Yan Luo
and
Artem Arutyunov
This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate.
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This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate.
Read more
This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate.
In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix. Additionally, you will generate recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering.
Finally, you will share your work with peers and have them evaluate it, facilitating a collaborative learning experience.
Register for this course and see more details by visiting:
OpenCourser.com/course/i7apkf/machine
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What's inside
Syllabus
Machine Learning Capstone Overview
In this module, you will be introduced to the idea of recommender systems. All labs in subsequent modules are based on this concept. You will also be provided with an overview of the capstone project. You will perform exploratory data analysis to find preliminary insights such as data patterns. You will also use it to check assumptions with the help of summary statistics and graphical representations of online course-related data sets such as course titles, course genres, and course enrollments. Next, you will extract a word-count vector called a “bag of words” (BoW) from course titles and descriptions. The BoW feature is probably the simplest but most effective feature characterizing textual data. It is widely used in many textual machine learning tasks. Finally, you will apply the cosine similarity measurement to calculate the course similarity using the extracted BoW feature vectors.
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Syllabus
Machine Learning Capstone Overview
In this module, you will be introduced to the idea of recommender systems. All labs in subsequent modules are based on this concept. You will also be provided with an overview of the capstone project. You will perform exploratory data analysis to find preliminary insights such as data patterns. You will also use it to check assumptions with the help of summary statistics and graphical representations of online course-related data sets such as course titles, course genres, and course enrollments. Next, you will extract a word-count vector called a “bag of words” (BoW) from course titles and descriptions. The BoW feature is probably the simplest but most effective feature characterizing textual data. It is widely used in many textual machine learning tasks. Finally, you will apply the cosine similarity measurement to calculate the course similarity using the extracted BoW feature vectors.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Focuses on creating recommender systems, useful for retail, entertainment, media, and technology
Teaches popular machine learning libraries: Pandas, sci-kit-learn, Tensorflow/Keras, and more
Suitable for individuals with previous knowledge of machine learning who want to specialize in recommender systems
Offers hands-on labs and interactive materials, allowing for practical application of concepts
Utilizes real-world datasets for practical experience
Students are expected to complete previous courses in the IBM Machine Learning Professional Certificate before taking this course
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Reviews summary
Applied machine learning capstone with recommender systems
According to students, this Machine Learning Capstone course is a highly practical and consolidating experience, particularly for those who have completed the IBM Machine Learning Professional Certificate. Learners commend the hands-on labs and projects, especially the creation of a Streamlit application, which effectively solidifies their understanding and ability to deploy ML solutions. The course provides a comprehensive deep dive into building real-world recommender systems, covering various algorithms including KNN, NMF, and neural networks. While the challenging content and practical application are widely praised, some learners note concerns regarding the inconsistent peer review system and occasional lack of clear lab support, suggesting areas for potential improvement in course delivery.
The course covers complex topics and is highly demanding.
"The content on NMF and neural networks for recommendation was advanced but well-explained."
"Very challenging course, perhaps too challenging. I struggled with the coding assignments, even after completing the prerequisite courses."
"This course definitely requires a solid grasp of previous courses in the specialization."
Serves as a strong conclusion to the ML certificate.
"This capstone was an excellent culmination of the IBM ML certificate."
"This course is the perfect conclusion to the IBM ML certificate. The practical assignments and the final project really consolidate all the learning."
"I feel much more confident in my ability to build and deploy ML solutions now. Highly recommend if you've done the prerequisites."
Course excels in hands-on application of ML concepts.
"The hands-on labs were challenging yet incredibly rewarding, especially building the Streamlit app. It really solidified my understanding..."
"A fantastic deep dive into building real-world recommender systems. The labs using Pandas, scikit-learn, and TensorFlow were instrumental in applying the theory."
"Really enjoyed the practical application of ML concepts to recommender systems. The variety of algorithms covered, from KNN to NMF and neural networks, was comprehensive."
Some learners encountered difficulties with lab clarity.
"The course has good concepts, but the support for labs was sometimes lacking."
"I found myself spending a lot of time debugging environment issues rather than focusing on the ML concepts."
"Some lab instructions could be clearer, and sometimes the peer review system was slow..."
Learners face issues with the peer evaluation process.
