Basics of Machine Learning Algorithms
Unsupervised Learning and Pattern Recognition
Identifying patterns or structure in data without
labeled responses
Examples: clustering, dimensionality reduction,
anomaly detection
Key Factors Enabling Machine Learning in Today's Era
Increased data availability and storage capabilities
Advances in algorithms and computational power
Improved understanding of machine learning
principles
Applications of Machine Learning in Real Life
Image and speech recognition
Natural language processing
Recommender systems
Predictive maintenance
Types of Machine Learning Paradigms
Supervised Learning and Labeled Data
Learning from example input-output pairs
Regression and classification tasks
Unsupervised Learning and Pattern Recognition
Identifying patterns or structure in data
Clustering and dimensionality reduction tasks
Reinforcement Learning and Feedback Mechanisms
Learning through trial and error
, Control tasks and game playing
Note: This note covers the topic of Basics of Machine
Learning Algorithms with a focus on Unsupervised
Learning, Key Factors enabling Machine Learning, Real-life
applications, Types of Machine Learning Paradigms with
details on Supervised Learning, Unsupervised Learning,
and Reinforcement Learning.
Types of Machine Learning Paradigms
Supervised Learning and Labeled Data
Involves a target/outcome variable (or dependent
variable) which is to be predicted from a given set of
predictors (independent variables)
Training dataset contains examples of input-output pairs
Unsupervised Learning and Pattern Recognition
Does not involve a target/outcome variable to be
predicted
Aims to model the underlying structure or distribution in
the data
Mainly used for clustering and association
Reinforcement Learning and Feedback Mechanisms
An agent learns to behave in an environment, by
performing certain actions and observing the
results/rewards
The goal Is to learn a series of actions that maximizes the
final reward
Unsupervised Learning and Pattern Recognition
Identifying patterns or structure in data without
labeled responses
Examples: clustering, dimensionality reduction,
anomaly detection
Key Factors Enabling Machine Learning in Today's Era
Increased data availability and storage capabilities
Advances in algorithms and computational power
Improved understanding of machine learning
principles
Applications of Machine Learning in Real Life
Image and speech recognition
Natural language processing
Recommender systems
Predictive maintenance
Types of Machine Learning Paradigms
Supervised Learning and Labeled Data
Learning from example input-output pairs
Regression and classification tasks
Unsupervised Learning and Pattern Recognition
Identifying patterns or structure in data
Clustering and dimensionality reduction tasks
Reinforcement Learning and Feedback Mechanisms
Learning through trial and error
, Control tasks and game playing
Note: This note covers the topic of Basics of Machine
Learning Algorithms with a focus on Unsupervised
Learning, Key Factors enabling Machine Learning, Real-life
applications, Types of Machine Learning Paradigms with
details on Supervised Learning, Unsupervised Learning,
and Reinforcement Learning.
Types of Machine Learning Paradigms
Supervised Learning and Labeled Data
Involves a target/outcome variable (or dependent
variable) which is to be predicted from a given set of
predictors (independent variables)
Training dataset contains examples of input-output pairs
Unsupervised Learning and Pattern Recognition
Does not involve a target/outcome variable to be
predicted
Aims to model the underlying structure or distribution in
the data
Mainly used for clustering and association
Reinforcement Learning and Feedback Mechanisms
An agent learns to behave in an environment, by
performing certain actions and observing the
results/rewards
The goal Is to learn a series of actions that maximizes the
final reward
