IBM

Machine Learning Capstone

This course is part of IBM Machine Learning Professional Certificate

Taught in English

Some content may not be translated

Yan Luo
Artem Arutyunov

Instructors: Yan Luo

11,251 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(65 reviews)

Advanced level

Recommended experience

20 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Compare and contrast different machine learning algorithms by creating recommender systems in Python

  • Predict course ratings by training a neural network and constructing regression and classification models 

  • Create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering

  • Develop a final presentation and evaluate your peers’ projects

Details to know

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Assessments

6 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(65 reviews)

Advanced level

Recommended experience

20 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your Machine Learning expertise

This course is part of the IBM Machine Learning Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate from IBM
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There are 5 modules in this course

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.

What's included

2 videos2 quizzes3 app items5 plugins

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.

What's included

1 video2 quizzes3 app items2 plugins

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.

What's included

1 video2 quizzes5 app items1 plugin

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.

What's included

2 videos4 plugins

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.

What's included

3 readings1 peer review4 plugins

Instructors

Instructor ratings
4.9 (17 ratings)
Yan Luo
IBM
7 Courses280,237 learners
Artem Arutyunov
IBM
1 Course11,251 learners

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IBM

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Reviewed on Mar 23, 2024

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