Machine learning is finally at the stage when not only computer scientists can utilize it efficiently, but businesses as well. This technology can help with customer personalization, determining where and when to invest resources, calculating customer lifetime value, and overall financial analysis, to name a few.
While it’s possible to achieve the same results with other means, there are cases when machine learning is simply the best way to go. In 2020, machine learning is finally out of the realm of hardcore computer science. It’s now a practical tool that businesses can use to enhance their performance and optimize the workflow. Even if you are not interested in becoming a developer, it’s still useful to familiarize yourself with basic concepts, definitions, and applications of machine learning.
The Best Machine Learning Courses For Executives and Beginners
As a business owner or a manager, you won’t need to dive in too deeply. However, you will require a certain level of technical understanding to communicate effectively with your team of data scientists and machine learning engineers. Whether it’s employees in your department or your outsourcing team, being aware of the definition of ML and how it works, can go a long way towards ensuring fruitful collaboration.
The courses below will provide you with a basic understanding of what machine learning is, how it works, and how you can apply it to your business.
1. Machine Learning Crash Course on Google
If you are completely new to machine learning, Machine Learning Crash Course is probably one of the best places to get familiar with the concept. As the name implies, it’s a bunch of fundamentals, packaged neatly into short videos, text explanations, and code snippets to make the course as newbie-friendly as possible. The crash course aims to provide an ultimate introduction without targeting any specific group.
Google’s Machine Learning Crash Course starts with the definition of ML and several key terms related to the technology. It then progresses to regressions, creating and training predictive models, and building neural networks. The course ends with the study of several real-world cases of ML application.
The course is self-paced and free, meaning anyone can take it anytime and have full control over their progress. With a reasonable amount of dedication and minimal mathematics and technology background, you should be able to complete the course in approximately 15 hours. In comparison with other courses that frequently take several weeks to complete, Machine Learning Crash Course is the most efficient introductory course for beginners.
2. Machine Learning with Python on Coursera
If you have time to spare, it can be beneficial to invest in IBM’s Machine Learning with Python on Coursera. The course is free to audit, and it provides an incredible overview of both the practical and the technical sides of ML. In four weeks, the two instructors will guide you through the process of getting familiar with the concept of machine learning all the way to creating your own models and testing them on real-life problems.
Getting your hands dirty and studying the technical part of ML can help illustrate how imperfect machine learning still is, and when it might be more appropriate to use other methods. For businesses, ML is a tool, and it’s hard to evaluate its potential effectiveness without knowing its limits. IBM’s course is perfect for getting familiar with the tools that data scientists use and problems they may encounter, provided you are ready to learn new technical skills.
3. Introduction to Data Analytics for Managers on edX
Introduction to Data Analytics for Managers offered by the University of Michigan is not a course centered around machine learning but rather an overview of the application of data science in business. Machine learning is only one tool that people rarely use outside the larger scope of analytics and prediction. Learning the context for its application is, therefore, often more useful than digging into ML itself.
This free course on edX is a perfect entry point for managers, business owners, and other individuals in non-technical positions who want to learn how data science can be used practically. The course presents video lectures that cover the basic theoretical information. Also, those who enroll will work with case studies, a graphical development environment, and practical machine learning tasks to solidify their understanding.
4. Building a Data Science Team on Coursera
Building a Data Science Team offered by Johns Hopkins University targets business owners, managers, HR specialists, and everyone who is looking to gather their team of data scientists. It is less about the technical sides of machine learning and more about how the communication between ML engineers and executives should work. This course presents an effective introduction to the field of data science with the focus on the people who are professionals in this area.
According to the authors of the course, Building a Data Science Team will take approximately one week of study with 4-6 hours of dedicated learning. During this time, you will learn how to conduct an interview to find the best fit for your position, what to look for in a data scientist, what professional roles exist in the field, how to manage a team, and how your data scientists and their activity will relate to other departments within the company.
5. Data Science in Real Life on Coursera
Data Science in Real Life is another course by Johns Hopkins University that follows the track of the previously discussed Building a Data Science Team. This course examines cases of the practical application of machine learning and other data science tools and strategies to better understand how they work and what their purpose is. Unlike the previous course, Data Science in Real Life also pushes you to apply the learned concepts and determine when machine learning is better than classical approaches, how to manage data quality, and how to identify strengths and weaknesses of various models and experimental designs.
Machine learning is a significant portion of the whole vast field of data science. Upon immersing yourself into ML, you will inevitably find that the two are virtually inseparable. The courses discussed above cannot possibly cover every single detail in machine learning, but all of them present good starting points to get familiar with ML and begin using it to your advantage.