Stanford University - Machine Learning
4.5 /5- Offered byCoursera
- Public/Government Institute
Machine Learning at Coursera Overview
Machine Learning
at Coursera
Start instantly and learn at your own schedule.
Duration | 56 hours |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Future job roles | Senior Data Analyst, Data Architect, Senior Business Analyst , Assistant Vice President - IT Knowledge Banking , E Commerce Analyst |
Machine Learning
Table of contents
Machine Learning at Coursera Course details
Machine Learning
at Coursera
Skills you will learn
What are the course deliverables?
- Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself
- Learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
More about this course
- This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:
- (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
- The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Machine Learning at Coursera Curriculum
Machine Learning
at Coursera
Introduction
Linear Regression with One Variable
Linear Algebra Review
Linear Regression with Multiple Variables
Octave/Matlab Tutorial
Logistic Regression
Regularization
Neural Networks: Representation
Neural Networks: Learning
Advice for Applying Machine Learning
Machine Learning System Design
Support Vector Machines
Unsupervised Learning
Dimensionality Reduction
Anomaly Detection
Recommender Systems
Large Scale Machine Learning
Application Example: Photo OCR
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Faculty: Andrew N G was facaulty there his teaching is very well known in machine learning community
Course is updated to latest research and right after learning we were able to check gained knowledge by assignment and mcg
Course Support: No
Reviewed on 28 Aug 2022Read More
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Learning Experience: This course is one of the best in this domain, It will cover all the aspects of machine learning in a easy way and we can also get hands on experience with theoretical knowledge.
Faculty: Andrew Ng is the faculty and his way of teaching is very good
Course Structure is good but it might not be updated with the current content or libraries
Course Support: Yes, by doing this course I get know many unknown aspects of machine learning and increase interest in this field
Reviewed on 12 Aug 2022Read More
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Machine Learning
at Coursera
Student Forum
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