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R machine learning training Hyderabad

During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science.

Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn.

R is rapidly becoming the leading language in data science and R Machine learning training Hyderabadstatistics. Today, the R programming language is the tool of choice for data scientists in every industry and field. Whether you are a full-time number cruncher, or just the occasional data analyst, R will suit your needs. R machine learning training Hyderabad

If science fiction stories are to be believed, teaching machines to learn will inevitably lead to apocalyptic wars between machines and their makers.
In the early stages, computers are taught to play simple games of tic-tac-toe and chess. Later, machines are given control of traffic lights and communications, followed by military drones and missiles. The machines’ evolution takes an ominous turn once the computers become sentient and learn how to teach themselves. Having no more need for human programmers, humankind is then “deleted.”
Thankfully, at the time of this writing, machines still require user input.
 Your impressions of machine learning may be very heavily influenced by these types of mass media depictions of artificial intelligence. And even though there may be a hint of truth to such tales; in reality, R Machine learning training Hyderabadmachine learning is focused on more practical applications.
The task of teaching a computer to learn is tied more closely to a specific problem that would be a computer that can play games, ponder philosophy, or answer trivial questions. Machine learning is more like training an employee than raising a child.
Putting these stereotypes aside, you will have gained a far more nuanced understanding of machine learning. You will be introduced to the fundamental concepts that define and differentiate the most commonly used machine learning approaches.
In a single sentence, you could say that machine learning provides a set of tools that use computers to transform data into actionable knowledge. To learn more about how the process works, read on.

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A closely related sibling of machine learning, data mining, is concerned with the generation of novel insight from large databases (not to be confused with the pejorative term “data mining,” describing the practice of cherry-picking data to support a theory).
Although there is some disagreement over how widely the two fields overlap, a potential point of distinction is that machine learning tends to be focused on performing a known task, whereas data mining is about the search for hidden nuggets of information.
For instance, you might use machine learning to teach a robot to drive a car, whereas you would utilize data mining to learn what type of cars are the safest.
Uses and abuses of machine learning
At its core, machine learning is primarily interested in making sense of complex data. This is a broadly applicable mission, and largely application agnostic. As you might expect, machine learning is used widely. For instance, it has been used to:
  • Predict the outcomes of elections
  • Identify and filter spam messages from e-mail
  • Foresee criminal activity
  • Automate traffic signals according to road conditions
  • Produce financial estimates of storms and natural disasters
  • Examine customer churn
  • Create auto-piloting planes and auto-driving cars
  • Identify individuals with the capacity to donate
  • Target advertising to specific types of consumers

For now, don’t worry about exactly how the machines learn to perform these tasks; we will get into the specifics later. But across each of these contexts, the process is the same.

A machine learning algorithm takes data and identifies patterns that can be used for action. In some cases, the results are so successful that they seem to reach near-legendary status.
One possibly apocryphal tale is of a large retailer in the United States, which employed machine learning to identify expectant mothers for targeted coupon mailings.
If mothers-to-be were targeted with substantial discounts, the retailer hoped they would become loyal customers who would then continue to purchase profitable items like diapers, formula, and toys.
By applying machine learning methods to purchase data, the retailer believed it had learned some useful patterns.
Certain items, such as prenatal vitamins, lotions, and washcloths could be used to identify with a high degree of certainty not only whether a woman was pregnant, but also when the baby was due.
After using this data for a promotional mailing, an angry man contacted the retailer and demanded to know why his teenage daughter was receiving coupons for maternity items.
He was furious that the merchant seemed to be encouraging teenage pregnancy. Later on, as a manager called to offer an apology, it was the father that ultimately apologized; after confronting his daughter, he had discovered that she was indeed pregnant.
Whether completely true or not, there is certainly an element of truth to the preceding tale. Retailers, do in fact, routinely analyze their customers’ transaction data. If you’ve ever used a shopper’s loyalty card at your grocer, coffee shop, or another retailer, it is likely that your purchase data is being used for machine learning.
Retailers use machine learning methods for advertising, targeted promotions, inventory management, or the layout of the items in the store.
Some retailers have even equipped checkout lanes with devices that print coupons for promotions based on the items in the current transaction.
Websites also routinely do this to serve advertisements based on your web browsing history. Given the data from many individuals, a machine learning algorithm learns typical patterns of behavior that can then be used to make recommendations.
Despite being familiar with the machine learning methods working behind the scenes, it still feels a bit like magic when a retailer or website seems to know me better than I know myself.
Others may be less thrilled to discover that their data is being used in this manner. Therefore, any person wishing to utilize machine learning or data mining would be remiss not to at least briefly consider the ethical implications of the art.
Regardless of whether the learner is a human or a machine, the basic learning process is similar. It can be divided into three components as follows:
  • Data input: It utilizes observation, memory storage, and recall to provide a factual basis for further reasoning.
  • Abstraction: It involves the translation of data into broader representations.
  • Generalization: It uses abstracted data to form a basis for action.

