If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Think back to the first time you heard the phrase "neural networks" or "neural nets" -- perhaps it's right now -- and try to remember what your first impression was. As an Applied Math and Economics major with a newfound interest in data science and machine learning, I remember thinking that whatever neural networks are, they must be extremely important, really cool, and very complicated. I also remember thinking that a true understanding of neural networks must be on the other side of a thick wall of prerequisite knowledge including neuroscience and graduate mathematics. Through taking a machine learning course with Professor Samuel Watson at Brown, I have learned that three of the previous four statements are true in most cases -- neural nets are extremely important, really cool, and they can be very complicated depending on the architecture of the model. But most importantly, I learned that understanding neural networks requires minimal prerequisite knowledge as long as the information is presented in a logical and digestable way.
In the Machine Learning terminology, the process of Classification can be defined as a supervised learning algorithm that aims at categorizing a set of data into different classes. In other words, if we think of a dataset as a set of data instances, and each data instance as a set of features, then Classification is the process of predicting the particular class that that individual data instance might belong to, based on its features. Unlike regression where the target variable (i.e., the predicted value) belongs to a continuous distribution, in case of classification, the target variable is discrete. It can only be one of the various target classes in a given problem. For example, let's say you are working on a cat-dog-classifier model that predicts whether the animal in a given image is a cat or a dog.
Artificial intelligence refers to the simulation of human intelligence in a machine that is programmed to think like humans. The idea of artificial intelligence initially begins by the computer scientist from 1943 to 1956. A model proposed by Alan Turing which is known as the Turing test. A Turing test is an algorithm that computes the data similar to human nature and behavior for proper response. Since this Turing test proposed by Alan Turing which plays one of the most important roles in the development of artificial intelligence, So Alan Turing is known as the father of artificial intelligence.
Linear regression is a simple Supervised Learning algorithm that is used to predict the value of a dependent variable(y) for a given value of the independent variable(x) by effectively modelling a linear relationship(of the form: y mx c) between the input(x) and output(y) variables using the given dataset. In this article we will be discussing the advantages and disadvantages of linear regression. Linear Regression is a very simple algorithm that can be implemented very easily to give satisfactory results.Furthermore, these models can be trained easily and efficiently even on systems with relatively low computational power when compared to other complex algorithms.Linear regression has a considerably lower time complexity when compared to some of the other machine learning algorithms.The mathematical equations of Linear regression are also fairly easy to understand and interpret.Hence Linear regression is very easy to master. Linear regression fits linearly seperable datasets almost perfectly and is often used to find the nature of the relationship between variables. Overfitting is a situation that arises when a machine learning model fits a dataset very closely and hence captures the noisy data as well.This negatively impacts the performance of model and reduces its accuracy on the test set.
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Logistic Regression is one of the supervised Machine Learning algorithms used for classification i.e. to predict discrete valued outcome. It is a statistical approach that is used to predict the outcome of a dependent variable based on observations given in the training set. Logistic Regression is one of the simplest machine learning algorithms and is easy to implement yet provides great training efficiency in some cases. Also due to these reasons, training a model with this algorithm doesn't require high computation power. The predicted parameters (trained weights) give inference about the importance of each feature.
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Facial recognition technology is generating lots of excitement. Yet, it is also very controversial around issues like privacy, reliability, possible bias and lack of regulation. As a result, businesses must beware of the potential disadvantages of facial recognition. There is much criticism about the use of facial recognition technology. Thus, interest groups tend to be very opinionated about it.
Every coin has two faces, each face has its own property and features. It's time to uncover the faces of ML. A very powerful tool that holds the potential to revolutionize the way things work. Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, for an e-commerce website like Amazon, it serves to understand the browsing behaviors and purchase histories of its users to help cater to the right products, deals, and reminders relevant to them.
Learn the complete quant trading workflow and use machine learning algortihms to develop good trading strategies. The course is designed to fully immerse you into the complete quantitative trading workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you from zero to hero. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes.