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An In-depth Analysis of Game Theory for AI

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An In-depth Analysis of Game Theory for AI

Game Theory is a branch of mathematics used to model the strategic interaction between different players in a context with predefined rules and outcomes. With the rapid rise of AI, along with the extensive time and research we are devoting to it, Game Theory is experiencing steady growth. If you are also interested in AI and want to be well-versed with it, then, opt for the Best Artificial Intelligence Training Institute in Gurgaon now!

Games have been one of the main areas of focus in artificial intelligence research. They often have simple rules that are easy to understand and train for. It is clear when one party wins, and frankly, it is fun watching a robot beat a human at chess. This trend of AI research being directed towards games is not at all an accident. Researchers know that the underlying principles of many tasks lie in understanding and mastering game theory. Both AI and game theory seek to find out how participants will react in different situations, figuring out the best response to situations, optimizing auction prices and finding market-clearing prices.

Some Useful Terms in Game Theory

  • Game: Like games in popular understanding, it can be any setting where players take actions and its outcome will depend on them.
  • Player: A strategic decision-maker within a game.
  • Strategy: A complete plan of actions a player will take, given the set of circumstances that might arise within the game.
  • Payoff: The gain a player receives from arriving at a particular outcome of a game.
  • Equilibrium: The point in a game where both players have made their decisions and an outcome is reached.
  • Dominant Strategy: When one strategy is better than another strategy for one player, regardless of the opponent’s play, the better strategy is known as a dominant strategy.
  • Agent: Agent is equivalent to a player.
  • Reward: A payoff of a game can also be termed as a reward.
  • State: All the information necessary to describe the situation an agent is in.
  • Action: Equivalent of a move in a game.
  • Policy: Similar to a strategy. It defines the action an agent will make when in particular states
  • Environment: Everything the agent interacts with during learning.

Different Types of Games in Game Theory

In the game theory, different types of games help in the analysis of different types of problems. The different types of games are formed based on number of players involved in a game, symmetry of the game, and cooperation among players.

Cooperative and Non-Cooperative Games

Cooperative games are the ones in which the players are convinced to adopt a particular strategy through negotiations and agreements between them.

Non-Cooperative games refer to the games in which the players decide on their strategy to maximize their profit. Non-cooperative games provide accurate results. This is because in non-cooperative games, a very deep analysis of a problem takes place.

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Normal Form and Extensive Form Games

Normal form games refer to the description of the game in the form of a matrix. In other words, when the payoff and strategies of a game are represented in a tabular form, it is termed as normal form games.

Extensive form games are the ones in which the description of the game is done in the form of a decision tree. Extensive form games help in the representation of events that can occur by chance.

Simultaneous Move Games and Sequential Move Games

Simultaneous games are the ones in which the move of two players (the strategy adopted by two players) is simultaneous. In a simultaneous move, players do not know the move of other players.

Sequential games are the ones in which the players do not have a deep knowledge about the strategies of other players.

Constant Sum, Zero Sum, and Non-Zero Sum Games

Constant sum games are the ones in which the sum of outcome of all the players remains constant even if the outcomes are different. 

Zero sum games are the ones in which the gain of one player is always equal to the loss of the other player. 

Non-zero sum games can be transformed to zero sum game by adding one dummy player. The losses of the dummy player are overridden by the net earnings of players. Examples of zero sum games are chess and gambling. In these games, the gain of one player results in the loss of the other player.

Symmetric and Asymmetric Games

Symmetric games are the ones where the strategies adopted by all the players are the same. Symmetry can exist in short-term games only because in long-term games the number of options with a player increases. 

Asymmetric games are the ones where the strategies adopted by players are different. In asymmetric games, the strategy that provides benefit to one player may not be equally beneficial for the other player.

Game Theory in Artificial Intelligence

Development of the majority of the popular games which we play in this digital world is with the help of AI and game theory. Game theory is used in AI whenever there is more than one person involved in solving a logical problem. There are various algorithms of Artificial Intelligence which are used in Game Theory. Minimax algorithm in Game Theory is one of the oldest algorithms in AI and is used generally for two players. Also, game theory is not only restricted to games but also relevant to the other large applications of AI like GANs (Generative Adversarial Networks).

