Artificial Intelligence is all the rage today and rightly so, given its broad spectrum application and usefulness in life. So for those interested in knowing more about AI and learning the basics, here is a quick guide on how to get started.
Basics:
Books: Before understanding the 'how', it makes sense to understand the 'why'. So if you are into books and reading, “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig is a great start. It is a great textbook for AI and provides a comprehensive overview of everything from search algorithms to machine learning. The chapters can be dense, but it's a great investment.
Online Courses: There are free and paid online courses on AI. I would highly recommend Andrew Ng's courses on AI- he has one on Coursera and his Stanford lectures are also available on You Tube.
YouTube Channels: Channels like 3Blue1Brown have excellent videos explaining AI and machine learning concepts in simple terms.
Python:
Python is widely used in AI because it’s simple, readable and has a ton of libraries that make life easier. So Python readiness is definitely a big help in learning AI.
Additionally, familiarity with some Python libraries such as NumPy and Pandas for data manipulation, Matplotlib for data visualization, Scikit-learn for basic machine learning algorithms and TensorFlow or PyTorch for deep learning woud be a huge boost.
Data/Machine Learning:
This is where the exciting part begins. Machine learning is a huge part of AI and it’s fun to train models that can make predictions or classify data.
Again, Andrew Ng’s Machine Learning Course is amazing. It covers algorithms like linear regression, decision trees, and neural networks.
Armed with some machine learning basics, Kaggle is a great platform for hands-on practice. It’s a place to find datasets, pre-trained models and essentially, a place to get some hands-on experience.
Deep Learning/ Neural Networks:
While I don't claim to be any kind of expert here, I am currently going through a course by Andrew Ng on Deep Learning Specialization which covers convolutional neural networks (CNNs), and recurrent neural networks (RNNs) and offers a structured path to learn Neural Networks/ Deep Learning.
Fast.ai also has several tutorials with an emphasis on pratical implementation.
Projects:
Finally, it is important to work through a project to test our learning and to implement theory into pratice. So working on projects like image classification, or text sentiment analysis, building chatbots all help in enhancing our learning.
If you have any tips or resources that worked for you, I’d love to hear them in the comments below. Let’s learn together!
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