Machine learning (ML) is the process of training computers, using math and statistical processes, to find and recognize patterns in data.
Machine Learning
Machine learning (ML) is the process of training computers, using math and statistical processes, to find and recognize patterns in data. After patterns are found, ML generates and updates training models to make increasingly accurate predictions and inferences about future outcomes based on historical and new data. For example, ML could help determine the likelihood of a customer purchasing a particular product based on previous purchases by the user or the product's past sales history.
Building ML applications is an iterative process that involves a sequence of steps. To build an ML application, follow these general steps:
- Formulate a problem
- Prepare your data
- Train the model
- Test the model
- Deploy your model
Note :-
“There’s nothing new to AI per se. It was deployed in the 1950s. But now, with cloud computing and the elasticity it provides, we can suddenly take massive amounts of data, throw it at the neural network, and actually get insights out of what is statistically difficult to engineer.”
What are the key terms in machine learning?
- Modle
- Traing
- Testing
- Deployment
What is artificial intelligence?
Artificial intelligence (AI) is any system that is able to ingest human-level knowledge to automate and accelerate tasks performable by humans through natural intelligence. AI has two categories: narrow, where an AI imitates human intelligence in a single context, and general, where an AI learns and behaves with intelligence across multiple contexts.
Examples of AI include:
- Intelligent search in Amazon Kendra
- Document analysis in Amazon Comprehend
- Data and text extraction in Amazon Textract
- Business metrics analysis in Amazon Lookout for Metrics and Amazon Forecast
Note
A system able to ingest human-level knowledge and use that information to automate and accelerate tasks that were previously performable only by humans.
What is the difference between ML and AI?
Artificial intelligence ingests data, such as human-level knowledge, and imitates natural intelligence. Machine learning is a subset of AI, where data and algorithms continuously improve the training model to help achieve higher-quality output predictions. Deep learning is a subset of machine learning. It is an approach to realizing ML that relies on a layered architecture, mimicking the human brain to identify data patterns and train the model.
What are the requirements to implement AI?
The core components of AI are domain knowledge to structure and frame the problem correctly, high-quality input data to train the model, and methods to detect patterns and make predictions.
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What is the difference between machine learning and classical programming?
Machine learning involves teaching a computer to recognize patterns by example, rather than programming it with specific rules. These patterns can be found in the data. In other words, ML is about creating algorithms (or a set of rules) that learn from complex functions (patterns) from data and make predictions on it (a form of “narrow AI”). ML learns from data and can be reused for unseen, future, or new data without rewriting code. Put another way, with ML, you start with a problem, identify data associated with that problem, use an algorithm to then model that problem, and generate output.
- Supervised learning
- Unsupervised learning
- Reinforcement learning
What are some potential problems with machine learning?
By the end of this lesson, you will be able to:
-Describe the differences between simple and complex models.
-Understand unexplainability and uncertainty problems with ML models.
What are some potential problems with machine learning models?
Machine learning modeling can be problematic for learning algorithms due to the ingestion of poor quality data. For example, the data may not include enough samples to represent a sufficiently broad scope of relevant variables.
What are simple and complex models?
Simple and complex ML models differ when balancing a model's accuracy (number of correctly predicted data points) and a model's explainability (how much of the ML system can be explained in "human terms"). The output of a simple ML model may be explainable and produce faster results, but the results may be inaccurate. The output of a complex ML model may be accurate, but the results may be difficult to communicate.
- simpel model
- complex model
What is unexplainabilty?
Unexplainability represents how much of the reasoning behind an ML model's decision cannot be effectively described in human terms. There are potentially legal, professional, ethical, and regulatory conditions where the tolerance for unexplainability may vary from case to case.
What is uncertainty?
Uncertainty describes an imperfect outcome. In the context of machine learning, uncertainty arises from using models. These models attempt to fit a training data, which may have imperfect data. The "best" data may also be unknowable.
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