GANs (Generative Adversarial Networks)

GAN consists of 2 models, a discriminative model and a generative model. These models are participants on the training phase which looks like a game between them, and each model tries to better than the other.

The target of the generative model is to generate samples that are considered to be fake and are supposed to have the same distribution of the original data samples; on the other hand, the target of discriminative is to enhance itself to be able to recognize the real samples among the fake samples generated by the generative model.

It looks like a game, in which each player (model) tries to be better than the other, the generative model tries to generate samples that deceive and tricks the discriminative model, while the discriminative model tries to get better in recognizing the real data and avoid the fake samples. It is the same idea of the Minimax algorithm, in which each player targets to outclass the other and minimize the supposed loss.

This game continues until a state where each model becomes an expert on what it is doing. The generative model increases its ability to get the actual data distribution and produces data like it, and the discriminative becomes an expert in identifying the real samples, which increases the system’s classification task. In such a case, each model satisfied by its output (strategy), this is called Nash Equilibrium in Game Theory.

Nash Equilibrium

Nash equilibrium, named after Nobel winning economist, John Nash, is a solution to a game involving two or more players who want the best outcome for themselves and must take the actions of others into account. When Nash equilibrium is reached, players cannot improve their payoff by independently changing their strategy. This means that it is the best strategy assuming the other has chosen a strategy and will not change it. For example, in the Prisoner’s Dilemma game, confessing is Nash equilibrium because it is the best outcome, taking into account the likely actions of others.

Conclusion

So in this article, the fundamentals of Game Theory and essential topics are covered in brief. Also, this article gives an idea of the influence of game theory artefacts in the AI space and how Game Theory is being used in the field of Machine Learning and its real-world implementations.

Machine Learning is an ever-expanding application of Artificial Intelligence with numerous applications in the other existing fields. Besides, Machine Learning Using Python is also on the verge of proving itself to be a foolproof technology in the coming years. So, don’t wait and enrol in the world-class Artificial Intelligence Certification in Delhi NCR now and rest assured! 

 

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What is a Neural Network?

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What is a Neural Network?

Before we get started with the process of building a Neural Network, we need to understand first what a Neural Network is.

A neural network is a collection of neurons connected by synapses. This collection is organized into three main layers: the input layer, the hidden layer, and the output layer.

In an artificial neural network, there are several inputs, which are called features, producing a single output, known as a label.

Analogy between Human Mind and Neural Network

Scientists believe that a living creature’s brain processes information through the use of a biological neural network. The human brain has as many as 100 trillion synapses – gaps between neurons – which form specific patterns when activated.

In the field of Deep Learning, a neural network is represented by a series of layers that work much like a living brain’s synapses. It is becoming a popular course now, with an array of career opportunities. Thus, Deep learning Certification in Gurgaon is a must for everyone.

Scientists use neural networks to teach computers how to do things for themselves. The whole concept of Neural network and its varied applications are pretty interesting. Moreover, with the matchless Neural Networks Training in Delhi, you need not look any further.

There are numerous kinds of deep learning and neural networks:

  1. Feedforward Neural Network – Artificial Neuron
  2. Radial basis function Neural Network
  3. Kohonen Self Organizing Neural Network
  4. Recurrent Neural Network (RNN) – Long Short Term Memory
  5. Convolutional Neural Network
  6. Modular Neural Network
  7. Generative adversarial networks (GANs)

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Working of a Simple Feedforward Neural Network

  1. It takes inputs as a matrix (2D array of numbers).
  2. Multiplies the input by a set weight (performs a dot product aka matrix multiplication).
  3. Applies an activation function.
  4. Returns an output.
  5. Error is calculated by taking the difference from the desired output from the data and the predicted output. This creates our gradient descent, which we can use to alter the weights.
  6. The weights are then altered slightly according to the error.
  7. To train, this process is repeated 1,000+ times. The more the data is trained upon, the more accurate our outputs will be.