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To better understand the learning process, think about the last time you studied for a difficult test, perhaps for a university final exam or a career certification.
Did you wish for an eidetic (that is, photographic) memory? If so, you may be disappointed to learn that perfect recall is unlikely to save you much effort.
Without a higher understanding, your knowledge is limited exactly to the data input, meaning only what you had seen before and nothing more. Therefore, without knowledge of all the questions that could appear on the exam, you would be stuck attempting to memorize answers to every question that could conceivably be asked. Obviously, this is an unsustainable strategy.
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Steps to apply machine learning to your data
Any machine learning task can be broken down into a series of more manageable steps. This book has been organized according to the following process:
  1. Collecting data: Whether the data is written on paper, recorded in text files and spreadsheets, or stored in an SQL database, you will need to gather it in an electronic format suitable for analysis.
  2. Exploring and preparing the data: The quality of any machine learning project is based largely on the quality of data it uses. This step in the machine learning process tends to require a great deal of human intervention.
  3. Training a model on the data: By the time the data has been prepared for analysis, you are likely to have a sense of what you are hoping to learn from the data
  4. Evaluating model performance: Because each machine learning model results in a biased solution to the learning problem, it is important to evaluate how well the algorithm learned from its experience.
  5. Improving model performance: If better performance is needed, it becomes necessary to utilize more advanced strategies to augment the performance of the model. Sometimes, it may be necessary to switch to a different type of model altogether.
After these steps have been completed, if the model appears to be performing satisfactorily, it can be deployed for its intended task. As the case may be, you might utilize your model to provide score data for predictions (possibly in real time), for projections of financial data, to generate useful insight for marketing or research, or to automate tasks such as mail delivery or flying aircraft. The successes and failures of the deployed model might even provide additional data to train the next generation of your model.
Choosing a machine learning algorithm
The process of choosing a machine learning algorithm involves matching the characteristics of the data to be learned to the biases of the available approaches.
Since the choice of a machine learning algorithm is largely dependent upon the type of data you are analyzing and the proposed task at hand, it is often helpful to be thinking about this process while you are gathering, exploring, and cleaning your data.
Thinking about the input data
All machine learning algorithms require input training data. The exact format may differ, but in its most basic form, input data takes the form of examples and features.
An example is literally a single exemplary instance of the underlying concept to be learned; it is one set of data describing the atomic unit of interest for the analysis.
If you were building a learning algorithm to identify spam e-mail, the examples would be data from many individual electronic messages. To detect cancerous tumors, the examples might comprise biopsies from a number of patients.
The phrase unit of observation is used to describe the units that the examples are measured in. Commonly, the unit of observation is in the form of transactions, persons, time points, geographic regions, or measurements. Other possibilities include combinations of these such as person years, which would denote cases where the same person is tracked over multiple time points.
feature is a characteristic or attribute of an example, which might be useful for learning the desired concept. In the previous examples, attributes in the spam detection dataset might consist of the words used in the e-mail messages. For the cancer dataset, the attributes might be genomic data from the biopsied cells, or measured characteristics of the patient such as weight, height, or blood pressure.
The following spreadsheet shows a dataset in matrix format, which means that each example has the same number of features. In matrix data, each row in the spreadsheet is an example and each column is a feature.
Here, the rows indicate examples of automobiles while the columns record various features of the cars such as the price, mileage, color, and transmission. Matrix format data is by far the most common form used in machine learning, though as you will see in later, other forms are used occasionally in specialized cases.
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Thinking about types of machine learning algorithms
Machine learning algorithms can be divided into two main groups: supervised learners that are used to construct predictive models, and unsupervised learners that are used to build descriptive models. Which type you will need to use depends on the learning task you hope to accomplish.
predictive model is used for tasks that involve, as the name implies, the prediction of one value using other values in the dataset. The learning algorithm attempts to discover and model the relationship among the target feature (the feature being predicted) and the other features.
Despite the common use of the word “prediction” to imply forecasting predictive models need not necessarily foresee future events. For instance, a predictive model could be used to predict past events such as the date of a baby’s conception using the mother’s hormone levels; or, predictive models could be used in real time to control traffic lights during rush hours.
The often used supervised machine learning task of predicting which category an example belongs to is known as classification. It is easy to think of potential uses for a classifier. For instance, you could predict whether:
  • A football team will win or lose
  • A person will live past the age of 100
  • An applicant will default on a loan
  • An earthquake will strike next year
The target feature to be predicted is a categorical feature known as the class and is divided into categories called levels. A class can have two or more levels, and the levels need not necessarily be ordinal. Because classification is so widely used in machine learning, there are many types of classification algorithms.
Supervised learners can also be used to predict numeric data such as income, laboratory values, test scores, or counts of items. To predict such numeric values, a common form of numeric prediction fits linear regression models to the input data. Although regression models are not the only type of numeric models, they are by far the most widely used. Regression methods are widely used for forecasting, as they quantify in exact terms the association between the inputs and the target, including both the magnitude and uncertainty of the relationship.
Using R for machine learning
Many of the algorithms needed for machine learning in R are not included as part of the base installation. Thanks to R being free open source software, there is no additional charge for this functionality. The algorithms needed for machine learning were added to base R by a large community of experts who contributed to the software. A collection of R functions that can be shared among users is called a package. Free packages exist for each of the machine learning algorithms covered in this book. In fact, this book only covers a small portion of the more popular machine learning packages.
Machine learning originated at the intersection of statistics, database science, and computer science. It is a powerful tool, capable of finding actionable insight in large quantities of data. Still, caution must be used in order to avoid common abuses of machine learning in the real world.
In conceptual terms, learning involves the abstraction of data into a structured representation, and the generalization of this structure into action. In more practical terms, a machine learner uses data containing examples and features of the concept to be learned, and summarizes this data in the form of a model, which is then used for predictive or descriptive purposes. These can be further divided into specific tasks including classification, numeric prediction, pattern detection, and clustering. Among the many options, machine learning algorithms are chosen on the basis of the input data and the learning task.
R provides support for machine learning in the form of community-authored packages. These powerful tools are free to download, but need to be installed before they can be used.  We will further introduce the basic R commands that are used to manage and prepare data for machine learning.
Practical Machine learning in R with Linear Regression
 
