Asked by: Jofre Opfermann
technology and computing artificial intelligence

What is machine learning in detail?

Last Updated: 6th June, 2020

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

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Keeping this in view, what is machine learning and its types?

Machine learning is sub-categorized to three types: Supervised Learning – Train Me! Unsupervised Learning – I am self sufficient in learning. Reinforcement Learning – My life My rules!

One may also ask, what is machine learning and why is it important? The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It's a science that's not new – but one that has gained fresh momentum.

Considering this, what is machine learning and how does it work?

Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

What is machine learning basics?

Machine Learning is a subset of AI where the machine is trained to learn from it's past experience. The past experience is developed through the data collected. Then it combines with algorithms such as Naïve Bayes, Support Vector Machine(SVM) to deliver the final results.

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What are the two types of machine learning?

Learning Problems. First, we will take a closer look at three main types of learning problems in machine learning: supervised, unsupervised, and reinforcement learning.

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What is machine learning example?

But what is machine learning? For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc. The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data.

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What language is best for machine learning?

Top 5 best Programming Languages for Artificial Intelligence
  1. Python. Python is considered to be in the first place in the list of all AI development languages due to the simplicity.
  2. R. R is one of the most effective language and environment for analyzing and manipulating the data for statistical purposes.
  3. Lisp.
  4. Prolog.
  5. Java.

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What are the advantages of machine learning?

One of the biggest advantages of machine learning algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.

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What are different types of unsupervised learning?

Some of the most common algorithms used in unsupervised learning include:
  • Clustering. hierarchical clustering, k-means.
  • Anomaly detection. Local Outlier Factor.
  • Neural Networks. Autoencoders. Deep Belief Nets.
  • Approaches for learning latent variable models such as. Expectation–maximization algorithm (EM) Method of moments.

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What are learning algorithms?

A learning algorithm is a method used to process data to extract patterns appropriate for application in a new situation. In particular, the goal is to adapt a system to a specific input-output transformation task.

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What are the basics for machine learning?

Machine Learning algorithms can be classified into:
  • Supervised Algorithms - Linear Regression, Logistic Regression, KNN classification, Support Vector Machine (SVM), Decision Trees, Random Forest, Naive Bayes' theorem.
  • Unsupervised Algorithms - K Means Clustering.

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What is normalization in machine learning?

Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values. For machine learning, every dataset does not require normalization.

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Why is machine learning?

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. Machine learning applications for everyday life.

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How does an AI work?

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines.

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How many types of learning are available in machine learning?

Broadly, there are 3 types of Machine Learning Algorithms
The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

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What is required for machine learning?

Machine Learning Algorithms
Having a firm understanding of algorithm theory and knowing how the algorithm works, you can also discriminate models such as SVMs. You will need to understand subjects such as gradient decent, convex optimization, quadratic programming, partial differential equations and alike.

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What is the most important part of machine learning?

Training is the most important part of Machine Learning. Choose your features and hyper parameters carefully. Machines don't take decisions, people do. Data cleaning is the most important part of Machine Learning.

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What are the objectives of machine learning?

The primary purpose of machine learning is to discover patterns in the user data and then make predictions based on these and intricate patterns for answering business questions and solving business problems. Machine learning helps in analysing the data as well as identifying trends.

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What are applications of machine learning?

Applications of Machine Learning
Companies in the financial sector are able to identify key insights in financial data as well as prevent any occurrences of financial fraud, with the help of machine learning technology. The technology is also used to identify opportunities for investments and trade.

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What is the objective of machine learning?

The purpose of machine learning is to discover patterns in your data and then make predictions based on often complex patterns to answer business questions, detect and analyse trends and help solve problems.

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Why machine learning is the future?

Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. As humans become more addicted to machines, we're witnesses to a new revolution that's taking over the world, and that is going to be the future of Machine Learning.

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Why is data important for machine learning?

Another most important role of training data for machine learning is classifying the data sets into various categorized which is very much important for supervised machine learning. It helps them to recognize and classify the similar objects in future, thus training data is very important for such classification.