Implementation of a Neural Network with Python and Keras

Keras has two types of models:

  • Sequential model
  • The model class used with functional API

Sequential model is probably the most used feature of Keras. Primarily, it represents the array of Keras Layers. It is convenient and builds different types of Neural Networks really quick, just by adding layers to it. Keras also has different types of Layers like Dense Layers, Convolutional Layers, Pooling Layers, Activation Layers, Dropout Layers etc.

The most basic layer is Dense Layer. It has many options for setting the inputs, activation function and so on. So, let’s see how one can build a Neural Network using Sequential and Dense. 

First, let’s import the necessary code from Keras:

After this step, the model is ready for compilation. The compilation step asks to define the loss function and the kind of optimizer which should be used. These options depend on the problem one is trying to solve.

Now, the model is ready to get trained. Thus, the parameters get tuned to provide the correct outputs for a given input. This can be done by feeding inputs at the input layer and then, getting an output.

After this one can calculate the loss function using the output and use backpropagation to tune the model parameters. This will fit the model parameters to the data.

Output of the above cell:-

This output shows the loss decrease and the accuracy increase over time. At this point, one can experiment with the hyper-parameters and neural network architecture to get the best accuracy.

After getting the final model architecture, one can now take the model and use feed-forward passes and predict inputs. To start making predictions, one can use the testing dataset in the model that has been created previously. Keras enables one to make predictions by using the .predict() function.

Some points to be remembered while building a strong Neural Network

1. Adding Regularization to Fight Over-Fitting

The predictive models mentioned above are prone to a problem of overfitting. This is a scenario whereby the model memorizes the results in the training set and isn’t able to generalize on data that it hasn’t seen.

In neural networks, regularization is the technique that fights overfitting by adding a layer in the neural network. It can be done in 3 ways:

  • L1 Regularization
  • L2 Regularization
  • Dropout Regularization

Out of these, Dropout is a commonly used regularization technique. In every iteration, it adds a Dropout layer in the neural network and thereby, deactivates some neurons. The process of deactivating neurons is usually random.

2. Hyperparameter Tuning

Grid search is a technique that you can use to experiment with different model parameters to obtain the ones that give you the best accuracy. This is done by trying different parameters and returning those that give the best results. It helps in improving model accuracy.

Conclusion

Neural Network is coping with the fast pace of the technology of the age remarkably well and thereby, inducing the necessity of courses like Neural Network Machine Learning Python, Neural Networks in Python course and more. Though these advanced technologies are just at their nascent stage, they are promising enough to lead the way to the future. 

In this article, Building and Training our Neural Network is shown. This simple Neural Network can be extended to Convolutional Neural Network and Recurrent Neural Network for more advanced applications in Computer Vision and Natural Language Processing respectively.

Reference Blogs:

https://keras.rstudio.com

https://www.khanacademy.org/math/precalculus/x9e81a4f98389efdf:matrices/x9e81a4f98389efdf:multiplying-matrices-by-matrices/v/matrix-multiplication-intro

 

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Statistical Application in R & Python: Negative Binomial Distribution

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Statistical Application in R & Python: Negative Binomial Distribution

Negative binomial distribution is a special case of Binomial distribution. If you haven’t checked the Exponential Distribution, then read through the Statistical Application in R & Python: EXPONENTIAL DISTRIBUTION.

It is important to know that the Negative Binomial distribution could be of two different types, i.e. – Type 1 and Type 2. In many ways, it could be seen as a generalization of the geometric distribution. The Negative Binomial Distribution essentially operates on the same principals as the binomial distribution but the objective of the former is to model for the success of an event happening in “n” number of trials. Here it is worth observing that the Geometric distribution models for the first success whereas a Negative Binomial distribution models for the Kth 

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This is explained below.

