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Last Updated: 02 July 2021

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General | Latest Info

Are you thinking of Chappie, Terminator, and Lucy? Sentient, self - aware robots are closer to becoming reality than you think. Developing computer systems that equal or exceed human intelligence is the crux of Artificial Intelligence. Artificial Intelligence is a study of computer science focusing on developing software or machines that exhibit human intelligence. Simple enough definition, right? Obviously, there is a lot more to it. Ai is broad topic ranging from simple calculators to self - steering technology to something that might radically change in the future. Primary goals of AI include deduction and reasoning, knowledge representation, planning, Natural Language processing, learning, perception, and ability to manipulate and move objects. Long - term goals of AI research include achieving Creativity, Social Intelligence, and General Intelligence. Ai has heavily influenced different sectors that we may not recognize. Ray Kurzweil says many thousands of AI applications are deeply embed in the infrastructure of every industry. John McCarthy, one of the founders of AI, once said that as soon as it work, no one call it AI anymore. While there are various forms of AI as its broad concept, we can divide it into the following three categories based on AI capabilities: weak AI, which is also referred to as narrow AI, focuses on one task. There is no self - awareness or genuine intelligence in case of weak AI. Ios Siri is a good example of weak AI combining several weak AI techniques to function. It can do a lot of things for the user,ss and youll see how narrow it exactly is when you try having conversations with a virtual assistant. Strong AI, which is also referred to as True AI, is a computer that is as smart as the human brain. This sort of AI will be able to perform all tasks that humans could do. There is a lot of research going on in this field, but we still have to do. It You should be imagining Matrix or I, Robot here. Artificial Superintelligence is going to blow your mind if strong AI impresses you. Nick Bostrom, leading AI thinker, defines it as intellect that is much smarter than best human brain in practically every field, including scientific creativity, general wisdom and social skills. Artificial Superintelligence is the reason why many prominent scientists and technologists, including Stephen Hawking and Elon Musk, have raised concerns about the possibility of human extinction. The first thing you need to do is learn a Programming Language. Though there are lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning. Codeacademy learns Python the hard way coursera Python Introduction to Computer Science BOT is the most basic example of weak AI that can do automated tasks on your behalf. Chatbots were one of the first automated programs to be called Bots. You need AI and ML for your chatbots.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Major Machine Learning Algorithms:

Machine Learning is coming into its own, with growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. Ml provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. The major reason for this is that ML is just plain tricky. This Machine Learning tutorial introduces the basics of ML theory, laying down common themes and concepts, making it easy to follow logic and get comfortable with Machine Learning basics.


What Is Artificial Intelligence?

Algorithms in each category, in essence, perform the same task of predicting outputs giving unknown inputs, However, here data is the key driver when it comes to picking the right algorithm. What follows is an outline of categories of Machine Learning Problems with a brief overview of the same: classification Regression Clustering heres table that effectively differentiates each of these categories of problems. Type Of Problems solved Using AI - Artificial Intelligence Algorithms - Edureka for each category of tasks, we can use specific Algorithms. In the below section youll understand how category of algorithms can be used as solutions to complex problems.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

II. Unsupervised Learning:

Unsupervised Learning refers to the use of artificial Intelligence algorithms to identify patterns in data sets containing data points that are neither classified nor labeled. Algorithms are thus allowed to classify, label and / or group data points contained within data sets without having any external guidance in performing that task. In other words, Unsupervised Learning allows a system to identify patterns within data sets on its own. In Unsupervised Learning, AI system will group unsorted information according to similarities and differences even though there are no categories provide. Unsupervised Learning algorithms can perform more complex processing tasks than supervised learning systems. Additionally, subjecting system to Unsupervised Learning is one way of testing AI. However, unsupervised learning can be more unpredictable than supervised learning model. While an Unsupervised Learning AI system might, for example, figure out on its own how to sort cats from dogs, it might also add unforeseen and undesired categories to deal with unusual breeds, creating clutter instead of order. Ai systems capable of Unsupervised Learning are often associated with generative learning models, although they may also use a retrieval - base approach. Chatbots, self - driving cars, facial recognition programs, expert systems and robots are among systems that may use either supervised or unsupervised learning approaches, or both.