"My main critique would be that some lab instructions could be clearer, and sometimes the peer review system was slow or led to inconsistent feedback."
"The peer grading system also felt a bit arbitrary at times, as some peers didn't provide constructive feedback."
"The peer review system also felt unfair when my work was graded by someone who clearly didn't understand the concepts. Wish there was more direct instructor feedback."
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 Machine Learning Capstone with these
activities:
Review basic Python programming concepts
Show steps
Ensures students have a strong foundation in Python programming, which is essential for completing the course assignments.
Browse courses on
Python Programming
Show steps
-
Review Python data types, variables, and operators
-
Practice writing simple Python functions
Find a mentor who has experience in machine learning or data science
Show steps
Provides guidance and support from experienced professionals in the field, which can accelerate learning and career growth.
Browse courses on
Mentorship
Show steps
-
Reach out to professionals in your network or online communities
-
Explain your goals and interests, and request their mentorship
Read 'Introduction to Information Retrieval'
Show steps
Provides a solid foundation in information retrieval concepts, which are essential for building effective recommender systems.
View
Introduction to Information Retrieval
on Amazon
Show steps
-
Read chapters 1-3
-
Summarize the main ideas of each chapter
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Complete the Coursera tutorial on natural language processing
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Provides a structured introduction to natural language processing techniques, which are used in the course for text analysis.
Browse courses on
Natural Language Processing
Show steps
-
Watch the video lectures
-
Complete the hands-on exercises
-
Join the discussion forums and ask questions
Python Coding Drills
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Solve coding challenges to improve your Python skills and prepare for this course
Browse courses on
Python
Show steps
-
Sign up for a coding challenge website
-
Start solving coding challenges
-
Continue solving challenges until you have completed at least 20
Solve practice problems on cosine similarity
Show steps
Reinforces understanding of cosine similarity, a key concept used in the course.
Browse courses on
Cosine Similarity
Show steps
-
Calculate the cosine similarity between different course titles
-
Use cosine similarity to find similar courses to a given course
Attend a study group and discuss course concepts
Show steps
Provides an opportunity to engage with peers, discuss course concepts, and reinforce learning through collaboration.
Show steps
-
Find a study group or create one with classmates
-
Prepare for the study group by reviewing course materials
-
Participate in discussions and ask questions
Review course materials with a peer
Show steps
Studying with others can lead to improved memory, better understanding of concepts, and higher test scores.
Show steps
-
Identify a peer in the class to collaborate with.
-
Set a time to meet and discuss course materials.
-
Work together to review key concepts, compare notes, and discuss any questions or areas of difficulty.
TensorFlow Tutorial
Show steps
Start developing neural networks and other deep learning models today!
Browse courses on
TensorFlow
Show steps
-
Create an account on TensorFlow.org
-
Install TensorFlow on your local system
-
Start the TensorFlow tutorial
-
Complete the tutorial
Design a user interface for the recommender system
Show steps
Develops skills in designing and prototyping user interfaces, which is essential for delivering a user-friendly recommender system.
Browse courses on
User Interface Design
Show steps
-
Sketch out a wireframe of the user interface
-
Create a prototype using HTML, CSS, and JavaScript
-
Test the prototype with users and gather feedback
Solve hands-on machine learning problems
Show steps
Solving problems will enhance your understanding of the concepts and techniques covered in the course.
Browse courses on
Machine Learning
Show steps
-
Find a set of machine learning problems to solve.
-
Use the concepts and techniques you have learned in the course to solve the problems.
-
Check your solutions against the provided solutions or discuss them with your peers.
Write a blog post summarizing a key concept
Show steps
Writing a blog post will force you to think deeply about the concept and explain it in a clear and concise way.
Show steps
-
Choose a key concept from the course.
-
Research the concept to gain a deep understanding.
-
Write a blog post that explains the concept in a clear and concise way.
-
Share your blog post with others and get feedback.
Build a course recommender system
Show steps
Provides hands-on experience in applying machine learning techniques to build a real-world recommender system.