Regression is a supervised learning method, which is employed to model and analyze the relationship between a dependent (response) variable and one or more independent (predictor) variables. One can use regression to build a prediction model, which can first be used to find the best fitted model with minimum squared errors of the fitted values. The fitted model can then be further applied to data for continuous value predictions.
There are many types of regression. If there is only one predictor variable, and the relationship between the response variable and independent variable is linear, we can apply a linear model. However, if there is more than one predictor variable, a multiple linear regression method should be used. When the relationship is nonlinear, one can use a nonlinear model to model the relationship between the predictor and response variables.
we introduce how to fit a linear model into data with the lm function. Next, for distribution in other than the normal Gaussian model (for example, Poisson or Binomial), we use the glm function with an appropriate link function correspondent to the data distribution. Finally, we cover how to fit a generalized additive model into data using the gam function.
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Fitting a linear regression model with lm
The simplest model in regression is linear regression, which is best used when there is only one predictor variable, and the relationship between the response variable and the independent variable is linear. In R, we can fit a linear model to data with the lm function.
Getting ready
We need to prepare data with one predictor and response variable, and with a linear relationship between the two variables.
How to do it…
Perform the following steps to perform linear regression with lm:
1 You should install the car package and load its library:
 
> install.packages(“car”)
> library(car)
2 From the package, you can load the Quartet dataset:>
data(Quartet)
3 You can use the str function to display the structure of the Quartet dataset:
> str(Quartet)
 ‘data.frame’:   11 obs. of  6 variables:
 $ x : int  10 8 13 9 11 14 6 4 12 7 …
 $ y1: num  8.04 6.95 7.58 8.81 8.33 …
 $ y2: num  9.14 8.14 8.74 8.77 9.26 8.1 6.13 3.1 9.13 7.26 …
 $ y3: num  7.46 6.77 12.74 7.11 7.81 …
 $ x4: int  8 8 8 8 8 8 8 19 8 8 …
 $ y4: num  6.58 5.76 7.71 8.84 8.47 7.04 5.25 12.5 5.56 7.91 …
4 Draw a scatter plot of the x and y variables with plot, and append a fitted line through the lm and abline function:
> plot(Quartet$x, Quartet$y1)
> lmfit = lm(y1~x, Quartet)
> abline(lmfit, col=”red”)
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To view the fit model, execute the following:
> lmfitCall:lm(formula = y1 ~ x, data = Quartet)
Coefficients:(Intercept)          
  x     3.0001       0.5001
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