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.鈥� 鈥疘n 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.鈥� Finally, you will share your work with peers and have them evaluate it, facilitating a collaborative learning experience.鈥�
Elements Of A Successful Data Findings Report
Skills You'll Learn
Data Analysis, Scikit Learn (Machine Learning Library), Supervised Learning, Keras (Neural Network Library), Regression Analysis, Technical Communication, Machine Learning, Unsupervised Learning, Data Presentation, Artificial Neural Networks, Tensorflow, Exploratory Data Analysis, Applied Machine Learning, Statistical Analysis, Python Programming
Reviews
4.6 (137 ratings)
- 5 stars77.37%
- 4 stars16.78%
- 3 stars1.45%
- 2 stars0.72%
- 1 star3.64%
BS
Apr 17, 2025
Really great course which combines informational videos with hands on labs. The projects at the end are great fun and an awesome way to apply what you've learnt during the course.
A
Aug 29, 2024
good for getting overview of different machine learning ways
From the lesson
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.
Taught By
Yan Luo
Ph.D., Data Scientist and Developer
Artem Arutyunov
Data Scientist