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Stanford Machine Learning by Andrew Ng review

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The course Stanford Machine Learning Coursera covers a wide array of topics in an astonishingly detailed manner. It covers topics such as Linear and Logistic Regression, Vectorisation, Regularisation, Neural Networks, and many more.

The topics of Feedforward and Back Propagation, are explained in a particularly comprehensive manner, making it easy to comprehend and grasp. Other topics are also covered in excruciating detail.

There is a specific concentration on the practical implementation of machine learning methods. You will be learning how to diagnose errors and to measure model performance.

You will be getting valuable advice on which algorithms to use and how to make tailor-made adjustments so that specific needs could be addressed.

$80.00 $0.00

Machine learning or AI has become the need of the hour. Everything from a simple web search to a self-driving car, the world is reaping the rewards of machine learning at every turn. This is why there is a great demand for people who are inept in machine learning.

If you are one of those people who are looking to sharpen their knowledge of machine learning, the courses offered by Stanford Machine Learning Coursera, are the ultimate foundational guide for you.

Covering all the nitty-gritty of the Machine learning, you will be surprised at the quality of this Stanford course and the value you will gain from it.

Machine Learning by Andrew Ng Syllabus

The detailed syllabus of this course is given in the below table. Stanford machine learning syllabus is divided week wise. Each week you will cover a new topic. There are certain numbers of video lectures in each topic in which you will learn theoretical as well as the practical implementation of Machine learning concepts.

Machine Learning by Andrew Ng

The approximate time is also given to complete each topic. But the candidate can speed up the process by giving more time. In addition to video lectures by Andrew Ng, you will get reading and quiz with each topic. The quiz will help you to analyze your learning of the topic.


The course is designed by one of the Coursera Co-founder Mr. Andrew Ng. The course content and delivery are of superb quality and worth its price. This course has been taken up by 130205 students and has got a rating of 4.9 out of 5. Machine learning by Andrew Ng is one of the oldest courses of Coursera which has been updated from time to time. The only course in this niche which is close to it is Udacity self-driving car engineer.

Course Pricing

It is hard to beat the price of Stanford Machine Learning Coursera because it is free. Although, if you want to obtain a certificate, to show that you have taken the course, then yes, that would cost you around 80 USD.

In case, you want to access the course content free of cost, you should enroll in the course in audit mode.  You can watch videos and readings in audit mode. But you will not get certain assignments and feedback. The certificate is also not available to candidates opting to audit mode.

Candidates who wish to obtain Machine learning by Andre Ng course certificate and are unable to afford the course fee can apply for Coursera financial aid. However, financial aid is given to eligible candidates only.

Stanford Machine Learning Certificate

Once you complete this course in paid mode, you are eligible to get a certificate issued by Stanford University. However, the certificate issued to you will be of the electronic version. It will be saved in your Coursera account accomplishment section. Candidates can download the certificate from there and can get a printed copy.

The certificate can be shared online and showcase your accomplishment in Linkedin profile.


8.9Expert Score
Free if you dont need certificate
Any beginner must understand the basics. That is why the course provided by Stanford Machine Learning Coursera, delivers in-depth topics such as data mining and statistical pattern recognition. Concepts include Supervised learning, Unsupervised learning and Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course also draws upon several practical applications like medical informatics, text understanding, computer vision, and other such areas. The course is designed to give you a perspective on learning and implementing algorithms in a way that is meaningful and obtains the desired results.
  • Focuses on the practical application of Machine Learning.
  • Covers a broad range of Machine Learning methods.
  • Covers the mathematical complexities of the topics.
  • A sizeable portion of the course covers Neural Network fundamentals.
  • The use of Octave programming language for projects makes it easy to tackle syntax/logic-based issues.’
  • Programming projects can be overly abstract at times.
  • Octave is not that prevalent of a programming language.

Specification: Stanford Machine Learning by Andrew Ng review

Completion Certificate


Content Type

On demand video, Other downloadable Content, Practical Projects

Course Duration

Self Paced

Course Language


Cousre Access

Till course completion





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