Type 1 Binomial distribution  aims to model the trails up to and including the “kth success” in “n number of trials”. To give a simple example, imagine you are asked to predict the probability that the fourth person to hear a gossip will believe that! This kind of prediction could be made using the negative binomial type 1 distribution. 

Conversely, Type 2 Binomial distribution is used to model the number of failures before the “kth success”. To give an example, imagine you are asked about how many penalty kicks it will take before a goal is scored by a particular football player. This could be modeled using a negative binomial type 2 distribution, which might be pretty tricky or almost impossible with any other methods.

The probability distribution function is given below: 

In the next section, we will take you through its practical application in Python and R. 

Application:

Mr. Singh works in an Insurance Company where his target is to sale a minimum of five policies in a day. On a particular day, he had already sold 2 policies after numerous attempts. The probability of sales on each policy is 0.6. Now, if the policies may be considered as independent Bernoulli trials, then:

  1. What is the probability that he has exactly 4 failed attempts before his 3rd successful sales of the day?
  2. What is the probability that he was fewer than 4 failed attempts before his 3rd successful sales of the day?

So, the number of sales = 3.

The probability of failed attempts is 4.

The success of each sale is 0.6.

Calculate Negative Binomial Distribution in R:

In R, we calculate negative binomial distribution to find the probability of insurance sales. Thus, we get,

  1. The probability that he has exactly 4 failed attempts before his 3rd successful sales are 8.29%.
  2. The probability that he has fewer than 4 failed attempts before his 3rd successful sales is 82.08%.

Hence, we can see that chances are quite high that Mr. Singh will succeed in making a sale after 4 failed attempts.

Calculate Negative Binomial Distribution in Python:

In Python, we get the same results as above.

Conclusion:

Negative Binomial distribution is the discrete probability distribution that is actually used for calculating the success and failure of any observation. When applied to real-world problems, the outcomes of the successes and failures may or may not be the outcomes we ordinarily view as good and bad, respectively.

Suppose we used the negative binomial distribution to model the number of days a certain machine works before it breaks down. In this case, “success” would be the days that the machine worked properly, whereas the day when the machine breaks down would be a “failure”. Another example would be, if we used the negative binomial distribution to model the number of attempts an athlete makes on goal before scoring r goals, though, then each unsuccessful attempt would be a “success”, and scoring a goal would be “failure”.

This blog will surely aid in developing a better understanding of how negative binomial distribution works in practice. If you have any comments please leave them below. Besides, if you are interested in catching up with the cutting edge technologies, then reach the premium training institute of Data Science and Machine Learning leading the market with the top-notch Machine Learning course in India.

 

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Artificial Intelligence Jobs: Data Science and Beyond!

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Artificial Intelligence Jobs: Data Science and Beyond!

Artificial Intelligence is the latest technology that the industry of computer science has been working on for quite some time now. Though it has not yet been possible to materialize the high-end AIs, weak/narrow Artificial Intelligence which includes, Siri, Cortana, Bixby, Tesla, are the ones that have grown to be simply inseparable in our daily lives. This is simultaneous with the widespread of the Artificial intelligence Course in Delhi, which is encouraging more and more students to explore new-age technologies. 

With the extensive research and tests carried out on all these new technologies to implement them in the modern industries; AI is yielding more jobs than ever before.

Jobs Springing from the Artificial Intelligence

Artificial intelligence and data always go hand in hand because it is the data that helps us gain insight into the results. Thus, it is not surprising that the professionals utter AI and data at the same instant.

When Amazon mentioned of up-skilling 100,000 employees from the United States to make them ready for the technology of the age, they also claimed that the machines with the ability to deal with data are responsible for most of these jobs.

There have been huge changes in the figures since then, with the data mapping scientists increased to 832%, the total data scientists jumped by 505%, and the total business analysts hiked about 160%. Besides, there is also a marked demand for the other employees, who are from a non-technological background. However, most of these are associated with Artificial Intelligence, like logistics coordinator and executive; process improvement manager; transportation specialist and so on.