Browse courses on
Machine Learning
Show steps
-
Gather and preprocess course data
-
Extract features from course titles and descriptions
-
Train a machine learning model to predict course ratings
-
Evaluate the performance of the model
Review basic Python programming concepts
Show steps
Ensures students have a strong foundation in Python programming, which is essential for completing the course assignments.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review Python data types, variables, and operators
- Practice writing simple Python functions
Find a mentor who has experience in machine learning or data science
Show steps
Provides guidance and support from experienced professionals in the field, which can accelerate learning and career growth.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Reach out to professionals in your network or online communities
- Explain your goals and interests, and request their mentorship
Read 'Introduction to Information Retrieval'
Show steps
Provides a solid foundation in information retrieval concepts, which are essential for building effective recommender systems.
View
Introduction to Information Retrieval
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3
- Summarize the main ideas of each chapter
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Complete the Coursera tutorial on natural language processing
Show steps
Provides a structured introduction to natural language processing techniques, which are used in the course for text analysis.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Watch the video lectures
- Complete the hands-on exercises
- Join the discussion forums and ask questions
Python Coding Drills
Show steps
Solve coding challenges to improve your Python skills and prepare for this course
Browse courses on
Python
Show steps
Show steps
Show steps
- Sign up for a coding challenge website
- Start solving coding challenges
- Continue solving challenges until you have completed at least 20
Solve practice problems on cosine similarity
Show steps
Reinforces understanding of cosine similarity, a key concept used in the course.
Browse courses on
Cosine Similarity
Show steps
Show steps
Show steps
- Calculate the cosine similarity between different course titles
- Use cosine similarity to find similar courses to a given course
Attend a study group and discuss course concepts
Show steps
Provides an opportunity to engage with peers, discuss course concepts, and reinforce learning through collaboration.
Show steps
Show steps
Show steps
- Find a study group or create one with classmates
- Prepare for the study group by reviewing course materials
- Participate in discussions and ask questions
Review course materials with a peer
Show steps
Studying with others can lead to improved memory, better understanding of concepts, and higher test scores.
Show steps
Show steps
Show steps
- Identify a peer in the class to collaborate with.
- Set a time to meet and discuss course materials.
- Work together to review key concepts, compare notes, and discuss any questions or areas of difficulty.
TensorFlow Tutorial
Show steps
Start developing neural networks and other deep learning models today!
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Create an account on TensorFlow.org
- Install TensorFlow on your local system
- Start the TensorFlow tutorial
- Complete the tutorial
Design a user interface for the recommender system
Show steps
Develops skills in designing and prototyping user interfaces, which is essential for delivering a user-friendly recommender system.
Browse courses on
User Interface Design
Show steps
Show steps
Show steps
- Sketch out a wireframe of the user interface
- Create a prototype using HTML, CSS, and JavaScript
- Test the prototype with users and gather feedback
Solve hands-on machine learning problems
Show steps
Solving problems will enhance your understanding of the concepts and techniques covered in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a set of machine learning problems to solve.
- Use the concepts and techniques you have learned in the course to solve the problems.
- Check your solutions against the provided solutions or discuss them with your peers.
Write a blog post summarizing a key concept
Show steps
Writing a blog post will force you to think deeply about the concept and explain it in a clear and concise way.
Show steps
Show steps
Show steps
- Choose a key concept from the course.
- Research the concept to gain a deep understanding.
- Write a blog post that explains the concept in a clear and concise way.
- Share your blog post with others and get feedback.
Build a course recommender system
Show steps
Provides hands-on experience in applying machine learning techniques to build a real-world recommender system.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather and preprocess course data
- Extract features from course titles and descriptions
- Train a machine learning model to predict course ratings
- Evaluate the performance of the model
Career center
Learners who complete Machine Learning Capstone will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist helps build machine learning models that can be used to provide actionable insights for business decisions. This course will provide the foundation for a career as a Data Scientist by teaching the fundamentals of machine learning, as well as how to apply those fundamentals to real-world datasets.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. This course will provide the skills and knowledge necessary to become a Machine Learning Engineer, including how to build and train machine learning models, as well as how to deploy those models into production.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. This course will provide the skills and knowledge necessary to become a Data Analyst, including how to collect and clean data, as well as how to analyze and interpret data using statistical and machine learning techniques.
