Useful Info
If you’re a programmer who’s trying to change lanes or just a beginner developer who wants to learn more about Artificial Intelligence, you will need a starting point in terms of research and documentation.
Machine learning has been on the rise, speaking of interest in the past five years, with most of the curiosity for the industry being focused in China, Singapore, India, South Korea, Nepal, Israel, Switzerland and the United States. Europe only has one country listed in the top 10 by region, but it is slowly catching up with a higher number of searches on AI in Ireland, Denmark, Sweden and the U.K. The sub-field of machine learning / artificial intelligence has increasingly gained popularity mostly because of the “big players” in the market, but the open source machine learning framework that has seem to got everyone’s attention is TensorFlow – which just recently grew over 1000 contributors.
TensorFlow™ is an open source software library for high performance numerical computation.
Now let’s talk a bit about how you can get access to relevant information on the Machine Learning & Artificial Intelligence topics.
Obviously, the best way to start would be to sign up to a technical university that has programs discussing. This way you would have access to lecturers with experience and you’d be able to clear out things directly with the professor. But if you’re already past that time in your life, here is where you can start:
1. Artificial Intelligence: A Modern Approach – a book by Stuart Russell and Peter Norvig – which covers all of the major topics. The book is an “up-to-date introduction to the theory and practice of artificial intelligence”.
2. Intro to Machine Learning – Pattern Recognition for Fun and Profit – a Udacity course where you’ll have as instructors two experienced leads: Katie Malone and Sebastian Thrun.The course if free (at least at the time of writing this article) and should take approximately 10 weeks to complete it. “This is a class that will teach you the end-to-end process of investigating data through a machine learning lens.”
3. Machine Learning – taught by Andrew Ng (Adjunct Professor, Stanford University) – is an 11 weeks course which will take you from linear regression with one variable to large ccale machine mearning.This is probably one of the most complete guides into Machine Learning currently available online.
4. Machine Learning A-Z™: Hands-On Python & R In Data Science (Kirill Eremenko, Hadelin de Ponteves) is available on Udemy. This course will help you learn how to build robust Machine Learning models, make accurate predictions or know which Machine Learning model to choose for each type of problem.
Of course, if you’re searching for a free course for beginners, because you’re not sure the topic will catch your interest on the long run, you can also have a look over these Free A.I. Resources from Google.
If you don’t have a technical background and don’t really want to tackle a steep learning curve in the domain of Artificial Intelligence, but still would like to develop a project, you always have the option of getting in touch with a software development company that has specialized personnel to help you at all stages of your project, starting from consultancy and design and ending with deployment.
Have you tried out any of the resources above or have other courses and books you’d like to share? Drop us a comment using the comment form below, we’re happy to hear your thoughts.