Studying Machine Learning Virtually At MIT or Stanford? SoloLearn Is Your Perfect Companion21 July 2020
Machine learning is a hot topic for programmers and big tech companies right now. And for good reason -- machine learning is powering everything from self-driving cars to advanced data-driven fintech startups to smart utility grids.
So it’s no surprise that universities known for their cutting-edge technology education are jumping into machine learning by offering online courses and certificates. In this article, we’ll examine the machine learning courses offered by MIT and Stanford. Plus, we’ll give you some tips on how to get the most out of these courses by using free study resources from SoloLearn.
Why Should I Learn About Machine Learning?
Machine learning is a way for computers to learn new things -- to make decisions without needing to be specifically programmed for each and every situation. Traditionally, a computer only does what you program it to do, using only the information you give it. But as computers have become faster and more powerful, they’re now able to make smarter decisions, and even learn new things, by applying statistical models and algorithms to the data they collect.
This concept of machine learning has many real-world applications. For example, facial recognition and speech recognition are two areas that require “learning”. How can you program a computer to recognize a specific sound as a word, even if it’s spoken at a different tone or with an accent? Or how can you get it to tell if one face is the same as another, even if the lighting or angle is different? That’s where machine learning comes in. By developing and training algorithms that can recognize even “fuzzy” patterns like speech or objects in a photograph, a computer program can be developed to do things that up until now only the human brain was capable of.
There are hundreds of other applications for machine learning, including self-driving cars, big data analysis, manufacturing process automation -- and even things that haven’t even been invented yet. So studying machine learning now can give you an advantage in the current and future job marketplace as employers look for those who are familiar with this important field.
Where Can I Study Machine Learning?
Because of the huge potential for machine learning to revolutionize technology, many companies are interested in developing skills in this emerging field. In response to this demand, major universities are now offering online classes that will help you get up to speed.
Three of the most popular machine learning programs are being offered by MIT and Stanford. What can you expect to learn from these courses? And how is SoloLearn’s machine learning course the perfect companion for these courses?
MIT Professional Education’s Machine Learning: From Data to Decisions Course
MIT is known for its research programs at the cutting edge of modern technology. So it’s no surprise that they’re offering a comprehensive machine learning course as part of their Professional Education program. This course provides a high-level overview of machine learning, the principles behind it, and how it can be integrated into business processes and new technologies.
The MIT Professional Education machine learning course is a paid program, meaning there is a fee to access the full course materials. But with the fee comes instructor-led lecture sessions, graded homework assignments, and a certificate of completion that can also be used for certain continuing education credits.
What is Covered in the MIT Machine Learning Course?
The MIT machine learning course is broken into eight modules. After an introduction and overview module, Module 2 covers understanding data -- how to make sure you have the right data and that you’re asking the right questions to get the information you’re after. The next three modules cover how predictions can be made from a dataset. This introduces three core prediction concepts: regression, classification, and neural networks.
Next, two modules focus on decision making in the face of uncertainty. Then the final module covers causal inference, or the relationship between cause and effect as it applies to machine learning.
As you work through modules 3-5 of the MIT course, be sure to check out the corresponding lessons in the SoloLearn machine learning course. Modules 2 and 6 will cover how to apply linear regression models for data classification, as well as an introduction to neural networks. Use SoloLearn to check your understanding and review the key concepts in these modules from anywhere by using our mobile app.
Who is the MIT Machine Learning Course Designed For?
MIT’s course is designed to give a broad overview of the concepts involved in machine learning. It is not designed to be a coding course, but rather an introduction to the principles of machine learning and how they can be used in business applications.
So the course counts CEOs, CIOs, directors, VPs, and data architects among its alumni. So it’s really designed for those who want to make use of machine learning without getting bogged down in the details. It helps managers and executives to use the resources they have more effectively for business and product development.
MIT OpenCourseWare’s 6.867 Machine Learning Course
Besides professional development, MIT is known as an early supporter of MOOCs -- Massively Online Open Courses. They’ve uploaded the course materials for thousands of undergraduate and graduate-level courses, freely available through their OpenCourseWare system.
If you’re looking for something a little more in-depth than the From Data to Decisions course, then MIT’s open 6.867 Machine Learning course may be perfect for you. This graduate-level course is designed to give a more in-depth understanding of machine learning for those who are already familiar with computer science concepts.
The difference between this and the MIT Professional Education course is that as an open course, there are no instructor-led sessions or graded assignments. Also, completing the course does not earn you a certificate or continuing education credit. However, unlike the PE course, all of the materials are completely free and available for you to work through at your own pace.
What is Covered in MIT 6.867 Machine Learning?
