Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to learn and improve automatically from experience without being explicitly programmed. Machine learning focuses on computer programs development that can access data and use it to learn for them.
Machine learning is a study of computer algorithms that can learn from experience or historical data and improve automatically through experience.
It’s a subset of artificial intelligence (AI), which focuses on using statistical techniques to build intelligent computer systems to learn from databases available to it. Machine learning is known as part of artificial intelligence.
Machine learning helps to create better technologies to power today’s approaches such as medical diagnosis, image processing, prediction, classification, learning association, regression etc.
The topic you need to know in Machine Learning
Artificial Intelligence (AI)
Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. AI refers to an extended idea where machines are able to learn, adapt through experience and execute tasks smartly.
AI aims to create intelligent machines that simulate human functions such as knowledge, reasoning, problem-solving, perception, learning, planning, and ability to manipulate and move objects.
Machine Learning (ML)
Machine learning comes under AI that provides a system with the intelligence to learn automatically and develop from experience without being explicitly programmed.
The prime intention is to let the computers to acquire automatically without human intrusion or assistance and adjust actions respectively.
Supervised learning
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labelled training data consisting of a set of training examples.
Unsupervised learning
Unsupervised learning is a machine learning technique that draws interface from a database consisting of input data without labelled responses. This learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, the machine is forced to build a compact internal representation of its world.
Artificial Neural Network (ANN)
An artificial neural network (ANN) refers to a biologically inspired sub-field of artificial intelligence modelled after the brain. It solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.
Deep Neural Network (DNN) or Deep Learning
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep learning allows the network to extract different features until it can recognize what it is looking for.
Linear regression
Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It’s used to predict values within a continuous range, rather than trying to classify them into categories.
Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc.
Logistic regression
Logistic regression is a supervised learning classification algorithm that is applied to predict the possibility of a target variable. The nature of the target or dependent variable is dichotomous, which means there would be only two possible classes.
- Binary
- Multi-class
Ensemble learning
An ensemble method is a machine learning technique that combines several base models to produce one optimal predictive model. They are meta-algorithms that combine several machine learning techniques into one predictive model to decrease variance (bagging), bias (boosting) or improve prediction (stacking).
Overfitting & Underfitting
Good performance on the training data, poor generalization to other data is known as Overfitting. And poor performance on the training data and poor generalization to other data called underfitting.
Regularization
In mathematics, statistics, finance, computer science, particularly in machine learning and inverse problems, regularization is the process of adding information to solve an ill-posed problem or to prevent overfitting. Regularization applies to objective functions in ill-posed optimization problems.
Cross-Validation
Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross–validation to detect overfitting, i.e., failing to generalize a pattern.
These are the different types of cross-validation techniques:
- Holdout method
- K-fold (most popular)
- Leave-P-out
EndNote
IT world is changing rapidly; the demand for Machine Learning Engineer is keeping on a hike. Machine Learning engineers are building these systems. If this is your future, then there’s no time like the present to start mastering the Machine Learning Training in Noida and developing the mind-set you’re going to need to succeed. For the best learning institute, you can go with Aptron Machine Learning Institute in Noida. Apton is one of the best learning platforms for Machine Learning Course in Noida. All the best for your learning journey and keep learning.
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