Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective when manual programming is not. Machine learning (also known as data mining, pattern recognition and predictive analytics) is used widely in business, industry, science and government, and there is a great shortage of experts in it. If you pick up a machine learning textbook you may find it forbiddingly mathematical, but in this class you will learn that the key ideas and algorithms are in fact quite intuitive. And powerful! Most of the class will be devoted to supervised learning (in other words, learning in which a teacher provides the learner with the correct answers at training time). This is the most mature and widely used type of machine learning. We will cover the main supervised learning techniques, including decision trees, rules, instances, Bayesian techniques, neural networks, model ensembles, and support vector machines. We will also touch on learning theory with an emphasis on its practical uses. Finally, we will cover the two main classes of unsupervised learning methods: clustering and dimensionality reduction. Throughout the class there will be an emphasis not just on individual algorithms but on ideas that cut across them and tips for making them work. In the class projects you will build your own implementations of machine learning algorithms and apply them to problems like spam filtering, clickstream mining, recommender systems, and computational biology. This will get you as close to becoming a machine learning expert as you can in ten weeks!
Week One: Basic concepts in machine learning.Week Two: Decision tree induction.Week Three: Learning sets of rules and logic programs.Week Four: Instance-based learning.Week Five: Statistical learning.Week Six: Neural networks.Week Seven: Model ensembles.Week Eight: Learning theory.Week Nine: Support vector machines.Week Ten: Clustering and dimensionality reduction.
completed this course, spending 4 hours a week on it and found the course difficulty to be very easy.
This course is a good first step of the specialization in machine learning. It provides a very loose overview of what is going on. Each week one lecturer explains the "idea" behind a machine learning algorithm, then the other one implements parts of it. For the quiz you're required to use the output...
This course is a good first step of the specialization in machine learning. It provides a very loose overview of what is going on. Each week one lecturer explains the "idea" behind a machine learning algorithm, then the other one implements parts of it. For the quiz you're required to use the output to find specific data results, or add some minor feature changes. The lecturers in general do a good job and the lectures are well structured, with the welcome occasional piece of humor.
I personally found the course to be completely overshadowed by Andrew Ng's Machine Learning Course. This course makes use of a highly specialized tool: most of the time the actual "machine learning" part is done by some already built-in algorithm of the software, and almost all the work we do is in data handling. While I'm sure this is great for some people, I would hesitate to describe this as performing Machine Learning ourselves. Similarly, while the tool is excellent and I'm sure people use it in the industry, I went into the course hoping to learn more about the fundamentals of ~how~ machine learning works, which wasn't covered too well. It's quite likely the later courses cover this as this is the first in a specialization, but for anyone other than a beginner you might experience these same concerns.
The quizzes being multiple choice is also a little aggravating. By comparison, many other programming courses have code submitting and marking tools, and it makes this course feel somewhat unprofessional at times.