Thus, in contradiction to our surmises that AI and its likes will throttle our jobs and crumble every other our opportunities of the same are turning out to be false for good!

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Drawing to a Close

Whether it is Machine Learning, Data Science or Artificial Intelligence, we are noticing a rapid progress and can easily count on a better future rich with technology. However, with the increasing hardware, software and advanced computing, the need to grasp the pacing technology thoroughly is becoming predominant. Thus, Machine Learning Using Python, Neural Network Machine Learning Python and Data Science Courses in Gurgaon are rising in demand to meet the need of the mass. However, you should always go for the best Artificial Intelligence Training Institute in Gurgaon to imbibe a wholesome knowledge of the subject.

 


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How to Structure Python Programs? An Extensive Guide

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How to Structure Python Programs? An Extensive Guide

Python is an extremely readable and versatile high-level programming language. It supports both Object-oriented programming as well as Functional programming. It is generally referred to as an interpreted language which means that each line of code is executed one by one and if the interpreter finds an error it stops proceeding further and gives an error message to the user. This makes Python a widely regarded language, fueling Machine Learning Using Python, Text Mining with Python course and more. Furthermore, with such a high-end programming language, Python for data analysis looks ahead for a bright future.

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In the Structure of Python

Computer languages have a structure just like human languages. Therefore, even in Python, we have comments, variables, literals, operators, delimiters, and keywords.

To understand the program structure of Python we will look at the following in this article: –

  1. Python Statement
    • Simple Statement
    • Compound Statement
  2. Multiple Statements Per Line
  3. Line Continuation
    • Implicit Line Continuation
    • Explicit Line Continuation
  4. Comments
  5. Whitespace
  6. Indentation
  7. Conclusion

Python Statement

A statement in Python is a logical instruction that the interpreter reads and executes. The interpreter executes statements sequentially, one by one. In Python, it could be an assignment statement or an expression. The statements are mostly written in such a style so that each statement occupies a single line.

Simple Statements

A simple statement is one that contains no other statements. Therefore, it lies entirely within a logical line. An assignment is a simple statement that assigns values to variables, unlike in some other languages; an assignment in Python is a statement and can never be part of an expression.

Compound Statement

A compound statement contains one or more other statements and controls their execution. A compound statement has one or more clauses, aligned at the same indentation. Each clause has a header starting with a keyword and ending with a colon (:), followed by a body, which is a sequence of one or more statements. When the body contains multiple statements, also known as blocks, these statements should be placed on separate logical lines after the header line, indented four spaces rightward.

Multiple Statements per Line

Although it is not considered good practice multiple statements can be written in a single line in Python. It is advisable to avoid multiple statements in a single line. But, if it is necessary, then it can be written with the help of semicolon (;) as the terminator of every statement.

Line Continuation

In Python there might be some cases when a single statement is too long that does not fit the browser window and one needs to scroll the screen left or right. This can be a case of assignment statement with many terms or defining a lengthy nested list. These long statements of code are generally considered a poor practice.

To maintain readability, it is advisable to split the long statement into parts across several lines. In Python code, a statement can be continued from one line to the next in two different ways: implicit and explicit line continuation.

Implicit Line Continuation

This is the more straightforward technique for line continuation. In implicit line continuation, one can split a statement using either of parentheses ( ), brackets [ ] and braces { }. Here, one needs to enclose the target statement using the mentioned construct.

Explicit Line Continuation

In cases where implicit line continuation is not readily available or practicable, there is another option. This is referred to as an explicit line continuation or explicit line joining. Here, one can right away use the line continuation character (\) to split a statement into multiple lines.

Comments

A comment is text that doesn’t affect the outcome of a code; it is just a piece of text to let someone know what you have done in a program or what is being done in a block of code. This is especially helpful when a code is written and someone is analyzing it for bug fixing or making a change in logic, by reading a comment one can understand the purpose of code much faster than by just going through the actual code.

There are two types of comments in Python.
1. Single line comment
2. Multiple line comment

Single line comment

In python, one can use # special character to start the comment.

