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A Handbook of the Basic Data Types in Python 3: Strings

A Handbook of the Basic Data Types in Python 3: Strings

In general, a data type defines the format, sets the upper & lower bounds of the data so that a program could use it appropriately. Data types are the classification or categorization of data items which describes the character of a variable. The most used data types are numeric, non-numeric and Boolean (true/false).

Python has the following standard Data Types:

  • Booleans
  • Numbers
  • String
  • List
  • Tuple
  • Set
  • Dictionary

Mutable and Immutable Objects

Data objects of the above types are stored in a computer’s memory for processing. Some of these values can be modified during processing, but the contents of the others can’t be altered once they are created in the memory.

Number values, strings, and tuple are immutable, which means their contents can’t be altered after creation.

On the other hand, the collection of items in a List or Dictionary object can be modified. It is possible to add, delete, insert, and rearrange items in a list or dictionary. Hence, they are mutable objects.

Booleans

A Boolean is such a data type that almost every programming language has, and so does Python. Boolean in Python can have two values – True or False. These values can be used for assigning and comparison.

Numbers

Numbers are one of the most prominent Python data types. In Numbers, there are mainly 3 types which include Integer, Float, and Complex.

String

A sequence of one or more characters enclosed within either single quotes ‘or double quotes” is considered as String in Python. Any letter, a number or a symbol could be a part of the string. Multi-line strings can be represented using triple quotes,”’ or “””.

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List

Python list is an array-like construct which stores a heterogeneous collection of items of varied data typed objects in an ordered sequence. It is very flexible and does not have a fixed size. The Index in a list begins with a zero in Python.

Tuple

A tuple is a sequence of Python objects separated by commas. Tuples are immutable, which means tuples once created cannot be modified. Tuples are defined using parentheses ().

Set

A set is an unordered collection of items. Set is defined by values separated by a comma inside braces { }. Amongst all the Python data types, the set is one which supports mathematical operations like union, intersection, symmetric difference etc. Since the set derives its implementation from the “Set” in mathematics, so it can’t have multiple occurrences of the same element.

Dictionary

A dictionary in Python is an unordered collection of key-value pairs. It’s a built-in mapping type in Python where keys map to values. These key-value pairs provide an intuitive way to store data. To retrieve the value we must know the key. In Python, dictionaries are defined within braces {}.

This article is about one specific data type, which is a string. The String is a sequence of characters enclosed in single (”) or double quotation (“”) marks.

Here are examples of creating strings in Python.

Counting Number of Characters Using LEN () Function

The LEN () built-in function counts the number of characters in the string.

Creating Empty Strings

Although variables S3 and S4 do not contain any characters they are still valid strings. S3 and S4 both represent empty strings here.

We can verify this fact by using the type () function.

String Concatenation

String concatenation means joining one or more strings together. To concatenate strings in Python we use + operator.

String Repetition Operator (*)

Just like in numbers, * operator can also be used with strings. When used with strings * operator repeats the string n number of times. Its general format is: 1 string * n,

where n is a number of type int.

Membership Operators – in and not in

The in or not in operators are used to check the existence of a string inside another string. For example:

Indexing in a String

In Python, characters in a string are stored in a sequence. We can access individual characters inside a string by using an index.

An index refers to the position of a character inside a string. In Python, strings are 0 indexed. This means that the first character is at index 0; the second character is at index 1 and so on. The index position of the last character is one less than the length of the string.

To access the individual characters inside a string we type the name of the variable, followed by the index number of the character inside the square brackets [].

Instead of manually counting the index position of the last character in the string, we can use the LEN () function to calculate the string and then subtract 1 from it to get the index position of the last character.

We can also use negative indexes. A negative index allows us to access characters from the end of the string. Negative index starts from -1, so the index position of the last character is -1, for the second last character it is -2 and so on.

Slicing Strings

String slicing allows us to get a slice of characters from the string. To get a slice of string we use the slicing operator. Its syntax is:

str_name[start_index:end_index]

str_name[start_index:end_index] returns a slice of string starting from index start_index to the end_index. The character at the end_index will not be included in the slice. If end_index is greater than the length of the string then the slice operator returns a slice of string starting from start_index to the end of the string. The start_index and end_index are optional. If start_index is not specified then slicing begins at the beginning of the string and if end_index is not specified then it goes on to the end of the string. For example:

Apart from these functionalities, there are so many built-in methods for strings which make the string as the useful data type of Python. Some of the common built-in methods are as follows: –

capitalize ()

Capitalizes the first letter of the string

join (seq)

Merges (concatenates) the string representations of elements in sequence seq into a string, with separator string.

lower ()

Converts all the letters in a string that are in uppercase to lowercase.

max (str)

Returns the max alphabetical character from the string str.

min (str)

Returns the min alphabetical character from the string str.

replace (old, new [, max])

Replaces all the occurrences of old in a string with new or at most max occurrences if max gave.

 split (str=””, num=string.count(str))

Splits string according to delimiter str (space if not provided) and returns list of substrings; split into at most num substrings if given.

upper()

Converts lowercase letters in a string to uppercase.

