Machine Learning with Python
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Course
Machine Learning with Python
Explore the fundamentals of machine learning using Python. Discover how to uncover hidden insights, predict future trends, and create prototypes.
Develop these core skills and take a critical step forward in your data science career.
Self-Paced
Mentored
Beginner
Duration
1 hour/week
This Machine Learning with Python course dives into the basics of machine learning using an approachable and well-known programming language. You'll learn about supervised vs unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each. Plus, you will look at real-life examples of machine learning and how it impacts and helps businesses and society.
In this course, you will explore many algorithms and models, including classification, regression, clustering, and dimensional reduction. You will also build your understanding of popular models such as train/test split, root mean wquared error, and random forests.
Learning to analyze data with Python is a key skill for anyone who wants to excel in the field of data science. This course will provide you with an excellent foundation in using Python for machine learning and set you up for the next step in your data science career.
This IBM certified course comprises five purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to 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 1 hour per week, you will complete the course in 3 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 videos, reading material, and online exams questions.
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 will vary across packages.
Once you have successfully completed the course, you will earn your Certificate of Completion.
After this course you will understand:
- Statistical modeling.
- Real-life examples of machine learning.
- Python libraries.
- Popular algorithms: regression, classification, and clustering.
- Recommender systems: content-based and collaborative filtering.
- Popular models: train/test split, gradient descent, and mean squared error.
- 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.
Ideally you should have completed the following courses:
- Python for Data Science
- Data Science Hands-on with Open Source Tools
- Data Analysis with Python
If you have not taken these courses, you need to have experience of using Python for data science and data analysis, plus some experience using open source tools.