Machine Learning
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on building systems that can learn from and make decisions based on data.
In simple terms, machine learning means making the machine learn things like human beings. For example, a human child is taught how to differentiate between cats and dogs and colors. In the same way, we make machines learn the differences between cats and dogs by providing data.
Types of machine learning
Supervised Learning:
Description: In supervised learning, models are trained on labeled datasets, where the input data is paired with the correct output. The goal is to learn a mapping from inputs to outputs.
Examples: Regression and classification problems, such as predicting house prices(regression) or determining if an email is spam or not.
Unsupervised Learning:
Description: Models are trained on unlabeled datasets and tasked with finding hidden patterns or intrinsic structures in their input data.
Examples: Clustering (grouping similar data points, e.g., customer segmentation) and dimensionality reduction(reducing the number of features, e.g., Principal component analysis).
Semi-Supervised Learning:
Description: This approach uses a combination of labeled and unlabeled data for training. It's useful when obtaining fully labeled data is expensive or impractical.
Examples: This is often used in scenarios where only a small amount of data is labeled, and the majority is unlabeled.
Reinforcement Learning:
Description: An agent learns to make decisions by performing actions and receiving feedback in the form of rewards or penalties. The goal is to maximize cumulative rewards by learning a policy.
Examples: Training robots to navigate environments.
Deep Learning:
Description: A subset of ML involving neural networks with many layers (deep neural networks). It's particularly effective for complex tasks such as image and speech recognition.
Examples: Convolutional Neural Networks (CNNs) for image recognition.
AI vs. ML vs. DL
Artificial Intelligence (AI):
Scope: A broad field encompassing any technique that enables computers to mimic human intelligence. It includes rule-based systems, expert systems, machine learning, and beyond.
Example Applications: Natural language processing, robotics, expert systems.
Machine Learning (ML):
Scope: A subset of AI focusing on algorithms that allow computers to learn from and make predictions or decisions based on data. ML algorithms improve over time as they are exposed to more data.
Example Applications: Fraud detection, recommendation systems, predictive maintenance.
Deep Learning (DL):
Scope: A subset of ML based on neural networks with multiple layers that can learn hierarchical representations of data. DL models require large amounts of data and computational power.
Example Applications: Autonomous vehicles, voice assistants, medical image analysis.
In summary, AI is the overarching concept, ML is a key approach within AI that focuses on learning from data, and DL is a sophisticated ML technique that uses neural networks to learn from large-scale and complex datasets.