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Machine learning is a field of computer science where various statistical techniques are used to let the computer learn on its own through analysis of data without actual programming. It is the area of Artificial Intelligence where machine learning is often used and focus on development of computer applications accessing the data and using that data for learning without any human intervention. Here the process of learning starts through observation of the data and the aim is to let computer automatically start learning without the assistance of humans.
In case of machine learning it is the algorithms that are used for receiving the data as an input and statistical techniques are applied to anticipate the output while ensuring an update in output with the change in data. The process used in machine learning is similar to the process of data mining and predictive models as all these processes focus on searching the data for pattern and adjusting the actions of the program accordingly. The use of such models is to help business organizations to take effective decisions through details analysis of the huge amount of data. Machine learning is useful in several areas and fields like heath care, identification of fraud, financial services, customized recommendations and many more.
There are key areas in the process of machine learning, like:
Machine Learning can be dividing into three types:
Supervised Learning: Labels are present, training of data points, classified into anomalous and non-anomalous. This is the easiest type of machine learning popular for its easy implementation through data that is in the form of labels is fed using a learning algorithm to let the algorithm predict the label and acquire the feedback for checking the correctness of the predicted result. Once the system gets trained properly, it will become possible for the supervised learning algorithm to identify new data and identify the label on the basis of the known data. This type of machine learning homework help can be applied for checking the popularity of an advertisement and classification of spam in the latest email system. Predictive models are developed using two types of methods namely classification techniques and regression technique.
Classification: Under classification technique the direct responses are predicted and used for medical imaging credit scoring, speech recognition, etc. The algorithms used for classification include Super vector Machine (SVM), K-nearest neighbor, Neural networks, Logical regression, Bagged decision tree.
Regression: Under regression technique, the focus in on generating continuous responses like change in temperature, power fluctuations with demand. The important regression algorithm techniques are linear model, non-linear model, regularization, step-wise regression, neural network, bagged decision trees, adaptive neuro-fuzzy learning.
Unsupervised Learning: Confidence interval is set, generalized ESD test is implemented, Data points are detected. Unsupervised learning is completely opposite of supervised learning where labels do not exist. Here the data is fed, and tools are given to learn about the attributes of the data. This will learn the way of grouping, make clusters and organizing the data like humans. A key point to be noted here is that the majority of data under this unsupervised learning are not labeled. Huge chunks of data is processed through the intelligence algorithm and organized in a proper manner helping the business organizations and industries to earn profits through productivity enhancement.
The method is best applied in relation to buying habits of consumers that are save in the database. These habits are used by unsupervised learning algorithms to make clusters of consumers based on their purchase behavior helping the companies to identify appropriate marking strategies to target the segmented group of people and earn huge profits in future. The techniques used to explain the data under unsupervised learning includes:
Clustering: Here the focus is on analysis of the exploratory data to identify hidden patterns or data groups. The key applications of this technique are market research, object recognition, etc.
Dimensionality reduction: Machine learning algorithms are used to filter out the noise produced in the incoming data. The most common algorithms used are K-means clustering, T-Distributed Stochastic Neighbor Embedding, principal Component Analysis and Association rule.
Reinforcement Learning: Collaborative filtering, Netflix recommendation. This method comprises of the trait and hit method where the learning model interact with the environment to deliver right actions and identify the best results. Once the model if trained it becomes possible to predict the new data where the machines can detect ideal behavior in a specific situation to improve the performance. The reinforcement learning is applied to video games and certain applications on Google like AlphaZero and AlphaGo. The key reinforcement machine learning includes:
The Machine Learning Process
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Key Applications of Machine Learning:
|Machine Learning||Deep Learning||Bioinformatics|
|Video Games||Automatic Speech Recognition||Genomics|
|Computer Vision||Mobile Advertising||System Biology|
|Language Processing||Natural Language Processing||Proteomics|
|Face Detection||Bioinformatics||Text mining|
|Image Recognition||CRM Technologies||Microarrays|
|Pattern Recognition||Image Recognition||Neural Networks|
|Bayesian Network||Toxicology||DNA sequence analysis|
|Data Mining||Colorize Images||Protein sequence analysis|
|Cognitive Services||Automatic Game Playing||Predicting functional structures|
|Predictive Learning||Object Classification in Photographs||Drug Screening|
|Reinforcement Learning||Automatic Machine Translation||Metabolic and regulatory networks|
|Classification Trees||Optimization Methods|
|Optimization methods||Naive Bayes Theorem|
|K Means clustering||Decision Tree|
|Natural Language Processing||Hidden Markov Models|
|Random Forrests||Kernel PCA|
|Gradient Boosting||Kernel Ridge Regression|
|Factor Analysis Bias and Variance||Probabilistic Modeling|
|Deep Learning||Artificial Neural Networks|
|Clustering Algorithms||Predictive Modeling|
|Hypothesis Space||Hierarchical Clustering|
|Instance based Learning||Graphical Models and Factor Graphs|
|Ensemble Learning||WEKA implementations|
Machine Learning Project
|Unsupervised Learning||Machine Learning Problems|
|Machine Learning Techniques|
|Supervised Learning||Reinforcement Learning|