"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
The study of algorithms and systems that can learn, reason, and act intelligently.
Introduction to Artificial Intelligence: Overview of what Artificial Intelligence is, its branches, history, and applications.
Mathematics for Machine Learning: Linear Algebra, Calculus, Probability, and Statistics required for Machine Learning.
Python Programming: Basics of programming with Python, including data types, loops, functions, and control flow.
Data Preprocessing: Techniques and methods for preparing raw data for analysis, including cleaning, normalization, and scaling.
Supervised Learning: Learning from labelled data, including classification, regression, and decision trees.
Unsupervised Learning: Learning from unlabelled data, including clustering, dimensionality reduction, and anomaly detection.
Neural Networks: The fundamentals of artificial neural networks as a computing system and their various types.
Deep Learning: Neural networks with multiple layers that can learn and make predictions on complex data.
Natural Language Processing: The use of machines to manipulate and analyze natural language texts, including sentiment analysis and text classification.
Computer Vision: The use of machines to understand and interpret visual data, including image recognition and object detection.
Reinforcement Learning: A type of machine learning where an agent learns how to make decisions based on rewards and punishments.
Ethics and Bias in AI: The societal implications of AI and how to mitigate bias and ethical concerns.
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"Recently, generative artificial neural networks have been able to surpass results of many previous approaches."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"where it is too costly to develop algorithms to perform the needed tasks."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"ML is known in its application across business problems under the name predictive analytics."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."
"the problems are solved by helping machines 'discover' their 'own' algorithms without needing to be explicitly told what to do by any human-developed algorithms."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"development of algorithms by human programmers would be cost-prohibitive"
"generative artificial neural networks have been able to surpass results of many previous approaches."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"Machine-learning approaches have been applied to...medicine."
"helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"where it is too costly to develop algorithms to perform the needed tasks."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."