Conclusion

So in this article, firstly, we have seen a brief introduction of all the data types of python. Later in this article, we focused on the strings. We have seen several Python operations on strings as well as the most common useful built-in methods of strings.

Python is the language of the present age, wherein almost every field there is a need for Python. For example, Python for data analysisMachine Learning Using Python has been easy and comprehensible than they were ever before. Thus, if you are also interested in Python and looking for promising courses Computer Vision Course PythonRetail Analytics using PythonNeural Network Machine Learning Python, then get in touch with Dexlab Analytics now and step into the world of opportunities!

 

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Python Statistics Fundamentals: How to Describe Your Data? (Part II)

Python Statistics Fundamentals: How to Describe Your Data? (Part II)

In the first part of this article, we have seen how to describe and summarize datasets and how to calculate types of measures in descriptive statistics in Python. It’s possible to get descriptive statistics with pure Python code, but that’s rarely necessary.

Python is an advanced programming language extensively used in all of the latest technologies of Data Science, Deep Learning and Machine learning. Furthermore, it is particularly responsible for the growth of the Machine Learning course in IndiaMoreover, numerous courses like Deep Learning for Computer vision with Python, Text Mining with Python course and Retail Analytics using Python are pacing up with the call of the age. You must also be in line with the cutting-edge technologies by enrolling with the best Python training institute in Delhi now, not to regret it later.

In this part, we will see the Python statistics libraries which are comprehensive, popular, and widely used especially for this purpose. These libraries give users the necessary functionality when crunching data. Below are the major Python libraries that are used for working with data.

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NumPy and SciPy – Fundamental Scientific Computing

NumPy stands for Numerical Python. The most powerful feature of NumPy is the n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities. NumPy is much faster than the native Python code due to the vectorized implementation of its methods and the fact that many of its core routines are written in C (based on the CPython framework).

For example, let’s create a NumPy array and compute basic descriptive statistics like mean, median, standard deviation, quantiles, etc.

SciPy stands for Scientific Python, which is built on NumPy. NumPy arrays are used as the basic data structure by SciPy.

Scipy is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transforms, Linear Algebra, Optimization and Sparse matrices. Specifically in statistical modelling, SciPy boasts of a large collection of fast, powerful, and flexible methods and classes. It can run popular statistical tests such as t-test, chi-square, Kolmogorov-Smirnov, Mann-Whitney rank test, Wilcoxon rank-sum, etc. It can also perform correlation computations, such as Pearson’s coefficient, ANOVA, Theil-Sen estimation, etc.

Pandas – Data Manipulation and Analysis

Pandas library is used for structured data operations and manipulations. It is extensively used for data preparation. The DataFrame() function in Pandas takes a list of values and outputs them in a table. Seeing data enumerated in a table gives a visual description of a data set and allows for the formulation of research questions on the data.

The describe() function outputs various descriptive statistics values, except for the variance. The variance is calculated using the var() function in Pandas.

The mean() function, returns the mean of the values for the requested axis.

Matplotlib – Plotting and Visualization

Matplotlib is a Python library for creating 2D plots. It is used for plotting a wide variety of graphs, starting from histograms to line plots to heat plots. One can use Pylab feature in IPython notebook (IPython notebook –pylab = inline) to use these plotting features inline. If the inline option is ignored, then pylab converts IPython environment to an environment, very similar to Matlab.

matplotlib.pylot is a collection of command style functions.

If a single list array is provided to the plot() command, matplotlib assumes it is a sequence of Y values and internally generates the X value for you.

Each function makes some change to a figure, like creating a figure, creating a plotting area in a figure, decorating the plot with labels, etc. Now, let us create a very simple plot for some given data, as shown below:

Scikit-learn – Machine Learning and Data Mining

Scikit-learn built on NumPy, SciPy and matplotlib. Scikit-learn is the most widely used Python library for classical machine learning. But, it is necessary to include it in the discussion of statistical modeling as many classical machine learning (i.e. non-deep learning) algorithms can be classified as statistical learning techniques. This library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensional reduction.

