This is the repository for the LinkedIn Learning course Foundations of AI and Machine Learning for Java Developers
. The full course is available from LinkedIn Learning.
In this course, explore the exciting world of artificial intelligence (AI) and machine learning (ML) from a Java developer’s perspective. Start with the essential principles of AI/ML and why patterns are so important. Learn how to differentiate between AI, ML, and deep learning, and clearly understand predictive AI versus generative AI. Join instructor Frank Greco to build a solid foundation on how to use ML in your software development projects and processes effectively.
These files can be easily built once you specify an API key from a GenAI provider, eg, OpenAI, Google, Anthropic, et al. You should specify your API key as an environment variable in the IDE's runtime configuration options. For example, in IntelliJ,
Run->[Edit Configurations]->[Environment variables]-> OPENAI_API_KEY=....
Due to image licensing restrictions, we cannot include the dog images that we used from the open-source Stanford Dogs Dataset.
Here are the steps for you to download/install the images.
Copy the Chihuahua images from the Stanford Dogs Dataset and put the images into the ./training_data/DogImages2/train/000.Chihuahua folder. In the sibling 000.Negative folder, you can put approximately the same number of non-Chihuahua images. The Kaggle Computer Vision datasets are useful, for example Food or Cars. You should make sure the training data is non-biased by having a similar number of images.
The folder structure should look like:
../training_data/
DogImages2/
train/
000.Chihuahua/ <----- Chihuahua dog image files go here
000.Negative/ <----- Non-Chihuahua images files go here [ie, any object other than chihuahuas, eg, flowers, cars, pizza, et al]
Frank Greco
Senior Technology Consultant, AI/ML Strategist, Developer
Check out my other courses on LinkedIn Learning.