In the last decade, a new trend has emerged in Machine Learning called Deep Learning. Deep Learning refers to a class of artificial neural networks (ANNs) composed of many processing layers. ANNs have existed for many decades, but attempts at training deep architectures of ANNs failed until Geoffrey Hinton's breakthrough work in the mid-2000s. In addition to algorithmic innovations, the increase in computing capabilities using GPUs (general processor units) and the collection of larger datasets are all factors that have helped in the recent surge of deep learning. 
How do I get started with AI?
If you come from a non-technical background, the best way to start is to take the “AI For Everyone” course from deeplearning.ai. You’ll need to dedicate around 10 hours to complete it. It was designed by Andrew Ng and is structured in 4 lessons:
⇒ What is AI
⇒ Building AI Projects
⇒ AI in Your Company
⇒ AI and Society
If you come from a technical background and you want a hands-on experience, then you’ll need some coding capabilities. Most of Machine Learning and Deep Learning courses use Python programming language.
If you don’t have experience in Python, then I recommend you start with one of the following resources:
⇒ Codecademy - Learn Python (Free with offers)
⇒ Book - A Whirlwind Tour of Python
- Fast-paced introduction to essential features of the Python language, aimed at researchers and developers who are already familiar with programming in other languages.
⇒ Content as Jupyter notebooks (Free)
- Before building a Machine Learning model, I recommend you become familiar with the Python data science stack (Jupyter notebooks, Pandas, Numpy, Matplotlib, etc…). The material below is a good introduction to the topic.
⇒ Wes McKinney - Python for Data Analysis Book
- Jupyter notebooks (Free)
- It includes a complete set of instructions to manipulate, process, clean, and crunchdatasets in Python. This hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn pandas, NumPy, IPython, and Jupyter in the process.
⇒ Dataschool - Best practices with pandas (10 videos) (Free)
- Best practices with pandas to help students become more fluent at using pandas to answer data science questions and avoid data science errors.
Once you are familiar with Python stack for data analysis, you can start building your Machine Learning and Deep Learning projects. Below are some of the best resources for both:
⇒ Fast.ai - Introduction to Machine Learning for Coders (Free)
- Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.
⇒ Andreas Mueller - Introduction to Machine Learning (Free)
⇒ Sebastian Raschka - Python Machine Learning, 2nd Edition
⇒ Fast.ai - Practical Deep Learning for Coders, v3
- Excellent course to learn Deep Learning.
As you have seen, there is a vast amount of information online that will help you get started with this technology. AI will be one of the areas that spurs most interest and will have most impact across sectors. It is a discipline that requires many technical and technological skills, but also human skills. As well as knowledge in mathematics and statistics, it’s important to learn programming languages and the possibilities the cloud offers. Finally, keep in mind that working in this field will always keep you outside of your comfort zone which means you will have to be patient and meticulous.