MIT 6.867 spans 24 lectures, 5 problem sets, a midterm, and a final exam. Similar to the PE course, it starts out with introductory machine learning topics like classification and linear regression. But it quickly takes a turn into more advanced territory, diving into such concepts as kernel regression, boosting, spectral clustering, Markov models, and Bayesian networks.
As you get into the first few lectures of MIT 6.867, the free SoloLearn machine learning course can help you understand the topics and check your understanding with fun quizzes and try-it-yourself coding exercises. Module 2 on classification can help you get started with lectures 1-3, while lectures 9 and 10 on model selection and evaluation go hand-in-hand with Module 3.
Who is MIT 6.867 Designed For?
This is a graduate-level course, designed for those who already have a computer science background, as well as statistics and college-level mathematics. Since there are no set time limits, classroom sessions, graded homework or other constraints, it’s perfect to learn at one’s own pace. But, this can make it difficult to dedicate the time to work through the course, and you won’t be able to check your understanding of the material by means of regular tests and quizzes.
Stanford SEE’s CS229 Machine Learning Course
Similar to the MIT course, Stanford offers CS229 Machine Learning as a free, open course through their “SEE” (Stanford Engineering Everywhere) program. Like the MIT open course, there are no graded assignments or direct instructor-led sessions -- everything is self-paced and self-guided.
Unlike MIT 6.867, CS229 offers video recordings of each of the lecture sessions. The course includes note handouts for many of the lectures, as well as four problem sets along with solutions so that you can apply what you’re learning immediately.
What is Covered in Stanford CS229 Machine Learning?
CS229 breaks its machine learning curriculum into four broad categories: supervised learning, unsupervised learning, machine learning theory, and modern application of machine learning. It covers these topics over the course of 20 lectures. CS229 gets into the details of implementing machine learning algorithms in real life in several of its lectures, which beginning students of machine learning will find helpful and practical.
SoloLearn’s free machine learning course focuses on supervised learning, where the target or answer is known based on past data. Likewise, the Stanford course discusses supervised learning concepts for the first 11 lectures. For example, lecture 3 discusses logistic regression in detail. Before or after that course, you can use Module 2 of the SoloLearn course to make sure you understand the core concepts presented in the lecture. And as you get to lecture 6, which discusses neural networks, be sure to study the examples found in Module 6 to make sure you have a good foundation before moving on.
Who is Stanford CS229 Machine Learning Designed For?
Like MIT 6.867, this is a course targeted to those who are already familiar with computer science principles. Prerequisites listed on the course page include knowledge of computer science principles “at a level sufficient to write a reasonably non-trivial computer program”, as well as a familiarity with basic probability theory and linear algebra. For those who need a refresher on the mathematical topics required for this course, the handouts include a linear algebra and probability review.
You can also find a basic statistics review in the first module of the SoloLearn machine learning course. Concepts like mean, median, percentile, and standard deviation are very important in machine learning. The exercises in the SoloLearn course will help you to understand these points and how they apply to machine learning.
How Can I Get The Most From an Online Machine Learning Course?
If you don’t already have a background in computer science or programming, jumping in to one of these machine learning courses can be a bit daunting. If you’d like to brush up on your programming skills and get a solid introduction to computer science, try the free SoloLearn Python course. In that course, you’ll start from the basics of programming, and then work your way up to functional and object-oriented programming concepts.
What’s more, you’ll get a good foundation in the programming language used by machine learning professionals worldwide. Python has become popular for machine learning applications because of its ease-of-use combined with an extensive collection of libraries designed for data analysis -- a very important part of machine learning.
Once you’ve brushed up on Python and computer science, you’ll be ready to jump into a machine learning course. However, you may feel overwhelmed if you dive right into one of the MIT or Stanford open courses. As mentioned, these courses are taught at an advanced level, and they dive right into some very deep topics right away. Also, as with most open online courses, there are no quizzes or tests to check your knowledge as you progress through the course.
So before diving into one of these advanced courses, you can get a solid introduction to machine learning from SoloLearn’s free online course. With quick, bite-size lessons and quizzes, you’ll get up to speed on the basics of machine learning quickly.
Using Python, you’ll learn how to use machine learning to solve classification problems -- where you want to determine what class or category something belongs to. You’ll cover concepts like logistic regression, decision trees, random forests, and neural networks.
After mastering these topics, you’ll be ready to move on to a more advanced machine learning course from MIT or Stanford. But anytime you need a review of one of the concepts covered in the course, the SoloLearn app will be right at your fingertips with a clear and simple explanation, examples, and community support from our forums.
Machine learning is here to stay. With so many applications across nearly every industry and market, those who have the skills and knowledge to develop machine learning systems will be in high demand. Jumpstart your career by diving into a machine learning course today!