Machine learning is the term for the field of computer science that attempts to allow computers to “learn” by using statistically analyzed data and without programming input from humans, other than providing that data.
It emerged out of the study of patterns and pattern recognition, as well as computational learning theory in artificial intelligence, and is sometimes referred to as “predictive analytics” when used in a commercial setting. Machine learning applies to fields such as data analysis, business management and science. The ability to mine historical data to advance machine learning is vital for roles like BI Analytics Developer, Data Scientist and Analytics Modeler, and careers in these fields earn up to $130k per year. However, obtaining the skills to have a successful career working with machine learning may be difficult, and that is why we’ve done the research for you to kickstart your education.
How We Choose Our Ratings
We understand the importance of providing honest and accurate information when it comes to suggesting courses for the next step in your education. For this reason, we have reviewed testimonials from students who have graduated from these courses, as well as reviews from students who are currently enrolled. By taking the opinions of these students into account along with many other factors, we strive to offer you the best choices for the top online courses in machine learning, so if you’re ready to begin your journey in this field, keep reading below for what we believe to be the best courses out there.
We will begin our list with Microsoft’s own course, Implementing Predictive Analytics with Spark in Azure HDInsight. The course's instructor is Graeme Malcolm, the Senior Content Developer for Microsoft Learning Experiences, and through the duration of the course, he will guide you through predictive analytics implementation for big data by using Apache Spark. For those who have experience in programming, the course also lets you work in Python or Scala to build machine learning models with Spark ML, Spark’s library for machine learning. It should be mentioned, however, that the course does require a subscription to Microsoft’s cloud computing service, Azure, in order to make the most of it. Although the course is technically free to take, a certificate of completion will cost $99. The extra charge may be worth it for a certificate that can easily be added to a resume, CV, or LinkedIn profile, though.
This six-week course has a load of around three to four hours of class time per week, and during the length of the class, students will learn to ready themselves for data exploration and machine learning using Spark. The course begins with an introduction to data science and Spark, where students use Spark to run Scala or Python code and work with data. Then, the student will evaluate models and optimize them, as well as build machine learning models that are both supervised and unsupervised.
Familiarity with Azure HDInsight, SQL, computer programming and databases prior to enrolling in this course is advisable, as well as having the willingness to take a self-paced learning approach to machine learning.
The next course on our list comes from Statistics.com, taught by Dr. Galit Shmueli and Anthony Babinec. The course’s purpose is to introduce students to the basic concepts of predictive modeling and machine learning. The student will learn by analyzing the relationships between different variables, as well as visualizing historical data. During the course, students will learn about four different techniques that are vital for machine learning: k-nearest neighbors, classification and regression trees, and Bayesian classifiers. Once you have achieved comfort with these models, you will then begin to combine models with others in order to attain more accurate results than any model could arrive at on its own. Machine Learning Tools also will cover dividing data into validation data, test data, and training data with the use of partitioning.
There are three different software options for this class: R, Python, and XLMiner, a data-mining add-on for Microsoft Excel. These three options open the course to students who have prior knowledge of these softwares and give an opportunity for quicker learning.
By the time the course is over, you will have the ability to obtain a clearer understanding of the relationships within different data sets by visualizing the data, providing an assessment basis for predictive modeling, understanding how models can improve predictions, and implementing models using four different algorithms. The program lasts for four weeks, offers certificate and individual programs, and includes a free one week preview before you start.
Practical Predictive Analytics: Models and Methods is a course that comes from the University of Washington through Coursera. This course, taught by Bill Howe, the director of research at the University of Washington, focuses on designing statistical experiments in order to analyze their results using modern methods, and you will observe the downfalls of interpreting mathematical arguments associated with large datasets. Each week in this course requires approximately six to eight hours of work, and by the end, you will be able to make statistical arguments by using resampling methods, and you'll have a deep understanding of structural query, traversals, recursive queries, PageRank, and community detection. You will also be able to explain learning concepts and methods and apply practical machine learning methods and theories to problems in the real world.
The course boasts nearly 70 videos to guide you along the four weeks, and the syllabus divides into different learning sections for each week. You will learn the basics of statistical inference in Week 1 and will make simple programs to make mathematical arguments. Publication bias is also covered in the first week.
Week 2 covers supervised learning, and the students will begin to explore important algorithms, techniques and methods involved in machine learning. This week’s lesson also takes students through the steps involved in avoiding pitfalls while structuring algorithms.