Conclusion

In this article, we covered a set of Python open-source libraries that form the foundation of statistical modelling, analysis, and visualization. On the data side, these libraries work seamlessly with the other data analytics and data engineering platforms, such as Pandas and Spark (through PySpark). For advanced machine learning tasks (e.g. deep learning), NumPy knowledge is directly transferable and applicable in popular packages such as TensorFlow and PyTorch. On the visual side, libraries like Matplotlib, integrate nicely with advanced dashboarding libraries like Bokeh and Plotly.

 

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html

 

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Deep Learning and its Progress as Discussed at Intel’s AI Summit

Deep Learning and its Progress as Discussed at Intel’s AI Summit

At the latest AI summit organized by Intel, Mr. Naveen Rao, Vice President and General Manager of Intel’s AI Products Group, focused on the most vibrant age of computing that is the present age we are living. According to Rao, the widespread and sudden growth of neural networks is putting the capability of the hardware into a real test. Therefore, we now have to reflect deeply on “how processing, network, and memory work together” to figure a pragmatic solution, he said.

The storage of data has seen countless improvements in the last 20 years. We can now boast of our prowess of handling considerably large sets of data, with greater computing capability in a single place. This led to the expansion of the neural network models with an eye on the overall progress in neural Network Machine Learning Python and computing in general.

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With the onset of exceedingly large data sets to work with, Deep learning for Computer Vision Course and the other models of Deep Learning to recognize speech, images, and text are extensively feeding on them. The technological giants were undoubtedly the early birds to grab the technical: the hardware and the software configuration to have an edge on the others. 

Surely, Deep Learning is on its peak now, where computers can identify the images with incredible vividness. On the other hand, chatbots can carry on with almost natural conversations with us. It is no wonder that the Deep learning Training Institutes all over the world are jumping in the race to bring all of these new technologies efficiently to the general mass.

The Big Problem

We are living in the dynamic age of AI and Machine Learning, with the biggies like Google, Facebook, and its peers, having the technical skills and configuration to take up the challenges. However, the neural networks have fattened up so much lately that it has already started to give the hardware a tough time, getting the better of them all the time.

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The number of parameters of the Neural network models is increasing as never before. They are “actually increasing on the order of 10x year on year”, as per Rao. Thus, it is a wall looming in AI. Though Intel is trying its best to tackle this obvious wall, which might otherwise give the industry a severe setback, with extensive research to bring new chip architectures and memory technologies into play, it cannot solve the AI processing problem single-handedly. Rao concluded on a note of requesting the partners in the present competitive scenario.

 

Sourced from: www.datanami.com/2019/11/13/deep-learning-has-hit-a-wall-intels-rao-says

 

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The Future of AI and Machine Learning: What the Experts Say?

The Future of AI and Machine Learning: What the Experts Say?

It’s hard to ignore the growing prowess of AI and machine learning.

Previously, Gartner predicted that AI will become one of the key priorities for more than 30% C-Suite professionals by 2020. Indeed, it’s true; software vendors across the globe are following this new gold rush. For them, data is like new oil. In this blog, we explore the future of this budding technology and gain some new insights and ideas. Let’s see what the heavyweights from the digital industry have to say:

Hyper-targeting and Personalization

Ben Wald, Co-Founder & VP of Solutions Implementation at Very

Though machine learning is a subset of data analysis, it’s rapidly influencing the IoT industry and its respective devices. In the last couple of years, nearly 90% of data was generated through an array of smartphones, watches and cars. These mountains of data help in forming better customer relationships.

How? Using Machine Learning Using Python of course! With this power tool, the corporate houses are trying to understand their target audience and extract crucial information regarding how well they receive their products and related after-sales services. Fine-tuning personalization on a wider scale is the key. Hopefully, soon, we will be able to achieve this goal. We are still in the nascent stage.

Improved Search Engine Experiences

Dorit Zilbershot, Chief Product Officer at Attivio

Did you know that AI algorithms have a massive impact on search engine results?

In the next few years, search engines are expected to enhance user and admin experience: courtesy breakthroughs in neural networks and deep learning technologies. These revolutionary technologies, especially deep learning for computer vision with Python will make sure users enjoy a fabulous searching experience and will deliver highly relevant answers. Currently, we are working on delivering results that are based on user’s query and profile. The process requires a lot of manual configurations and a fundamental understanding of how search engines work. Later, the results will be customized based on individuals’ past preferences, interactions and words used. It will be fun to see how machine learning algorithms transform the dynamo of content publishing and search engines.

