Artificial Intelligence (AI) is a field of computer science that focuses on creating intelligent machines that function like humans. Machine Learning (ML) is a subset of AI that uses statistical techniques that enable computers to learn from data without human intervention. There are three categories of Machine Learning methods:
- Supervised Machine Learning: The computer trains itself on a labeled data set with this method. Supervised ML requires less training data and makes training easier since results can be compared to labeled results. The drawback is the cost of preparing and marking data and the risk of creating a model that’s too similar and biased to the training data. When this happens, variations in data aren’t interpreted accurately.
- Unsupervised Machine Learning: with this method, the computer consumes large, unlabeled data sets and extracts meaningful flags using algorithms to label, sort, and classify data in real time without human intervention. Unsupervised ML focuses more on identifying patterns and relationships than automating decisions and predictions.
- Semi-Supervised Learning: this method falls neatly between Supervised and Unsupervised ML. Semi-Supervised ML uses a small labeled dataset to classify and feature extractions from a larger, unlabeled dataset.
How does Machine Learning work?
Data scientists use four basic steps to build machine-learning applications:
1. Prepare a Data Training Set
Preparing training data means randomizing, de-duplicating, and checking for inaccuracies or biases. Preparing training data means randomizing, de-duplicating, and checking for inaccuracies or biases. Preparing training data means randomizing, de-duplicating, and checking for inaccuracies or biases. Training data is labeled data that represents the data the ML model will interpret and solve. It’s tagged with features and classifications that the ML model will identify. Sometimes training data is unlabeled, and the model has to figure out how to extract features and assign categories. Training data should also be divided into a training subset to train the application and an evaluation subset to test and refine it.
2. Choose an Algorithm
An algorithm is a set of processing steps, and the type of algorithm implemented is determined by the type and amount of training data and the type of problem being solved. There are several common types of ML algorithms used with labeled training data. Instance-based algorithms estimate the likelihood that a data point is a member of a certain group based on its proximity to other data points. Instance-based algorithms assess the probability that a data point is a member of a certain group based on its proximity to other data points. Instance-based algorithms estimate the likelihood that a data point is a member of a certain group based on its proximity to other data points. Regression algorithms understand relationships in data by predicting a dependent variable’s value based on the value of an independent variable. Decision trees make recommendations based on a set of decision rules.
Several common types of ML algorithms are also used with unlabeled training data. Clustering algorithms identify groups of similar records and label them with their appropriate group. Association algorithms identify patterns and relationships to identify association rules or ‘if-then’ relationships. Neural networks define layered networks of calculations that consume, interpret, and deliver conclusions about data. Each layer refines the results of the previous layer in a process known as deep Learning.
3. Train the Algorithm
Training an algorithm is a multi-step process. Variables run through the algorithm, and output is compared with the expected results. Weights and biases are adjusted to get more accurate results, and then variables are rerun until the work matches the desired results more times than not. The trained, precise algorithm is now the Machine Learning model.
4. Use and Improve the Model
Lastly, using new data with the ML model and improving its accuracy and effectiveness is important. The source of the new data depends on the nature of the problem being solved.
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