Differences Between Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing
When I started learning Data Science, these terms Artificial Intelligence(AI), Machine Learning (ML), Deep Learning(DL) , Natural Language Processing (NLP) used to confuse me a lot. In this blog, I will explain the differences between these terms in the easiest way.
Lets understand:
Artificial Intelligence(AI):
In simple words, it is a computer system which requires human level intelligence to solve the problems of human and make their life more comfortable. Now, how does it do?? It involves various approaches and techniques to create machines and software that can mimic cognitive functions associated with human intelligence.
The various approaches and techniques are:
- Machine Learning (ML)
- Deep Learning(DL)
- Natural Language Processing(NLP)
1.Machine Learning (ML): is a subfield of AI that improves the accuracy of AI systems by leveraging big data. In this area, we have a lot of data such as photos, messages, documents, human behaviour patterns etc. ML Algorithms use this data to predict future outcomes. It includes supervised learning, unsupervised learning and Reinforcement learning.
2. Deep Learning(DL): is a specialized field of ML that enables artificial neural networks, multiply layers to handle complicated tasks such as face recognition and autonomous driving in vehicles, generate images, create videos, craft creative things, produce music and many other applications.
DL requires more significant computational resources, specialized hardware to automatically learn hierarchical features, intricate features from the data unlike other machine learning algorithms where human intervention is required to identify features.
3. NLP (Natural Language Processing): a specialized field within AI that focuses on the interaction between computers and human language. NLP plays an important role in sentiment analysis (analyzing the mood of expressed emotions such as positive/negative or neutral), language translation (translating from one language to another), chatbots and virtual assistants (to answer human queries), text summarization (creating summaries from articles), speech recognition (for voice-controlled systems), and many other areas. Detailed Explanations and its real time applications in our daily life.
I hope I am able to explain the basic distinctions between the terms. :) :)
Other Related Blogs:
Distinctions between Data Scientist, Data Engineer, Machine Learning Engineer
How Different Data Professionals Contribute in a Single Project
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