Multi-line comment

To have a multi-line comment in Python, one can use Triple Double Quotation at the beginning and the end of the comment.

Whitespace

One can improve the readability of the code with the use of whitespaces. Whitespaces are necessary for separating the keywords from the variables or other keywords. Whitespace is mostly ignored by the Python interpreter.

Indentation

Most of the programming languages provide indentation for better code formatting and don’t enforce to have it. However, in Python, it is mandatory to obey the indentation rules. Typically, we indent each line by four spaces (or by the same amount) in a block of code. Also for creating compound statements, the indentation will be of utmost necessity.

Conclusion

So, this article was all about how to structure the Python program. Here, one can learn what constitutes a valid Python statement and how to use implicit and explicit line continuation to write a statement that spans multiple lines. Furthermore, one can also learn about commenting Python code, and about the use of whitespace and indentation to enhance the overall readability.

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Machine Learning in the Healthcare Sector

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Machine Learning in the Healthcare Sector

The healthcare industry is one of the most important industries when it comes to human welfare. Research analysis from the U.S. federal government actuaries say that Americans spent $3.65 trillion on health care in 2018(report from Axios) and the Indian healthcare market is expected to reach $ 372 billion by 2022. To reduce cost and to move towards a personalized healthcare system, the industry faces three major hurdles: –

1) Electronic record management
2) Data integration
3) Computer-aided diagnoses.

Machine learning in itself is a vast field with a wide array of tools, techniques, and frameworks that can be exploited and manipulated to cope with these challenges. In today’s time, Machine Learning Using Python is proving to be very helpful in streamlining the administrative processes in hospitals, map and treat life-threatening diseases and personalizing medical treatments.

This blog will focus primarily on the applications of Machine learning in the domain of healthcare.

Real-life Application of Machine learning in the Health Sector

  1. MYCIN system was incepted at Stanford University. The system was developed in order to detect specific strains of bacteria that cause infections. It proposed a good therapy in 69% of the cases which was at that time better than infectious disease experts.

  2. In the 1980s at the University of Pittsburgh, a diagnostic tool named INTERNIST-I was developed to diagnose symptoms of various diseases like flu, pneumonia, diabetes and more. One of the key functionalities of the INTERNIST-I was to be able to detect the problem areas. This is done with a view of being able to remove diagnostics’ likelihood.

  3. AI trained by researchers from Pennsylvania has been developed recently which is capable of predicting patients who are most likely to die within a year. This is assessed based on their heart test results. This AI is capable of predicting the death of patients even if the figures look quite normal to the doctors. The researchers have trained the AI with 1.77 million electrocardiograms (ECG) results. The researchers have made two versions of this Al: one with just the ECG data and the other one with ECG data along with the age and gender of the patients.
  4. P1vital’s PReDicT (Predicting Response to Depression Treatment) built on the Machine Learning algorithms aims to develop a commercially feasible way to diagnose and provide treatment of depression in clinical practice.
  5. KenSci has developed machine learning algorithms to predict illnesses and their cure to enable doctors with the ability to detect specific patterns and indicators of population health risks. This comes under the purview of model disease progression.
  6. Project Hanover developed by Microsoft is using Machine Learning-based technologies for multiple purposes, which includes the development of AI-based technology for cancer treatment and personalizing drug combination for Acute Myeloid Leukemia (AML).
  7. Preserving data in the health care industry has always been a daunting task. However, with the forward-looking steps in analytics-related technology, it has become more manageable over the years. The truth is that even now, a majority of the processes take a lot of time to complete.

    8. Machine learning can prove to be disruptive in the medical sector by automating processes relating to data collection and collation. This is highly profitable in terms of cost-effectiveness. Newer algorithms such as Vector Machines or OCR recognition are designed to automate the task of document reading and classification with high levels of precision and accuracy.

  8. PathAI’s technology uses machine learning to help pathologists make faster and more accurate diagnoses. Furthermore, it also helps in identifying patients who might benefit from a new and different type of treatments or therapies in the future.

