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Courses Discover the Most Popular Open Online Course of ALL Time


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Stanford University’s online Machine Learning course on Coursera, stands out from the rest by the sheer volume of learners who have enrolled - 3,205,878 people as of today. To put this number into perspective, the course with the second highest number of enrolled students has 2,525,187, a difference of over 680,500 learners. The Stanford's Machine Learning course also boasts a 4.9-star weighted average rating based on thousands of learner reviews.

Stanford Machine Learning Course.png

Released in 2011 and taught by the famous Andrew Ng, co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University, this online machine learning course helped launch the MOOC movement by initially attracting over 100,000 students when first launched, and also led to the founding of Coursera.

Andrew Ng's course covers mainly the theory and concepts of machine learning and does not assume the student has any prior knowledge, making it an ideal starter course for beginners. The course is also structured in such a way that it remains relevant (even 9 years after its first publication) and does not involve any outside libraries which would have evolved over time.

In his review of online machine learning courses, Data Science Educator, David Venturi,commented, "[Stanford University’s Machine Learning on Coursera] covers all aspects of the machine learning workflow. Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms."

Structured over 11 weeks, Stanford University’s Machine Learning course on Coursera is made up of video and reading lectures, quizzes, and programming assignments with a recommended investment of between 5 and 7 hours per week to complete the course. However, students can choose their own pace if they wish and finish sooner. Students can enrol and access the course content for free, or can choose to purchase a course certificate for $79 USD.

About the Instructor
Andrew Ng is one of the world’s leading AI experts. He also works on machine learning, with an emphasis on deep learning and founded and led the “Google Brain” project, which developed massive-scale deep learning algorithms. Until recently, he led Baidu's ~1300 person AI Group, which developed technologies in deep learning, speech, computer vision, NLP, and other areas. See more courses by Andrew Ng here.

Week 1
  • Introduction: Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed.
  • Linear Regression with One Variable: Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.
  • Linear Algebra Review: This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.
Week 2
  • Linear Regression with Multiple Variables: What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.
  • Octave/Matlab Tutorial: This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.
Week 3
  • Logistic Regression: Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
  • Regularization: Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.
Week 4
  • Neural Networks: Representation: Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
Week 5
  • Neural Networks: Learning: In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.
Week 6
  • Advice for Applying Machine Learning: Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.
  • Machine Learning System Design: To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
Week 7
  • Support Vector Machines: Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.
Week 8
  • Unsupervised Learning: We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.
  • Dimensionality Reduction: In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.
Week 9
  • Anomaly Detection: Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
  • Recommender Systems: When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
Week 10
  • Large Scale Machine Learning: Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.
Week 11
  • Application Example: Photo OCR: Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.

We have picked out a selection of learner reviews below:

Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

Everything is taught from basics, which makes this course very accessible- still requires effort, however will leave you with real confidence and understanding of subjects covered. Great teacher too.

This is the best course I have ever taken. Andrew is a very good teacher and he makes even the most difficult things understandable.

Exceptionally complete and outstanding summary of main learning algorithms used currently and globally in software industry. Professor with great charisma as well as patient and clear in his teaching.

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

For more information, visit Machine Learning by Stanford University | Coursera