How to break into AI

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In the world of AI this week: Starting next week, we are renaming and changing the structure of weekly post like this and will call it, In the world of AI this week, and will cover more about what is happening in the world of AI in businesses, research, news, funding, product, corporation, law etc. The post will be available for FREE to all the members who sign up for these posts.

Getting On With AI

Resources to learn AI as a beginner

You may want to bookmark today's post to come back to it regularly.

Today's post came from the most common request of members, and that is to know about the various online resources to learn about AI. Everyday series aims to share one post a day to learn AI while there are multiple modules where you can learn about AI at a faster pace. However, most of these resources cater to different needs of users and are based on their background as well.

There are a variety of different paths to learning machine learning. The most common path is to take a course at a university or online. However, there are other ways to learn machine learning as well. You can find many resources on the internet, including websites, videos, and books. There are also boot camps and conferences that teach machine learning.

The best way to learn is often determined by your goals and interests. If you want to become an expert in machine learning, then it's best to take a course at a university or online program. These courses will give you in-depth knowledge of the subject matter and allow you to work with experts in the field. If you're just interested in using machine learning for your own projects, then finding resources on the internet may be better for you since it allows you more flexibility in what topics you learn about and how much time you spend on each one.

No matter which path you choose, make sure that it fits with your goals and interests so that you can get the most out of it!

Don't get scared with the jargons and complexity of the course links that I will share below. At Everyday Series we will cover all of these with one post a day. Should you have any query regarding any of these, feel free to reach out, you can also reply to this email or reach us via the chatbot on the website.

Bottom-up and Top-down approaches

There are two main approaches to learning machine learning: the bottom-up approach and the top-down approach. The bottom-up approach starts with basic concepts and gradually builds up to more complex ideas. The top-down approach starts with more complex concepts and gradually breaks them down into simpler ideas.

The bottom-up approach is often preferred by beginners because it is easier to understand basic concepts than more complex ones. It can also be helpful for people who want a deeper understanding of machine learning, as it allows them to build their knowledge from scratch. However, the downside of the bottom-up approach is that it can be slow at times. In everyday series, we follow a bottom-up approach so that the foundation remains strong.

The top-down approach is often preferred by experienced users because it allows them to quickly get up to speed with new techniques. It can also help them better understand how different parts of a machine learning algorithm work together. However, the downside of the top-down approach is that beginners may find it difficult to follow without first having some background knowledge in machine learning basics.

I will list various resources to get started with AI here.

Python:

Python is a versatile language that you can use on the backend, frontend, or full stack of a web application. It's also great for data science and machine learning projects.

The syntax is relatively easy to learn compared to other languages, and there are plenty of resources available online if you get stuck. Python is used by major companies like Google, Facebook, and Netflix, so it's definitely worth learning!

One of the good courses to learn python is at Coursera.