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To Sum Up:

As the modern technologies of Machine Learning, Artificial Intelligence and Big Data Analytics are tottering forth in multiple domains, there is a long path they need to walk to ensure an unflinching success. Besides, it is also important for every one of us to be accustomed to all these new-age technologies.

With an expansion of the quality Machine Learning course in India and Neural Network Machine learning Python, all the reputed institutes are joining hands together to bring in the revolution. The initial days will be slow and hard, but it is no doubt that these cutting edge technologies will transform the medical industry along with a range of other industries, making early diagnoses possible along with a reduction of the overall cost. Besides, with the introduction of successful recommender systems and other promises of personalized healthcare, coupled with systematic management of medical records, Machine Learning will surely usher in the future for good! 

 


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Machine Learning Jobs in 2019: Freezing your own Job

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Machine Learning Jobs in 2019: Freezing your own Job

Machine Learning surely needs no introduction. Joining forces with Data Science and Big Data, Machine Learning is one of the principal technologies, which is carving the future for us. From self-propelled cars to voice assistants, to surgical robots, Artificial Intelligence is already amongst us.

Besides, with this cutting-edge technology, marketing is also witnessing a fresh bloom, irrespective of the field you are working on. Thus, it is obvious that the career opportunities have quickly and radically shifted in the way of the candidates who are well-versed with Machine Learning platforms and languages. If you are also looking forward to shooting your career up, the premium Machine Learning course in India is the place you should reach now!

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Learning Machine Learning is No More a Pain Now!

Whether you are a professional or a fresher planning your way to be successful as a Machine Learning professional, you must ensure that you are updated. Besides, you should also be careful that you have certain skills in your grip that you can work on!

However, if you are not aware of them still, here are the skills that you need to focus on to rest assured:

Programming Languages

As you speak English and/or your regional languages accepted to your society in order to communicate comprehensibly, you also need to be well-versed with the languages specific to Machine Learning.

In a nutshell, R programming certification and Machine Learning Using Python are undoubtedly the most significant ones when it comes to Machine Learning.

Data Modeling

If you believe that you can already boast of considerable knowledge of R & Python, then you shall extend your knowledge a bit more towards the advanced methods of analysis. Brief know how of the coding structures, Data Modeling and Data Visualization will help you steer your career forwards.

Deep Learning and AI using Python

Statistics and Probability

If you are seeking to make a career out of Machine Learning, it is important to note that you should have a good grip of statistics and probability. Now, with the thorough courses of Python for Data Analysis along with extensive knowledge of statistics and probability from Dexlab Analytics, it will be easier than ever.

Besides all these, you also need to grasp significant insights into the improved algorithms and clustering methods. 

 

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A Nifty Guide to Initiate AIOps in 2019

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A Nifty Guide to Initiate AIOps in 2019

AIOps (artificial intelligence for IT operations) is the buzz word of the 21st century.

In this digitally-charged world, AIOps platforms are the key. They fuse ML and big data functionalities to boost and partly replace primary IT operations’ programs, including event correlation and analysis, performance monitoring and IT service automation and management.

In simple terms, AIOps is the combined application of data science and machine learning to help mitigate IT operations-related challenges and find faster insights. It fixes high-severity outages in a jiffy. 

The main objective of revolutionary AIOps platforms is to ingest and analyze the aggravating volume, variety and velocity of data and deliver it in a useful manner.

Deep Learning and AI using Python

IT bigwigs are excited about the prospects of applying AI and ML to IT operations.

Gartner expects that big enterprises’ usage of AIOps and other monitoring tools and applications will rise from 5% in 2018 to 30% in 2023. The long-term impact of AIOps on IT operations is predicted to be transformative.

Fortunately, AI capabilities are making headway, and more real-time solutions are being formulated and made available each day.

Read on to know how to get started with AIOPs:

Be prepared

First and foremost, you have to familiarize yourself with all the ML and AI capabilities and vocabulary. It doesn’t matter if you are gearing up for an AIOps project or not. Capabilities and priorities change; so be ready to implement the platform anytime soon.

