Machine Learning with Python: A Practical Introduction

This Machine Learning with Python: A Practical Introduction course comprises six purposely designed modules that take you on a carefully defined learning path. If you are thinking about taking the course separately, it is worth noting that it is part of the IBM Data Science Professional Certificate Program, and you may want to consider enrolling for the whole program rather than just enrolling for one course at a time.

This is a self-paced course, which means it does not run on a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 4-6 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes, and final assignment.

Once you have successfully completed this Machine Learning with Python: A Practical Introduction course, you will earn your IBM Certificate.

As part of our mentoring service, you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.

• Be familiar with machine learning terms, libraries, and the languages used to create them.
• Be able to apply the appropriate form of regression to a data set for estimation.
• Be able to apply an appropriate classification method for a particular machine learning challenge.
• Be able to use the correct clustering algorithms on different data sets.
• Be able to explain how recommendation systems work, and implement one on a data set.
• Have demonstrated your understanding of machine learning in an assessed project.

• Individuals looking to learn how to work with different kinds of data.
• Individuals wanting to perform analysis on data.
• Individuals wanting an introduction to Machine Learning with Python.

There are no prerequisites for this course.