Machine Learning

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 What is Machine Learning

As per the Net and ChatGPT, the definition is like this:

"Machine Learning (ML) is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn from and make decisions based on data. Instead of being explicitly programmed to perform a task, machine learning models are trained on data to identify patterns and make predictions or decisions without human intervention."

"Machine Learning is the science of programming computers so they can learn from data"

"Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed." –Arthur Samuel,1959

"A computer is said to learn from experience E for some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E."  -Tom Mitchell, 1997

Machine Learning can be broadly categorized into three types:

  1. Supervised Learning:

    • In supervised learning, the model is trained on a labeled dataset, meaning that each training example is paired with an output label. The model learns to map inputs to the correct outputs, and the goal is to predict the output for new, unseen data.
    • Examples: Image classification, spam detection, sentiment analysis.
    • Algorithms: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Random Forests, Neural Networks.

  2. Unsupervised Learning:

    • In unsupervised learning, the model is trained on an unlabeled dataset. The goal is to identify patterns, structures, or relationships within the data without specific output labels.
    • Examples: Clustering, anomaly detection, and dimensionality reduction.
    • Algorithms: K-Means Clustering, Principal Component Analysis (PCA), Autoencoders.

  3. Reinforcement Learning:

    • In reinforcement learning, an agent interacts with an environment and learns to make decisions by receiving feedback in the form of rewards or penalties. The goal is to learn a policy that maximizes cumulative rewards over time.
    • Examples: Game playing (e.g., chess, Go), robotic control, and self-driving cars.
    • Algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods.

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