Select the first few test cases carefully

Small and steady wins the race. The same phrase applies to transformation initiatives. They start small, seize knowledge and iterate from there. Imbibe the same approach for AIOps success.

Enhance your proficiency

Decode the intricacies of AIOps amongst your colleagues by displaying simple techniques. Ascertain your skills and identify the loopholes, then devise a relevant plan to fill up those gaps in-between.

Feel free to experiment

Although a majority of AIOps platforms are complex and costly, there is a substantial number of open-source and relatively low-cost ML software available in the market that lets you evaluate the efficacy of AIOps and ML applications and their uses.

Look beyond IT

Don’t forget to leverage all kinds of data analytics resources available in your organization. Data management is the cornerstone of AIOps. Most of the teams are already skilled in it. Statistical analytics and business analysis are key components of contemporary business frameworks, and many techniques traverse public domains. 

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Standardize and modernize, as and when required

Prepare your work infrastructure to implement a robust AIOps adoption by embracing secure automation architecture, immutable infrastructure patterns and infrastructure as code (IaC).

Interested in learning more about Machine Learning Using Python? Feel free to reach us at DexLab Analytics. We’re a premier learning platform specialized in offering in-demand skill training courses to the interested candidates.

 

The blog has been sourced from ― www.gartner.com/smarterwithgartner/how-to-get-started-with-aiops

 

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Take a Deep Look on How Machine Learning Boosts Business Growth!

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Take a Deep Look on How Machine Learning Boosts Business Growth!

Machine Learning is the technology of the future and the rise of it is, well, shocking! Numerous businesses have already started adopting Machine Learning into their business strategy which is ultimately culminating towards their growth. You can also get the most of Machine Learning by going for the best Machine Learning course in India without wasting hours on the internet.

This new and improving technology is showing marked results in making a particular business more efficient, enhancing customer relationships and driving more sales than ever. You can get right on to Machine Learning Significantly Aids in Improving the Business Performance: Learn the Hows and learn about Machine Learning and its rising curve.

Here we have decided to discuss in details about the ways how Machine Learning is helping business touch great heights:

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Natural Language

One of the major setbacks in the industry of computer science was the inability of computers to comprehend our natural language or the way we speak in our everyday life. This is slowly changing with the rapid growth and considerable research and development on Machine Learning. 

It looks like we have come a long way from the crude search terms that we used to generate the results that we wanted. The AI-driven programs of now, with the help of Machine Learning, can figure out the essence of our conversations and also capitalizing largely on the nuances of our language. Most importantly, they learn from past experiences, which is highly progressive.

Logistics

The retail industry and that of logistics are largely relying on Python for Data Analysis and this in turn, is making them future-proof.

Retail giants like Amazon are encouraging the use of Machine Learning to sharpen the efficiency of their company with new features and technology like “anticipatory shopping” protocol. Retail analytics using Python is becoming formidable.

Even in the field of logistics, the inclusion of Machine Learning is proving a boon!

Manufacturing Industry

Innumerable manufacturing companies are adopting the budding technology of Machine Learning and utilizing it in almost every stage of production, simply because the AI-driven technology reduces unnecessary expenses. 

Companies like Seebo, are taking up Python seriously to build accurate data analytics software. Moreover, machine learning is estimated to cut down on the delivery times by 30% and surprisingly save fuel by 12%. According to the reports, the programs fed on AI would even reduce the maintenance costs by 20 – 30%.

Deep Learning and AI using Python

Consumer Data

We have already seen a world of data collection which has been on a rise for years. Now, finally, with the rise of machine learning, the companies are looking forward to making some use of all these data that they have accumulated. In the coming years, we will see AI improving powered by Machine Learning to make the world productive and smart all the more.

You can take a look at A DISCUSSION ABOUT ARTIFICIAL INTELLIGENCE: KNOWING AI CLOSELY if you are interested in AI. Stay glued to our website for more updates and information from the world of technology!

 

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