Learn more about artificialintelligence with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
3 Categories of Machine Learning
- Supervised learning: It has a set of labelled data to train the model on. In a way, it means supervising a machine by providing a ton of information about a particular case and giving it the case outcome.
- Unsupervised learning: Unsupervised learning relies on giving the machine or algorithm unlabeled data and allowing it to identify the pattern on its own.
- Reinforcement learning: It depends on ML algorithms with a set of rules and let it learn on its own on how to achieve the goals.
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Machine Learning as a part of a greater whole
Machine learning relies on defining behavioural rules by examining a set of data to find patterns. Since the main objective of ML is to enable machines to learn by themselves, given a set of data, it is merely a technique for realising AI.
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To wrap it up...
- AI refers to machines exhibiting human-like intelligence. There are several techniques for that, machine learning being one of the most prominent ones.
- AI’s ultimate goal is to develop a smart system to simulate human thinking and intelligence, while ML allow...
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Narrow AI & General AI
Very broadly, AI can be divided into two: narrow AI and general AI.
1. Narrow AI systems handle singular or limited tasks. Also referred to as weak AI sometime...
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Machine Learning Explained
Machine Learning is the process of letting your machine use the data to learn the relationship between predictor variables and the target variable. It is one of the first steps toward becoming a data scientist.
There are two kinds of Machine Learning: supervised, and unsupervised learning....
Machine Learning as a part of a greater whole
Machine learning relies on defining behavioural rules by examining a set of data to find patterns. Since the main objective of ML is to enable machines to learn by themselves, given a set of data, it is merely a technique for realising AI.
Transfer learning concept
The biggest problem, thoug h, is that models like this one are performed only on a single task. Future tasks require a new set of data points as well as equal or more amount of resources.
Transfer learning is an approach in deep learning (and machine learning) where knowledge is ...
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