Machine Learning Capstone from Coursera

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. 

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.

Unsupervised-Learning Based Recommender System

In this module, you will create three course recommendation systems using different methods. In lab 1, you will create a course recommendation system based on user profile and course genre matrices by computing an interest score for each course and recommend the courses with the highest interest scores. In the second lab, you will generate a course similarity matrix to create the recommendation system. In the third lab, you will implement a clustering-based recommender system algorithm using K-means clustering and principal component analysis based on group members’ course enrollment history. In labs four and five you will use collaborative filtering to make predictions about a user’s interest based on a collection of other users’ similar preferences. In lab 4, you will perform KNN-based collaborative filtering and in lab 5, you will use non-negative matrix factorization.

Supervised-Learning Based Recommender Systems

In this module, you will predict course ratings using neural networks. In the first lab, you will train neural networks to predict course ratings while simultaneously extracting users' and items' latent features. In lab 2, you will be given course interaction feature vectors as input data. Using regression analysis, you will calculate numerical rating scores that predict whether a student will audit or complete a course. Lab 3 is similar to lab 2 but instead of using regression you will use a classification model. You will extract user and item embedding feature vectors from a neural network. With those embedding feature vectors, you will create an interaction feature vector and use that to build a classification model. The model maps the interaction feature vector to a rating mode that predicts whether a learner will audit or complete a course.

Share and Present Your Recommender Systems

In this module, you will review guidelines and best practices for creating successful reports. As well you may wish to review instructions on creating PowerPoint presentations and how to save a PowerPoint as a PDF.

Final Submission

In this final module, you will be introduced to Streamlit and have the opportunity to build a Streamlit app to showcase your work in previous modules. You will complete your submission of screenshots from the hands-on labs for your peers to review. Once you have completed your submission you will then review the submission of one of your peers and grade their submission.

Good to know

Know what's good

, what to watch for

, 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

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

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Ensures students have a strong foundation in Python programming, which is essential for completing the course assignments.

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Review Python data types, variables, and operators
Practice writing simple Python functions

Find a mentor who has experience in machine learning or data science

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Provides guidance and support from experienced professionals in the field, which can accelerate learning and career growth.

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Reach out to professionals in your network or online communities
Explain your goals and interests, and request their mentorship

Read 'Introduction to Information Retrieval'

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Provides a solid foundation in information retrieval concepts, which are essential for building effective recommender systems.

View Introduction to Information Retrieval on Amazon

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Read chapters 1-3
Summarize the main ideas of each chapter

Ten other activities

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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.

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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

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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

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Reinforces understanding of cosine similarity, a key concept used in the course.

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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

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Provides an opportunity to engage with peers, discuss course concepts, and reinforce learning through collaboration.

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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

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Studying with others can lead to improved memory, better understanding of concepts, and higher test scores.

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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

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Start developing neural networks and other deep learning models today!

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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

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Develops skills in designing and prototyping user interfaces, which is essential for delivering a user-friendly recommender system.

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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

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Solving problems will enhance your understanding of the concepts and techniques covered in the course.

Browse courses on Machine Learning

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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

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Writing a blog post will force you to think deeply about the concept and explain it in a clear and concise way.

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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

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Provides hands-on experience in applying machine learning techniques to build a real-world recommender system.

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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

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Operations Research 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.

See salaries and explore the career path for Quantitative Analyst

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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Marketing Analyst

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.

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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.

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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.

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