Machine Learning Foundations: A Case Study
|University:||University of Washington|
|Certificate:||Yes, if purchased. $79.|
So many businesses and organizations have an abundance of data but no proper system to interpret that data to their own advantage. The course Machine Learning Foundations: A Case Study Approach investigates the different ways in which machine learning can turn mere data into valuable information. The course is based upon a series of practical case studies.
This course is also the first in the Machine Learning Specialization series of courses. If you take on the specialization at a later stage, you will receive credit for completing this course.
Machine Learning Foundations: A Case Study Approach was designed by the University of Washington. It is presented by Carlos Guestrin and Emily Fox, both Amazon Professors of Machine Learning. The fact that they have been rated and average of 4.6 out of a possible 5 by 4,627 previous students is a very clear indication of their teaching abilities and their dedication to their students.
The course is based upon case studies, which means that you will be very much hands on all the time. You will be completing practical assignments and submit them for grading every week during the course.
- You determine the pace at which you learn and master the objectives;
- You will not be penalized if you miss a deadline. Once you have enrolled for the course you will have a full 180 days’ access to all the course materials;
- You are supplied with all the resources that you need. You will have access to more than a hundred high definition video lectures and 22 readings;
- You will be submitting two practical assignments each week. They are graded, providing you with valuable feedback;
- You have access to a comprehensive support network;
- You will receive a widely acknowledged and accredited certificate;
The Machine Learning Foundations: A Case Study Approach is presented over a period of six weeks. You will need to dedicate approximately five to eight hours each week. In order to successfully complete the course you need to pass all the graded assignments. The topics covered during the course are as follows:
- Overview of the power of machine learning.
- The application of regression.
- Analyzing the performance of predictive models.
- Using iPython to implement regression.
- Creating models that predict a class.
- Analyzing the accuracy of classifiers.
- Implementing classifiers.
- Retrieving documents.
- Creating intelligent document retrieval systems.
- Collaborative filtering.
- Matrix factorization.
- Constructing deep features.
- Searching for images.
- Creating image classifiers.
- Building end-to-end applications using machine learning as the core.
- Utilizing data sets to analyze new data.
You will need no prior qualifications or experience in order to complete this course successfully.
Machine Learning Foundations, University of Washington – Conclusion
For those that dream of embarking on a career in machine learning this course is an absolute must. Once you completed this course, you will be ready for more advanced training and you can even decide to pursue the other courses in the machine learning specialization series.
The overwhelmingly positive feedback from previous students should reassure you that you will receive high quality instruction from true professionals.
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