Course description
IBM Machine Learning Professional Certificate
This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries and you will apply what you have learned in various courses by completing a final capstone project.
Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics.
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Upcoming start dates
Suitability - Who should attend?
Some related experience required.
Outcome / Qualification etc.
What You Will Learn
- Compare and contrast different machine learning algorithms by creating recommender systems in Python
- Develop a final project using machine learning methods and evaluate your peers projects
- 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
Training Course Content
- Exploratory Data Analysis for Machine Learning
- Supervised Machine Learning: Regression
- Supervised Machine Learning: Classification
- Unsupervised Machine Learning
- Deep Learning and Reinforcement Learning
- Machine Learning Capstone
Course delivery details
This course is offered through IBM Skills Network, a partner institute of Coursera.
Suggested pace of 3 hours/week
Expenses
Please visit the Institute website for more information about tuition fees