Machine Learning (ML) is an exciting and fast-paced field. Emerging technology is fast becoming the brain behind business intelligence and efficiency gains.
Along with its benefits, every industry wants to implement AI and ML in their domain.
As such, more and more companies are on the verge of hiring skilled ML engineers.
ML skills open a world of opportunities for anyone to develop cutting-edge applications. Such skills also give you a first-class ticket to some of the most exciting careers in the world today.
But how can you get started in ML? With so many ML courses, it's easy to get confused.
Let's jump in!
1. Andrew NG. machine learning with
My first experience with machine learning was in Andrew Ng's Coursera class. Andrew Ng is CEO/Founder Lending AI; co-founder of Coursera; Assistant Professor at Stanford University; Formerly chief scientist, Baidu and founding head of Google Brain.
More than 3.7 million students and professionals worldwide have taken this course. With a 4.9 rating out of 5, it's easy to see why the course is ranked the best on the web.
The course has excellent coverage of supervised and unsupervised algorithms and provides many practical insights around implementation to help learners understand ML concepts.
2. Machine Learning: Hands-on Python and R in Data Science
For beginners who are brand new to the field, this course is ideal for learning how to build ML algorithms in Python and R and comes with code templates. The course is designed by two professional data scientists who share knowledge to help you learn complex principles, algorithms and coding libraries in an amazingly simple way.
Explains most of the essential concepts of ML efficiently and I feel fortunate to have taken this course. Otherwise, I'll still be stuck in my own field that I can't learn to code" - a student review.
This course is fun and exciting and takes you step-by-step into the world of machine learning. With every step and tutorial, the program helps you develop new skills and improve your understanding in the field.
3. Machine Learning Course Fast.ai . By
Fastai is dedicated to providing AI education to both beginners and experienced learners. Whether you are a beginner or have some experience in the field, you will find AI content that is practical-based. The most important thing to note about this course is that it is both free, self-paced, and with a 4.5 out of 5 rating.
Learners use fast.ai library and train model. You can also join AI forums to communicate with peers and practitioners to help you through the entire learning experience.
4. Learn to Learn: Powerful mental tools to help you master difficult subjects
As you will see, there is nothing on AI and ML in this course. Specifically, it gives you easy access to invaluable learning techniques from experts in a range of subjects including math, science, music, literature, sports, and more. You will learn about how the brain uses two different learning methods and how it incorporates information that will thus help you along your ML learning journey. The curriculum also covers learning illusions, memory techniques, handling procrastination and best practices shown by research to be most effective at helping you master difficult topics.
5. Machine Learning Specialization
If you want to build intelligent ML applications, this specialization includes four practical courses that will help you do so. It is designed by leading researchers from the University of Washington to introduce you to the exciting and high-demand field of machine learning.
The course takes you through a series of practical case studies to help you gain applied experience in key areas of ML such as classification, prediction, clustering, and more. It also teaches you how to analyze complex datasets and create systems that adapt and improve over time and build intelligent applications that can make predictions from the data.
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