Artificial intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence, such as understanding language, making decisions, or playing games. AI encompasses various subfields, including machine learning, robotics, generative AI, and more.



Machine learning (ML) is a subset of AI that enables computers to learn from data without explicit programming. By analyzing data, ML algorithms identify patterns, make predictions, and improve over time.

In short, AI aims to simulate human intelligence, while machine learning is a method within AI that allows systems to learn and improve autonomously.

Learn more about AI vs. machine learning.

There are five main types of machine learning, each offering distinct ways for models to learn based on data structure and environmental interaction. The five categories are:



1) Supervised learning: In this approach, models are trained using labeled data, where the correct output is known. "Supervised" indicates that these labels guide the model in learning relationships between inputs and outputs, enabling tasks like classification and prediction. Learn more about supervised learning.

2) Unsupervised learning: In contrast to supervised learning, unsupervised learning works with unlabeled data to identify patterns or groupings in the data without being told the correct answers. This method is used for tasks like clustering and dimensionality reduction. Learn more about unsupervised learning.

3) Semi-supervised learning: This method combines both labeled and unlabeled data, using a small portion of labeled data to guide the learning process, which is then applied to a larger unlabeled dataset. Learn more about semi-supervised learning.

4) Reinforcement learning: Models in reinforcement learning interact with an environment and learn through trial and error, aiming to maximize rewards over time. Learn more about reinforcement learning.

5) Self-supervised learning: In this recent machine learning development, self-supervised learning uses unlabeled data to generate labels from the data itself. This approach is seen in large language models like GPT. Learn more about self-supervised learning.

No, deep learning is not the same as machine learning, but it is a subset of it.

Machine learning refers to the broader field where models are trained to recognize patterns and make decisions based on data. Deep learning is a specialized type of machine learning focusing on neural networks with many layers (hence "deep") to model complex patterns in large datasets. It's especially effective in tasks like image recognition, natural language processing (NLP), and other data-intensive applications.

Learn more about machine learning vs. deep learning.