INTRODUCTION TO MACHINE LEARNING
Machine learning is a subfield of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed. Machine learning
algorithms use historical data as input to predict new output values.
Key Concepts:
Data: The foundation of machine learning. It can be structured
(e.g., tables), unstructured (e.g., text, images), or semi-structured
(e.g., JSON).
Algorithms: The mathematical formulas and rules that guide
the learning process. Examples include linear regression,
decision trees, and neural networks.
Models: The output of the learning process, representing the
patterns and relationships discovered in the data.
Training: The process of feeding data to the algorithm to adjust
its parameters and improve its accuracy.
Prediction: The process of using the trained model to make
predictions on new, unseen data.
Types of Machine Learning:
1. Supervised Learning:
o Labeled data: The data used for training has both input
and output values.
o Goal: To learn a mapping function that can predict the
output for new, unseen input data.
o Examples:
Regression: Predicting continuous values (e.g., stock
prices, house prices).
Classification: Predicting categorical values (e.g.,
spam detection, image recognition).
2. Unsupervised Learning:
o Unlabeled data: The data used for training only has input
values, no output labels.
Machine learning is a subfield of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed. Machine learning
algorithms use historical data as input to predict new output values.
Key Concepts:
Data: The foundation of machine learning. It can be structured
(e.g., tables), unstructured (e.g., text, images), or semi-structured
(e.g., JSON).
Algorithms: The mathematical formulas and rules that guide
the learning process. Examples include linear regression,
decision trees, and neural networks.
Models: The output of the learning process, representing the
patterns and relationships discovered in the data.
Training: The process of feeding data to the algorithm to adjust
its parameters and improve its accuracy.
Prediction: The process of using the trained model to make
predictions on new, unseen data.
Types of Machine Learning:
1. Supervised Learning:
o Labeled data: The data used for training has both input
and output values.
o Goal: To learn a mapping function that can predict the
output for new, unseen input data.
o Examples:
Regression: Predicting continuous values (e.g., stock
prices, house prices).
Classification: Predicting categorical values (e.g.,
spam detection, image recognition).
2. Unsupervised Learning:
o Unlabeled data: The data used for training only has input
values, no output labels.