Types of Machine Learning

Unsupervised Machine Learning is typically tasked with finding relationships within data. There are no training examples used in this process. Instead, system is give set data and tasked with finding patterns and correlations therein. A good example is identifying close - knit groups of friends in social network data. Machine Learning algorithms used to do this are very different from those used for supervised learning, and the topic merits its own post. However, for something to chew on in the meantime, take a look at clustering algorithms such as k - means, and also look into dimensionality reduction systems such as principle component analysis. Our prior post on big data discusses a number of these topics in more detail as well.


Unsupervised Learning

Computer is train with unlabeled data. Here there is no teacher at all,. Actually, computer might be able to teach you new things after it learns patterns in data, These algorithms are particularly useful in cases where human experts do know what to look for in data. They are a family of machine learning algorithms which are mainly used in pattern detection and descriptive modeling. However, there are no output categories or labels here based on which algorithm can try to model relationships. These algorithms try to use techniques on input data to mine for rules, detect patterns, and summarize and group data points which help in deriving meaningful insights and describe data better to users.


Reinforcement Learning

Branch of Machine Learning algorithms which produce so - call agents. The agent role is slightly different than the classic model. It to receive information from the environment and react to it by performing action. Information is fed to an agent in the form of numerical data, call state, which is stored and then used for choosing the right action. As a result, agent receives reward that can be either positive or negative. A Reward is feedback that can be used by an agent to update its parameters. Training of agents is a process of trial and error. It needs to find itself in various situations and get punished every time it takes wrong action in order to learn. The goal of optimisation can be set in many ways depending on Reinforcement Learning approach eg based on Value Function, Gradient Policy or Environment Model. There is a broad group of Reinforcement Learning applications. The majority of them are inventions, that are regularly mentioned as the most innovative accomplishments of AI.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

1. Linear Regression:

Advantages And Disadvantages

AdvantagesDisadvantages
Linear regression performs exceptionally well for linearly separable dataThe assumption of linearity between dependent and independent variables
Easier to implement, interpret and efficient to trainIt is often quite prone to noise and overfitting
It handles overfitting pretty well using dimensionally reduction techniques, regularization, and cross-validationLinear regression is quite sensitive to outliers
One more advantage is the extrapolation beyond a specific data setIt is prone to multicollinearity

It is a Supervised Learning algorithm whose goal is to predict continuous, numerical values based on give data input. From a geometrical perspective, each data sample is a point. Linear Regression tries to find parameters of Linear Function, so the distance between all points and line is as small as possible. The algorithm used for parameters update is called Gradient Descent. For example, if we have dataset consisting of apartment properties and their prices in some specific area, Linear Regression algorithm can be used to find a mathematical function which will try to estimate the value of different apartment, based on their attributes. Another example can be prediction of food supply size for grocery store, based on sales data. That way, businesses can decrease unnecessary food waste. Such mapping is achievable for any correlated input - output data pairs.


What is Synthetic Data?

Synthetic data has a wide variety of uses, as it can be applied to just about any machine learning task. Common use cases for synthetic data include self - driving vehicles, security, robotics, fraud protection, and healthcare. One of the initial use cases for synthetic data was self - driving cars, as synthetic data is used to create training data for cars in conditions where getting real, on - road training data is difficult or dangerous. Synthetic data is also useful for creation of data used to train image recognition systems, like surveillance systems, much more efficiently than manually collecting and labeling bunch of training data. Robotics systems can be slow to train and develop with traditional data collection and training methods. Synthetic data allows robotics companies to test and engineer robotics systems through simulations. Fraud protection systems can benefit from synthetic data, and new fraud detection methods can be trained and tested with data that is constantly new when synthetic data is used. In the healthcare field, synthetic data can be used to design health classifiers that are accurate, yet preserve people's privacy, as data wont be based on real people.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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