4 free machine learning courses for a mid-career switch to artificial intelligence
Are you looking to switch careers into the booming and dynamic world of artificial intelligence? It may be a complex technology, but free courses in machine learning will get you up to speed so you can start a new career into this field.
Machine learning is the application of artificial intelligence (AI), that provides systems with the ability to automatically learn and improve from experience.
Companies across all industries today plan to implement machine learning to streamline processes and make them more intelligent and efficient.
According to CustomerThink, “Machine learning is employed by data scientists to find patterns and predict important outcomes. The application of machine learning reaches across industries (e.g., healthcare, education) and professions (e.g., marketing, content management), and data professionals have many different tools, methods, and products they can use to extract useful insights.”
Machine learning involves compiling data, examining it for patterns, and developing a prediction for future outcomes. As machine learning becomes more and more advanced and fine-tuned, automation will become more commonly used across industries.
For beginners with an interest in learning more about machine learning, you do not necessarily have to spend thousands of dollars on a degree programme.
Instead, you can upskill yourself through the abundance of free online courses in machine learning, artificial intelligence, automation, and more.
As online courses are self-paced, you can work according to your own schedule and don’t have to give up your full-time career while pursuing other career options part-time.
Here are four online courses highlighted by Benzinga for those looking for a mid-career switch in machine learning and AI:
This beginner-level course hosted on Coursera by the University of London, led by Dr. Marco Gillies, a senior lecturer in the computing department, will equip you with the basics of how modern machine learning technologies work, and how to explain and predict the way data affects the results of machine learning.
Plus, you will learn how to use a non-programming based platform and train a machine learning module using a dataset and form an informed opinion on the benefits and dangers of machine learning to society.
There are four modules in this course: machine learning, data features, machine learning in practice, and your machine learning project.
The course takes approximately 22 weeks to complete and you can enrol for free on Coursera.
This introductory course by Stanford University, facilitated by Adjunct Professor Andrew Ng, is more extensive but ideal for beginners who are interested in learning the fundamentals of machine learning.
It covers an array of modules such as Linear Regression with One Variable, Linear Algebra Review, Neural Networks: Representation, Neural Networks: Learning, Advice for Applying Machine Learning, Linear Regression with Multiple Variables and more.
Students will finish the course armed with the knowledge and skills required to leverage machine learning techniques to solve real-world issues. It takes approximately 56 weeks to complete and is also hosted on Coursera.
Another beginner-level course on Coursera, this one is offered by the University of Washington. This course is unique as students get hands-on experience with machine learning from a series of practical case-studies.
According to the website, “At the end of the first course, you will have studied how to predict house prices based on house-level features, analyse sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.”
By the end of the course, you should be able to identify potential applications of machine learning in practice, select the appropriate machine learning task for a potential application, build an end-to-end application that uses machine learning at its core, and more.
This course is one out of four courses of a Machine Learning specialisation, and takes about 15 hours to complete.
Hosted on Udacity, this course takes approximately 11 weeks to complete, teaching students the end-to-end process of investigating data through a machine learning lens.
Students will also learn how to how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.
This free course is part of the Data Analyst nanodegree online programme on Udacity.