Principles of Machine Learning

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7/10 stars
based on  8 reviews
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Course Details

Cost

FREE,
Add a Verified Certificate for $99

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  • Upcoming

Course Provider

edX online courses
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Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

Provider Subject Specialization
Sciences & Technology
Business & Management
23710 reviews

Course Description

This course is part of the Microsoft Professional Program Certificate in Data Science.

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using R, Python, and Azure Machine Learning.

Reviews 7/10 stars
8 Reviews for Principles of Machine Learning

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10/10 starsTaking Now
  • 3 reviews
  • 0 completed
2 years, 8 months ago
I am graduated from University of Southern California. I had took Biostatistics and Data Analysis courses in my graduate program. In my opinion, the course is not bad as someone commented. In fact, it's a very good course but requires some prerequisites. I suggest anyone take Essentials of Data Science before take this one. After done that course, you will feel more comfortable to understand this course. This program almost equals one master program in my university. Take it, digest it, and apply it to real world. And then you will have big advantages in the job market!
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D H

2/10 starsCompleted
2 years, 7 months ago
This was probably the worst of the Microsoft courses for the Data Science certificate. I had a hard time understanding the concepts as it was delivered as dry as sand paper.
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Yilun Luo

8/10 starsCompleted
2 years, 7 months ago
Overall, the course is well organized and presented. I was afraid Microsoft might introduce a lot of product related topics. But it turned out most of the course focuses on machine learning methodology, and Azure Machine Learning is only used as a tool. You can easily adopt the skills obtained here to other platforms. On the down side, the theoretical and practical parts are a bit out of sync. The labs are too simple and focus too much on how rather than what, when, and why (questions that are important to solve real world problems). It would be nice to guide students think more about the business scenario, like we have this model, so what's the next step? When can we use it? What problems might we encounter if we give the model too much confidence? Maybe we'll see that in the Applied Machine Learning course.
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Fatima Aldama

8/10 starsDropped
2 years, 8 months ago
The course has given me the understanding of Machine Learning, how the computers predict. unfortunately I was not able to créate models by my own
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Anton MB profile image
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Anton MB

9/10 starsCompleted
2 years, 8 months ago
This really is a very good course. Mrs. Rudin is very good in explaining difficult concepts fast, clear and without making them too simple. The (not strictly necessary) explanation of how the weights of a 'neuron' in a neural network are determined is an example. The labs are made in such a way you can focus on data analysis rather then making the software work. The R and Python scripts of mr. Elston are very useful and nice examples of what and how you can work with these languages. Scripts are not the main focus of this course but these are very welcome extra's.
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Al Ramos

2/10 starsCompleted
2 years, 9 months ago
Poorly taught. Have seen many courses on this platform. The teaching on this one ranks very low in comparison.
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student

7/10 starsCompleted
2 years, 9 months ago
Thank you very much. It helped me to progress quickly on the subject, because I want to change the world, I will continue learning AI and ML. Regards.
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student

8/10 starsCompleted
2 years, 10 months ago
The course is well structured, and even though the taught content is challenging, it succeeds to give you a good understanding of the main principles behind Machine Learning. The instructors are very knowledgeable and the labs take you through relevant examples. You'll need to build enough knowledge to pass the final exam, though it is not that difficult as it may seem at first view.
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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.