In Week 3, optimization is front and center, and the student may notice that there are far fewer videos to view than the previous two weeks. By this time, you should have a certain degree of comfort with the methods and ideas learned in past weeks, and you will use these ideas to optimize a cost function using gradient descent. This includes performance with randomization and parallelization. You will also begin to familiarize yourself with other popular methods and understanding the differences between them.
The last week of the course focuses on unsupervised learning. This week guides students through unsupervised learning methods and gives students an opportunity to practice with a pragmatic approach. Again, the video count for this week significantly drops down to only 4 for the entire week. You will be using your newfound knowledge to finish the last week of the course.
For those who may be skeptical about purchasing online programs, you can audit the videos of the Practical Predictive Analytics: Models and Methods course, and some of the course content is available for free. However, paying the full amount for the course will give you access to all of the material, as well as a certificate of completion that you can display on your LinkedIn page.
Not to be confused with Predictive Analytics 1 - Machine Learning Tools, Predictive Analytics is a seven-week course from the Indian Institute of Management, Bangalore, and you can expect around five hours of work each week. The instructor is Dinesh Kumar, a professor in Decision Sciences & Information Systems at IIMB, and it presents predictive modeling and machine learning as a business strategy used by high-performing companies. Students will use models such as logistic regression, linear regression, decision trees, and ARIMA to solve predictive analytics problems. This course is ideal for those who would like to build a foundation upon which the rest of their machine learning knowledge can be developed and is a fantastic place to begin a data analytics career.
Prerequisites for the course include a firm grasp of advanced statistical concepts like ANOVA, hypothesis testing, probability distribution and descriptive statistics. Working knowledge of softwares like SPSS, SAS, or STATA is a good idea prior to enrolling. At the end of the course, you will have a greater understanding of how the tools of predictive analytics can be used in order to analyze problems that modern businesses face, and you will use predictive analytics techniques to learn regression, logistic regression and forecasting, as well as explore model outputs by using MS Excel, SPSS, and SAS.
While this machine learning course is free, you will be required to pay a $50 fee for a verified LinkedIn certificate. The course is available in English as well as Hindi.
The last of our list of best online courses for machine learning is a class called Learning from Data (Introductory Machine Learning) and comes from the California Institute of Technology. This behemoth ten-week introductory course contains more than double the content of all of the other classes on our list with 10-20 hours of work per week. Yaser S. Abu-Mostafa, a professor of Electrical Engineering and Computer Science at the California Institute of Technology, leads beginner computer scientists through basic theory, algorithms and applications. There is a growing demand for careers in machine learning, and this course from Caltech can help you to prepare for your entry into one of these roles.
In this course, there are hands-on sections, as well as theory sections that aim to teach the student with a story that begins with the simple concept of learning and ends with the ability of students to fully understand and explain machine learning.
By the end of the class, you will be able to identify basic theoretical principles, applications of machine learning and algorithms. You will also analyze relationships between theory and practice of machine learning and take on the mathematical and heuristic aspects of machine learning.
The course, similar to most other classes that made our list, is available for free with an added option to purchase a verified certificate of completion.
Although most courses that we included on our list are offered for free or at the very least have content that is accessible with no purchase required, it is possible that you may wish to continue researching online machine learning courses. The courses that made our list are what we consider to be the best online courses available, but only you can weigh options in order to make an informed decision about the classes that are best for you. If you would like to continue looking further into online machine learning courses, here are a few tips:
- Check the syllabus. This is one of the most important things to consider when purchasing an online course. It is vital that you know the content that you will be diving into, rather than buying a class and subsequently finding out the information isn’t relevant to what you would like to do.
- Stay within your budget. Many courses have a free option that should keep your wallet happy, but if you are looking for absolutely maximum value, you may have to pay. If you end up spending on an online course, staying within your budget can help alleviate some of the pressures associated with taking a class.
- Be aware of your time management skills. Enrolling in an online course gives you certain freedoms when it comes to how you learn. This can be wonderful for those who know how to manage their time effectively but can be an utter pain for those who are a little more carefree with their time. Be certain that you have the dedication to hold yourself accountable to attend class and finish the coursework.
No matter if you select a course of your own choosing or one from our list, enrolling online can be your next step or the first step in your path to a career in machine learning and data analysis.