Getting started as an AI developer

A bachelor’s degree in CS or DS, a master’s degree in computer science or computer engineering, and/or a PhD in either field, or in machine learning that's what you will need to become a developer in AI. Really?

But AI was all science fiction a few years ago. Nobody would have thought it would start popping up in various aspects of our life. How could you have possibly known that a bachelor’s degree in CS is what you should have majored in in college? All hope is not lost!

A few years ago, Kaggle did a user survey that showed only 30% of people working in the artificial intelligence (AI) field have studied machine learning or data science as part of their formal education. The survey also found that 66% of the people were self-taught in machine learning and data science. More than half of them used online resources and courses to learn these subjects.

Getting started as a developer in AI requires a lot of perseverance and focus. But that doesn’t mean it’s impossible. It’s helpful if you have prior knowledge or experience in programming and math. But whether or not you have this knowledge, you can always learn artificial intelligence from scratch right from the comfort of your home. Once you gain a fair understanding of AI, you can begin putting it to practice you can create simple AI solutions.

In this article, I will share an effective learning plan to becoming a developer in AI.

Start with the basics

The study of artificial intelligence can be complicated. It is necessary that you get the basics right. But if you already know the basics, you can skip this part. In case, you don’t have prior knowledge, then I’d recommend that you spend enough time getting to learn the basics. Try and get to discover general information of what artificial intelligence is.

Abstract Thinking

The key to building great AI solutions is about having great problem-solving and logical reasoning skills. In machine learning, you need to find patterns in data, generating a hypothesis, and running experiments. To do this, you need to be able to present ordinary things in an abstract form. For example, to a data scientist, the intersections in Google maps are graphs or the number of cash withdrawals at an ATM is stats. But how do you learn to think like that? Here are a few videos that will get you started with abstract thinking.

A little mathematics literacy

If you would like a career in AI as a developer, you need to know math. You need to know the key concepts of linear algebra, mathematical analysis, and the fundamentals of probability theory. Here are a few courses I’d recommend you take to get a good grasp of mathematics.

Statistical methods

Getting familiar with statistical methods forms the foundation to understand machine learning algorithms. It helps you make better business decisions. Some of the commonly used methods for analyzing data are mean, standard deviation, regression analysis, and hypothesis testing.

Here are a few carefully selected models that illustrate the concepts in the above-mentioned courses and books.

Algorithms

There are various machine learning algorithms that have been developed to solve real problems. If you’ve been following our blog, you would already be familiar with an introduction to machine learning where we talk about the types of algorithms supervised, unsupervised, and reinforcement machine learning algorithms.

machine learning algorithms

Image source: GeeksforGeeks

There are plenty of algorithms, but you can simplify your learning by narrowing it to the top 10 algorithms in machine learning:

  1. Naïve Bayes Classifier Algorithm
  2. K Means Clustering Algorithm
  3. Support Vector Machine Algorithm
  4. Apriori Algorithm
  5. Linear Regression Algorithm
  6. Logistic Regression Algorithm
  7. Decision Trees Algorithm
  8. Random Forests Algorithm
  9. K Nearest Neighbours Algorithm
  10. Artificial Neural Networks Algorithm

Here are a few resources to get your started.

Now that we’ve covered the basics, it’s time to step up your lesson plan.

Programming

Python Programming Language

If you want to be a developer in AI, then most part of your work will revolve around computer-based applications. This means using programming languages and coding. While there are many languages out there, Python is one of the favorite languages among developers. This is because it is easy to learn, it is simple, and there are plenty of libraries, training courses, and free material that you can take advantage of. Here’s a list of some of Python learning resources.

AI

Let’s say, you’ve checked off all the resources and lessons that have been listed so far. AI is a broad field of technologies, theories, programs, methods, algorithms, and practices. How does each of these aspects fit into the broader spectrum of AI?

AI knowledge map

Image source: Forbes

This is an AI knowledge map that was designed by Francesco Corea. This landscape is a useful source of information for those who are new to the space, to help you grasp the complexity and depth of AI at a glance.

Machine learning

Machine learning is the ability of a computer to learn without assistance from a human being. It is the process in which you implement AI. Now, AI can be implemented without machine learning but that will require you to write down detailed step by step instructions for every little task. That means writing millions of lines of code. But with machine learning, an algorithm is trained to find the solution on its own. As mentioned earlier, there are four main ways in which machine learning happens supervised, unsupervised, semi-supervised, and reinforcement learning. To get you started on machine learning, here are a few resources to help you.

Neural networks

The fastest growing aspect of AI is neural networks which originates with the development of linear algebra and probability theory. A neural network is a kind of machine learning that helps a machine correct a task by finding the right connections or make a predetermined decision in a given situation. Here are a few good resources to help you.

You’ve come this far! Congratulations. Now let’s get you to practice what you’ve learned all along. There are two ways in which you can do this take part in Kaggle contests or pick up datasets to work on. Here are a few datasets that you can practice on.

Bonus resources: a lineup of speakers

  • Frédéric Bastien, Lead Theano Developer
  • Matthew Taylor, Open Source Community Flag-Bearer at Numenta
  • Alison Lowndes, Developer Relations NVIDIA
  • Ian Goodfellow, Research Scientist at OpenAI
  • Adam Gibson, Founder, Skymind
  • Angela Bassa, Data Science Manager, EnerNOC
  • Dennis Mortensen, Founder X.ai
  • Peter Norvig, Director of Google Research
  • Kamelia Aryafar, Senior Data Scientist, Etsy
  • Martin Bedard, Lead AI / Gameplay Developer Ubisoft

Closing thoughts

Laying out the learning path is the easiest part and I’ve done that for you. It’s up to you to stay focused on this learning path to get to where you want to be an AI developer. When you begin with a new subject that is as complex as AI, it can be a little scary. You may tend to give up earlier than you want to. But remember, the key is to stay motivated every day. Set aside time for your learning and practice. Be at it and don’t let even the slightest crack in the track be a reason for you to step back. If you have any questions, feel free to drop them as comments here. Good luck!

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