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### What is a Neural Network?

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.

### Classification Algorithms Explained in 30 Minutes - datamahadev.com

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.

### Artificial Intelligence Introduction

Free Coupon Discount - Artificial Intelligence Introduction, Introduction to AI, ML, Data Science, BI and Analytics for Non-Technicals, Leaders, Managers, freshers and Beginners Bestseller Created by Sudhanshu Saxena English [Auto] Students also bought Product Development & Systems Engineering Artificial Intelligence A-Z: Learn How To Build An AI Hands-On Robotics with Arduino, Build 13 robot projects Beginners Guide to AI (Artificial Intelligence) IoT#3: IoT (Internet of Things) Automation with ESP8266 Nanotechnology: Introduction, Essentials, and Opportunities Preview this Udemy Course GET COUPON CODE Description Section 1-L1: To learn the strategy of various skills of current and future world like Artificial Intelligence, Machine learning, Data Science, we are starting from understanding data. To expertise in Artificial Intelligence needs to be understood the basics of data. In this INTRODUCTION section, we will talk about What is the data? How does data divide into multiple parts? How do and where the data generate from?

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.

### Artificial Intelligence Introduction

Udemy Coupon - Artificial Intelligence Introduction, Introduction to AI, ML, Data Science, BI and Analytics for Non-Technicals, Leaders, Managers, freshers and Beginners HOT & NEW Created by Sudhanshu Saxena English [Auto-generated] Students also bought Artificial Intelligence A-Z: Learn How To Build An AI Artificial Intelligence: Reinforcement Learning in Python Artificial Intelligence & Machine Learning for Business Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs Artificial Intelligence 2018: Build the Most Powerful AI Preview this Course GET COUPON CODE Description Section 1-L1: To learn the strategy of various skills of current and future world like Artificial Intelligence, Machine learning, Data Science, we are starting from understanding data. To expertise in Artificial Intelligence needs to be understood the basics of data. In this INTRODUCTION section, we will talk about What is the data? How does data divide into multiple parts? How do and where the data generate from?

### 6 Disadvantages of Facial Recognition You Need to Be Aware of - Tech Business Guide

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.