What are the differences between various machine learning techniques?

What are the differences between various machine learning techniques?

There are several machine learning techniques, including supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled data, while unsupervised learning finds patterns in unlabeled data. Reinforcement learning focuses on training agents to make decisions through trial and error.

Can you give examples of each technique?

Sure! For supervised learning, examples include linear regression and decision trees. In unsupervised learning, clustering algorithms like K-means and hierarchical clustering are common. Reinforcement learning examples include Q-learning and deep Q-networks.

Which technique is best for image recognition?

For image recognition, convolutional neural networks (CNNs), a type of supervised learning technique, are typically the best choice due to their ability to capture spatial hierarchies in images.

What about natural language processing?

In natural language processing, techniques like recurrent neural networks (RNNs) and transformers are widely used. Transformers, in particular, have become the state-of-the-art for many NLP tasks.