Business Analyst
Business Analyst
A Business Analyst helps businesses understand their data and make informed decisions. This course will provide the skills and knowledge necessary to become a Business Analyst, including how to collect and analyze data, as well as how to communicate insights to stakeholders.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. This course will provide the skills and knowledge necessary to become a Quantitative Analyst, including how to build and train machine learning models, as well as how to apply those models to financial data.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to solve business problems. This course will provide the skills and knowledge necessary to become an Operations Research Analyst, including how to build and train machine learning models, as well as how to apply those models to business problems.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and tests software applications. This course will provide the skills and knowledge necessary to become a Software Engineer, including how to build and train machine learning models, as well as how to integrate those models into software applications.
Product Manager
Product Manager
A Product Manager defines and manages the development of software products. This course will provide the skills and knowledge necessary to become a Product Manager, including how to collect and analyze data, as well as how to communicate insights to stakeholders.
Project Manager
Project Manager
A Project Manager plans and executes projects. This course will provide the skills and knowledge necessary to become a Project Manager, including how to collect and analyze data, as well as how to communicate insights to stakeholders.
Consultant
Consultant
A Consultant provides advice and guidance to businesses. This course will provide the skills and knowledge necessary to become a Consultant, including how to collect and analyze data, as well as how to communicate insights to stakeholders.
Marketing Analyst
Marketing Analyst
A Marketing Analyst analyzes marketing data to help businesses make informed decisions. This course will provide the skills and knowledge necessary to become a Marketing Analyst, including how to collect and analyze data, as well as how to communicate insights to stakeholders.
Sales Analyst
Sales Analyst
A Sales Analyst analyzes sales data to help businesses make informed decisions. This course will provide the skills and knowledge necessary to become a Sales Analyst, including how to collect and analyze data, as well as how to communicate insights to stakeholders.
Customer Success Manager
Customer Success Manager
A Customer Success Manager helps businesses retain and grow their customer base. This course will provide the skills and knowledge necessary to become a Customer Success Manager, including how to collect and analyze data, as well as how to communicate insights to stakeholders.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation. This course will provide the skills and knowledge necessary to become a Technical Writer, including how to collect and analyze data, as well as how to communicate insights to stakeholders.
Librarian
Librarian
A Librarian manages and provides access to information. This course may be useful for a Librarian who wants to learn more about how to use data to improve library services.
For more career information including salaries, visit:
OpenCourser.com/course/i7apkf/machine
Reading list
We've selected 13 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
Machine Learning Capstone.
Save
Comprehensive guide to deep learning, covering the latest research and techniques. It valuable reference for those interested in learning more about deep learning.
Classic in the field of machine learning. It provides a comprehensive overview of the fundamental principles of machine learning.
Comprehensive guide to pattern recognition and machine learning. It valuable reference for those interested in learning more about these topics.
Practical guide to machine learning with Python, covering the most popular libraries and techniques. It valuable resource for those looking to apply machine learning to real-world problems.
Comprehensive guide to machine learning with R, covering the latest research and techniques. It valuable reference for those interested in learning more about machine learning.
Comprehensive guide to machine learning from a probabilistic perspective. It valuable reference for those interested in learning more about machine learning.
Comprehensive guide to machine learning, covering the latest research and techniques. It valuable reference for those interested in learning more about machine learning.
Comprehensive guide to machine learning algorithms. It valuable reference for those interested in learning more about machine learning.
Save
Practical guide to machine learning with Python, covering the latest research and techniques. It valuable resource for those looking to apply machine learning to real-world problems.
Comprehensive guide to data mining, covering the latest research and techniques. It valuable reference for those interested in learning more about data mining.
Provides a practical introduction to machine learning, with a focus on real-world applications. It covers a wide range of topics, including data preparation, model selection, and evaluation.
Save
Provides a practical guide to applying machine learning to business problems. It covers a wide range of topics, including data preparation, model selection, and evaluation.
Practical guide to machine learning for non-technical professionals. It valuable resource for those looking to apply machine learning to real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i7apkf/machine
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Effort
5 weeks of study, 4-6 hours/week
Level
Advanced
Via
Coursera
Institution
IBM
Instructors
Yan Luo
Artem Arutyunov
Language
English
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This course belongs to a
collection
called
IBM Machine Learning. It's comprised of two courses:
- Deep Learning and Reinforcement Learning
- Machine Learning Capstone (viewing)
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