Machine learning algorithms are pieces of code that help people explore, analyze, and find meaning in complex data sets. Each algorithm is a finite set of unambiguous step-by-step instructions that a machine can follow to achieve a certain goal. In a machine learning model, the goal is to establish or discover patterns that people can use to make predictions or categorize information.
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Machine learning algorithms use parameters that are based on training data—a subset of data that represents the larger set. As the training data expands to represent the world more realistically, the algorithm calculates more accurate results.
Different algorithms analyze data in different ways. They’re often grouped by the machine learning techniques that they’re used for: supervised learning, unsupervised learning, and reinforcement learning. The most commonly used algorithms use regression and classification to predict target categories, find unusual data points, predict values, and discover similarities. Do check the Bootcamp coding courses at Edureify available for students as well as professionals.
Machine learning techniques
As you learn more about machine learning algorithms, you’ll find that they typically autumn within one of three machine learning techniques:
Supervised learning
In supervised learning, algorithms make predictions based on a set of labeled examples that you provide. This technique is useful when you know what the outcome should look like.
For example, you provide a dataset that includes city populations by year for the past 100 years, and you want to know what the population of a specific city will be four years from now. The outcome uses labels that already exist in the data set: population, city, and year.
Unsupervised learning
In unsupervised learning, the data points aren’t labeled—the algorithm labels them for you by organizing the data or describing its structure. This technique is useful when you don’t know what the outcome should look like.
For example, you provide customer data, and you want to create segments of customers who like similar products. The data that you’re providing isn’t labeled, and the labels in the outcome are generated based on the similarities that were discovered between data points.
Reinforcement learning
Reinforcement learning uses algorithms that learn from outcomes and decide which action to take next. After each action, the algorithm receives feedback that helps it determine whether the choice it made was correct, neutral, or incorrect. It’s a good technique to use for automated systems that have to make a lot of small decisions without human guidance.
For example, you’re designing an autonomous car, and you want to ensure that it’s obeying the law and keeping people safe. As the car gains experience and a history of reinforcement, it learns how to stay in its lane, go the speed limit, and brake for pedestrians.
What you can do with machine learning algorithms
Machine learning algorithms help you answer questions that are too complex to answer through manual analysis. There are many different machine learning algorithm types, but use cases for machine learning algorithms typically autumn into one of these categories. Check the Bootcamp coding courses and build a great future in software development courses now.
Predict a target category
Two-class (binary) classification algorithms divide the data into two categories. They’re useful for questions that have only two possible answers that are mutually exclusive, including yes/no questions. For example:
Will this tire fail in the next 1,000 miles: yes or no?
Which brings in more referrals: a USD 10 credit or a 15% discount?
Multiclass (multinomial) classification algorithms divide the data into three or more categories. They’re useful for questions that have three or more possible answers that are mutually exclusive. For example:
In which month do the majority of travelers purchase airline tickets?
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Find unusual data points
Anomaly detection algorithms identify data points that autumn outside of the defined parameters for what’s “normal.” For example, you would use anomaly detection algorithms to answer questions like:
- Where are the defective parts in this batch?
- Which credit card purchases might be fraudulent?
Predict values
Regression algorithms predict the value of a new data point based on historical data. They help you answer questions like:
- How much will the average two-bedroom home cost in my city next year?
- How many patients will come through the clinic on Tuesday?
See how values change over time
Time series algorithms show how a given value changes over time. With time series analysis and time series forecasting, data is collected at regular intervals over time and used to make predictions and identify trends, seasonality, cyclicity, and irregularity. Time series algorithms are used to answer questions like:
- Is the price of a given stock likely to rise or autumn in the coming year?
- What will my expenses be next year?
- Discover similarities
- Clustering algorithms divide the data into multiple groups by determining the level of similarity between data points. Clustering algorithms work well for questions like:
- Which viewers like the same types of movies?
- Which printer models fail in the same way?
Classification
Classification algorithms use predictive calculations to assign data to pre-set categories. Classification algorithms are trained on input data, and used to answer questions like:
- Is this email spam?
So this was all from the insights of machine learning algorithms. Stay tuned to Eduriefy for more interesting concepts related to coding and programming. Download the Eduriefy app now for more updates.
Frequently Asked Questions (FAQs)
Question:- What is machine learning?
Answer:- Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. IBM has a rich history with machine learning.
Question:- What are the three types of machine learning?
Answer:- There are three machine learning types: supervised, unsupervised, and reinforcement learning.
Question:- What is AI vs machine learning?
Answer:- Artificial intelligence is a technology that enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. The goal of AI is to make a smart computer system like humans to solve complex problems.
Question:- Who is the father of machine learning?
Answer:- Geoffrey Everest Hinton CC FRS
Geoffrey Everest Hinton CC FRS FRSC (born 6 December 1947) is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks.
Question:- What are the most important machine learning algorithms?
Answer:- To recap, we have covered some of the most important machine learning algorithms for data science: 5 supervised learning techniques- Linear Regression, Logistic Regression, CART, Naïve Bayes, and KNN. 3 unsupervised learning techniques- Apriori, K-means, PCA.
