Artificial intelligence has taken the world by storm, and Python is the standout language used in AI development. This cheat sheet is designed to give you an overview of the different types of artificial intelligence models and how they can be implemented in Python for various tasks.
Understanding the types of artificial intelligence
It’s tempting to think of AI as one type of model (your typical chatbot, for example), but AI model types are many and numerous. It can be helpful to group these models into different categories depending on the problem type or specific technique. AI is often categorized into expert systems (rule-based AI systems) and machine learning (models learn from data). In the machine learning group, popular approaches include classical machine learning, neural networks, and reinforcement learning models. The following table outlines the different AI model types.
Artificial intelligence model types
| Category |
Subcategory |
Description |
Example Model |
| Expert systems |
Rule-based |
Models that use (typically human) created rules to evaluate a series of conditions. |
If-then condition checking on a set of data. |
| Expert systems |
Fuzzy logic |
Models that use a series of membership functions to turn crisp values into “fuzzy” human-readable conditions. |
A fuzzy controller handling “cold”, “just right”, and “hot” temperature conditions in a tank system. |
| Machine learning |
Classical machine learning |
Models that learn from a dataset using a wide variety of statistical analysis tools. |
A decision tree predicting a manufacturing fault. |
| Machine learning |
Neural networks |
Models that use a biological model of the brain to train on (usually large) datasets. These are definitely the most popular forms of AI models in use today. |
A chatbot recommending the next movie you should see based on your previous viewing history. |
| Machine learning |
Reinforcement learning |
Models that learn from previous mistakes and try new solutions to optimize some metric. |
A genetic algorithm finding the optimal configuration of a floor plan. |
Exploring Python libraries for AI
One of the main advantages of using Python as your AI development language of choice is the sheer number of libraries written for Python. The following table breaks down some of the popular Python AI libraries and what they are used for.
Popular Python libraries for AI model development
| Library |
Link |
Use |
| Pandas |
https://pandas.pydata.org/docs |
Data manipulation, especially for spreadsheet-type or tabular data. This library is magic for filtering, cleaning, and organizing data before passing it to an AI model. |
| scikit-learn |
https://scikit-learn.org |
Training classical machine learning models like decision trees, linear regression models, support vector machines, and ensemble models. This library also helps with some tasks like data normalization and splitting datasets into training, validation, and test subsets. |
| OpenCV |
https://docs.opencv.org/4.x |
All types of image data. OpenCV is an image processing powerhouse that is excellent for transforming pictures into model-usable data, or combining AI and more traditional machine-vision techniques. |
| Keras |
https://keras.io |
Neural-network model training. This library (a wrapper for the TensorFlow library) is one of the most popular to build, train, and test neural network–based techniques. |
| PyTorch |
https://docs.pytorch.org/docs/stable/index.html |
Neural-network model training. This is the other most popular approach for training neural-network techniques, and allows very high levels of model customization. |
| LLaMA |
https://huggingface.co/meta-llama/Llama-3.1-8B |
Compact, high-performance local large language model by Meta. Optimized for dialogue and assistant-type interactions. |
How to use Python & AI together
The possibilities of using Python and AI together are practically limitless. Python and AI can be used to create or interact with large language models, identify objects in a scene, predict the next disaster, or look for an unlikely solution in a sea of possibilities. Some popular applications of Python and AI are listed in the following table.
Popular Python & AI applications
| Application |
Model Type(s) |
Examples |
| Detecting objects in images |
Convolutional neural networks |
Drawing a box around a rubber duck any time it appears in an image; masking out a scratch in a piece of lumber on a conveyor belt |
| Forecasting future events |
Neural networks, long short-term memory networks (LSTMs) |
Turning a computer screen red when a machine goes out of tolerance; sending an email to a farmer when there’s likely to be a frost event in the next three days |
| Classifying samples |
Neural networks, linear regression models, decision trees, ensemble models |
Determining whether a picture contains a cat or a dog; identifying heat stress in field crops; labeling the likely causes of faults |
| Automating tasks |
Agentic AI models based on transformer neural networks |
Displaying company-documented resources to solve a specific problem; scraping the web |
| Exploring solution spaces |
Genetic algorithm |
Building an optimal plan from a large list of possibilities; finding likely combinations of problem factors for an environmental condition |
| Smart storage, retrieval, and searching of large text collections |
Large language models |
Building a highly searchable and context-aware storage of related text; also good for code generation with human assistance |
Planning an AI project
Every AI project is unique, but it’s helpful to follow a framework when planning a new and spectacular use of the latest technology. Talking to a domain expert — someone who is a wealth of information about the landscape of a given problem — is one of the most important things to do before implementing an AI model. These are the people who will be able to tell you about any potential pitfalls and who can help interpret your model results.
You should also define the type of problem you’re facing and look at any data you have available to you. Often, the data will be the biggest factor in choosing the type of AI you implement. At this point, you can create a list of potential models and evaluate them based on their problem fit, resource and data needs, and complexity. Once you have a couple of winners, it’s time to lock it!
It’s rare to find a perfect solution in one go. Usually there will be several (hundred) iterations before a model is perfectly tuned. Make sure to pick measurable metrics that will give you the best indication of how the model will perform “in the wild.” Once your model is ready for the big time, you’ll tie it to a useful output — drawing a graph, blinking a light, writing text, sending you an email, or maybe even moving a robot.
