In this article, we will learn the difference between AI (Artificial Intelligence), ML (Machine Learning), DL (Deep Learning) and DS (Data Science). These terms are often used interchangeably but actually, they refer to the different concepts within the field of computer science and data analysis.
Artificial Intelligence (AI)
Artificial Intelligence (AI) is the broadest concept and it refers to the simulation of human intelligence in machines through particular algorithms. These machines are designed to think and act like humans.
Based on the capabilities, AI is further categorized into.
- Artificial Narrow Intelligence: Designed for specific tasks. Example: Speech recognition systems like Siri, Alexa, Google Assistant, Spam Filters, etc.
- General AI (Artificial General Intelligence): Possess the ability to understand, learn, and apply knowledge across a wide range of tasks at the Human level. It is still theoretical and has not been achieved yet. Example Sophia (Humanoid robot).
- Artificial Super Intelligence: Surpasses human intelligence in all aspects, including creativity, problem-solving, etc.
Different Applications of Artificial Intelligence
- Machine Learning
- Vision: Image Recognition, Facial Recognition
- Hearing: Voice and Speech Recognition
- Natural Language Processing (NLP): Natural Language Understanding (NLU) and Natural Language Generation (NLG).
- Expert Systems
- Robotics etc.
Machine Learning (ML)
Machine Learning is a subset of AI that focuses on the development of algorithms and statistical models that allow computers to learn from and make predictions based on the data.
![Machine Learning]()
Machine Learning is further categorized into.
- Supervised Learning: Here model is trained on a labelled dataset means each training example is paired with an output. Examples of Supervised Learning algorithms are Regression and classification.
- Unsupervised Learning: In unsupervised learning, the model is trained on data without labelled responses. Examples of Unsupervised Learning Algorithms are Clustering and dimensionality reduction.
- Semi-Supervised Learning: It Combines a small amount of labelled data with a large amount of unlabelled data during the training.
- Reinforcement Learning: The system observes the environment and learns the ideal behaviour. In this, the Model receives feedback in the form of rewards or penalties
Some examples of Machine Learning are spam Email Filtering, Recommendation Systems etc.
Deep Learning (DL)
Deep Learning is a subset of ML that uses neural networks (similar to neuron networks present in the brain) with many layers to analyse various factors of data. These networks can automatically learn to represent data through multiple levels of abstraction, making deep learning particularly powerful for the task involving large and complex datasets.
Examples
Image Recognition, Autonomous driving technology, Natural Language Processing etc.
Data Science (DS)
Data Science is a field which uses scientific methods, processes, algorithms and systems to extract knowledge & insights from the data. It combines principles from Statistics, computer science, and domain expertise to analyse and interpret complex data sets. Data Science involves tasks such as data cleaning, data processing, data analysis, data visualization & data interpretation etc.
Some of the popular Tools and Techniques in Data Science are mentioned below.
- Programming Languages: Python, R, SQL
- Data Manipulation: Pandas, NumPy, dplyr
- Data Visualization: Matplotlib, Seaborn, ggplot2
- Machine Learning: Scikit-learn, TensorFlow, Keras
- Statistical Analysis: Hypothesis testing, regression analysis
Both fields, Data Science and Artificial Intelligence interest and complement each other (refer to the above image). Data science provides the data and analysis required for the AI Models. AI relies on Data Science for data preparation, analysis, and development of the models that require a large amount of data for the training.