It somewhat depends on if you are looking to build AI to address business problems or if you are more interested in the type of AI work you see companies like Google discussing.
I can speak to what "AI" means for most businesses outside Top Tech which more frequently work with tabular, relational, or log data rather than image and text. For these companies, this is what you need to learn how to do
1. Define a prediction problem and extract labels
2. Organize and clean the data for prediction
3. Perform feature engineering by applying domain expertise
4. Apply an off-the-shelf open source machine learning algorithm like a random forest
Assuming you have access to data and programming skills to clean your data, defining prediction problems and performing feature engineering are the most important skills you have to pick up. For machine learning you can you use open source libraries like scikit-learn or tensorflow.
At my company, we've noticed a lot of programmers are intimated by the feature engineering step in particular, so we tried to make it easier by creating an open source library called Featuretools [0].
I can speak to what "AI" means for most businesses outside Top Tech which more frequently work with tabular, relational, or log data rather than image and text. For these companies, this is what you need to learn how to do
Assuming you have access to data and programming skills to clean your data, defining prediction problems and performing feature engineering are the most important skills you have to pick up. For machine learning you can you use open source libraries like scikit-learn or tensorflow.At my company, we've noticed a lot of programmers are intimated by the feature engineering step in particular, so we tried to make it easier by creating an open source library called Featuretools [0].
[0] https://github.com/featuretools/featuretools