5 Types of Machine Learning Algorithms (With Use Cases)
5 Types of Machine Learning Algorithms (With Use Cases) | STATWORX
“The conceptual fundamentals for Machine Learning (ML) were developed in the second half of the 20th century. But computational limitations and sparsity of data postponed the enthusiasm around artificial intelligence (AI) to recent years. Since then, computers have become exponentially faster, and cloud services have emerged with nearly limitless resources. The progress in computational power, combined with the abundance of data, makes Machine Learning algorithms applicable in many fields today.
AI systems are beating human domain experts at complex games, such as the board game Go or video games like Dota2. Surprisingly, the algorithms can find ways to solve the task that human experts haven’t even considered. In this sense, humans can learn from their behavior.
All these success stories have to be put in context. ML algorithms are well suited for specialized tasks; however, they still generalize poorly as of today.
One recent exception is an enormous model in Natural Language Processing – the use of a human language (e.g., English) by a computer. The model is called GPT-3 and has performed exceptionally well in multiple tasks. It is an objective of the AI research community to make models applicable for different jobs…”