- Artificial Intelligence Tutorial
- AI - Home
- AI - Overview
- AI - History & Evolution
- AI - Types
- AI - Terminology
- AI - Tools & Frameworks
- AI - Applications
- AI - Real Life Examples
- AI - Ethics & Bias
- AI - Challenges
- Branches in AI
- AI - Research Areas
- AI - Machine Learning
- AI - Natural Language Processing
- AI - Computer Vision
- AI - Robotics
- AI - Fuzzy Logic Systems
- AI - Neural Networks
- AI - Evolutionary Computation
- AI - Swarm Intelligence
- AI - Cognitive Computing
- Intelligent Systems in AI
- AI - Intelligent Systems
- AI - Components of Intelligent Systems
- AI - Types of Intelligent Systems
- Agents & Environment
- AI - Agents and Environments
- Problem Solving in AI
- AI - Popular Search Algorithms
- AI - Constraint Satisfaction
- AI - Constraint Satisfaction Problem
- AI - Formal Representation of CSPs
- AI - Types of CSPs
- AI - Methods for Solving CSPs
- AI - Real-World Examples of CSPs
- Knowledge in AI
- AI - Knowledge Based Agent
- AI - Knowledge Representation
- AI - Knowledge Representation Techniques
- AI - Propositional Logic
- AI - Rules of Inference
- AI - First-order Logic
- AI - Inference Rules in First Order Logic
- AI - Knowledge Engineering in FOL
- AI - Unification in First Order Logic (FOL)
- AI - Resolution in First Order Logic (FOL)
- AI - Forward Chaining and backward chaining
- AI - Backward Chaining vs Forward Chaining
- Expert Systems in AI
- AI - Expert Systems
- AI - Applications of Expert Systems
- AI - Advantages & Limitations of Expert Systems
- AI - Applications
- AI - Predictive Analytics
- AI - Personalized Customer Experiences
- AI - Manufacturing Industry
- AI - Healthcare Breakthroughs
- AI - Decision Making
- AI - Business
- AI - Banking
- AI - Autonomous Vehicles
- AI - Automotive Industry
- AI - Data Analytics
- AI - Marketing
Artificial Intelligence - Machine Learning
What is Artificial Intelligence?
Artificial Intelligence is the ability of machines to perform tasks like thinking, reasoning, and learning similar to humans. It is a broad field in science that includes various techniques like Machine learning, Natural Language Processing, Robotics, and more.
What is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence that enables machines to learn from data without being explicitly programmed. It uses algorithms to analyze large amounts of data, learn from the insights, and gain patterns and make informed decisions.
In simple words, the machine "learns" from the data and uses this knowledge to make predictions and decisions.
Machine learning algorithms enhance their performance over time as they undergo continuous training and exposed to additional data. Machine learning models are the output or what the program learns by executing an algorithm on training data. The greater the amount of data used, the better the model will get.
There are three main types of machine learning −
- Supervised Learning: In this type of learning, the machine is given labeled data to train algorithms especially to classify data or predict outcomes. Some of the algorithms include Linear regression, Logistic regression, Random Forest, and other.
- Unsupervised Learning: In this type of learning, the machine is given unlabeled datasets to algorithms to find hidden patterns or data groupings. There are three types of unsupervised learning tasks which include clustering, association rule, and dimensionality reduction.
- Reinforcement Learning: In this type of learning, an agent is trained to interpret the environment and learns from the feedback which is can be a reward (positive feedback) or penalty (negative feedback).
Artificial Intelligence and Machine Learning are two term that are often used interchangeably. However, they are not the same thing but are closely connected.
Relationship Between AI and ML
Understanding the relationship between AI and ML is important for developing intelligent systems. The simplest way to understand this is −
- AI is the broader concept of enabling a system to think, act, and learn like humans.
- ML is an application of AI that allows machines to learn from data and gain knowledgeable insights.
Artificial Intelligence is the branch of computer science that covers a variety of approaches and algorithms, and machine learning being one of it.
Machine Learning Vs Artificial Intelligence
We are often confused between machine learning and artificial intelligence. The table below consists of the difference between both the terms −
| Aspect | Artificial Intelligence (AI) | Machine Learning (ML) |
|---|---|---|
| Definition | AI refers to the enabling systems to think, act, and learn similar to humans. | ML is a subset of AI that focuses on algorithms that learn from data and gain insights. |
| Scope | AI is a broad field that consists of various other technologies like Natural Language Processing and Robotics. | |
| Goals | To create systems that can perform tasks replicating humans. | To develop models that can improve their performance over time as they are exposed to more data. |
| Techniques Used | Includes reasoning, learning, planning, and understanding. | Primarily uses statistical methods, neural networks, and decision trees. |
| Complexity | Often involves multiple systems and layers of abstraction. | Generally focuses on specific tasks and can be less complex. |
Benefits of using AI and ML Together
AI and ML together offer some key benefits to enhance organizations and businesses which include −
- Wider Data Ranges: Analyzing and understanding a wide range of structured and unstructured data sources.
- Better Decision-Making: Improving data integrity, accelerating data processing, and reducing human error for more better and relevant decision-making.
- Efficiency: Increasing operational efficiency and reducing costs.
- Analytic Integration: Integrating predictive analytics and insights into business help them grow.