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[ML Tutorials #1] Grasping the concept of machine learning in just 90 seconds of reading 2610

Tima EL, 2 y.
1 min read.

[ML Tutorials #1] Grasping the concept of machine learning in just 90 seconds of reading

Tima EL, 2 d.
min read.
Machine learning is a branch of the artificial intelligence domain that encompasses various methods relying on learning from data to solve problems such as prediction, classification, dimensionality reduction, etc. Learning from the data means that machine learning systems can analyze patterns, extract insights, and make informed decisions without being explicitly programmed for a particular task. Instead of adhering to predetermined rules, machine learning methods adapt and improve their performance over time. The process involves training models, validating their accuracy, and testing their generalization to new, unseen data. Intuitively, we can envision the machine learning model as a student in a classroom. The teacher imparts knowledge to the student during what we refer to as the training step for the machine learning model. After the session, the student undergoes a quiz to solidify the concepts, representing the validation step for the machine learning model. Finally, the student takes a comprehensive final exam to test their understanding of the entire course. All of these stages occur gradually over what is termed as epochs in the context of a machine learning model. In this analogy, each epoch corresponds to a complete cycle of the training, validation, and testing phases. It's like the student attending multiple class sessions, quizzes, and exams to reinforce and assess their knowledge. With each successive epoch, the machine learning model refines its understanding of the data, enhancing its ability to make accurate predictions or classifications in real-world applications. Just as a student becomes more adept through repeated study sessions, the machine learning model becomes increasingly proficient with each pass through the data.

Grasping the concept of machine learning in just 90 seconds of reading

Tima EL, 2 y.
min read.
Machine learning is a branch of the artificial intelligence domain that encompasses various methods relying on learning from data to solve problems such as prediction, classification, dimensionality reduction, etc. Learning from the data means that machine learning systems can analyze patterns, extract insights, and make informed decisions without being explicitly programmed for a particular task. Instead of adhering to predetermined rules, machine learning methods adapt and improve their performance over time. The process involves training models, validating their accuracy, and testing their generalization to new, unseen data. Intuitively, we can envision the machine learning model as a student in a classroom. The teacher imparts knowledge to the student during what we refer to as the training step for the machine learning model. After the session, the student undergoes a quiz to solidify the concepts, representing the validation step for the machine learning model. Finally, the student takes a comprehensive final exam to test their understanding of the entire course. All of these stages occur gradually over what is termed as epochs in the context of a machine learning model. In this analogy, each epoch corresponds to a complete cycle of the training, validation, and testing phases. It's like the student attending multiple class sessions, quizzes, and exams to reinforce and assess their knowledge. With each successive epoch, the machine learning model refines its understanding of the data, enhancing its ability to make accurate predictions or classifications in real-world applications. Just as a student becomes more adept through repeated study sessions, the machine learning model becomes increasingly proficient with each pass through the data.