Quantum Computing

Matt Reaney, Founder & CEO of Big Cloud

Real and revolutionary, the concept of quantum computing is wreaking havoc in the domain of science and technology. It is the future of machine learning triggering an array of innovations. Integrating quantum computing with machine learning is expected to transform the field triggering accelerated learning, quicker processing and better capabilities. This means the intricate challenges that we can’t solve now could be done in a fraction of time then.

The potential of quantum computing is huge in the future and is likely affect millions of lives, notably in medicine and healthcare industry.

Currently, there are no commercially-built quantum algorithms or hardware available in the market. However, several research facilities and government agencies have been investing in this new field of science of late.

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End Notes

At DexLab Analytics, we love to craft and curate insights from industry pundits, especially when it comes to something as significant as technological innovations that transform lives altogether. Follow us and stay updated!

 


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How progressive is an Artificial Neural Network? Tracking ANNs

How progressive is an Artificial Neural Network? Tracking ANNs

The major improvements that Artificial Neural Network is bringing about in favour of deep learning for computer vision with Python are ground-breaking. Machine vision, in general, is hugely benefitted with the inclusion of the computer vision course Pythonspurred by the all-new technology of Neural Networks. This is by and large a huge advancement in the field of computer science and gives much of an insight into what the future holds for us.

However, along with an array of experiments that are performed day in day out with Neural Network Machine Learning Python, numerous other fields are also likely to be revamped in much the same way. Predicting the weather, studying animals and other critical studies of cosmology are also believed to be easing soon holding the hands of the Artificial Neural Network technology.

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Some Well-known Feats of the Artificial Neural Network

Artificial Neural networks (ANNs) are used in studying the patterns, relationships from the collected data just like humans. Going by the name, ANNs are modelled on the neural networks found in our brains, which are used to infuse the machines with the ability to learn by them. Besides, ANNs have been hugely successful in bringing about the concept of self-driving cars, boosting medical technology and numerous other fields. But, here we lay down some other fields which are soaking in the Artificial Neural Network extensively.

Meteorology

The accurate prediction of hailstorms and providing relevant alerts to the specific areas are expected to boost shortly. With the inclusion of Convolutional neural networks, (CNNs) the study of meteorology is deemed to achieve new heights. Besides, this improved technology would also be capable of identifying the size of the hails during this storm.

Tracking Bird Migration

We are all aware of the phenomenon of migration for the birds. But with the changing age, the routes of the birds are also different from what they used to be. However, if you need to track the migration of the birds, you can opt for the exclusive Neural Networks in Python course.

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Interpreting the Dark Matter

Dark matter has been a topic which remains largely unexplored till date. Nothing beyond the name and the fact that it binds the universe together is brought to light. However, with the marked progress of the premium institutes like the Neural Networks Training in Delhithe dark matter will no longer be a mystery.

 

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Python vs. Scala: Which is Better for Data Analytics?

Python vs. Scala: Which is Better for Data Analytics?

Data Science and Analytics seem to be synonymous to progress as far as the field of computer science is concerned. Now, with the rise of these technologies, everything goes down to the programming languages, which single-handedly help in the growth of them. 

This gave rise to Python, now known as the most significant language in the world of technology. Scala is another versatile language which is not unknown to the researchers and tech geeks. These two languages are the most talked about in the industry today. Nevertheless, both of them are extensively used in data analytics and data science. However, the debate regarding which one to opt for among the two has always been constant. But worry no longer because here we will discuss both of them, in brief, to help you with your choice!

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Python

Python is really one of the most popular languages in the industry. The open-source nature of the language makes it a popular choice for scripting and automation works. 

Besides, Python is powerful, effective, and easy to learn. Moreover, Neural Network Machine learning Python boasts of its efficient high-level data structures and for object-oriented programming.

Advantages

  • Easy to learn and effective too.
  • Exhaustive support from active communities.
  • Python enjoys built-in support for the datatypes.

Disadvantages

  • Your computer might slow down a little when you are running Python. This is in contrast to when you are running other languages like C or Java.

Scala

If you want an object-oriented, functional programming language, then Scala would certainly be your first choice. It was basically built for the Java Virtual Machine (JVM) and remains the most compatible programming language with Java code till date.

Advantages

  • Scala can utilise the majority of the JVM libraries, thus helping them to be embedded in the enterprise code.
  • It shares an array of readable syntax features of the popular languages, like Ruby.
  • Scala brags about numerous incredible features like string comparison advancements, pattern matching and its likes.

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Disadvantages

  • Scala has a limited number of users in the communities, which encourages lesser interactions and stunted growth.
  • At times the type-information in Scala is really complex to comprehend. This difficulty can be attributed to the functional and object-oriented nature of the language.

We hope that this article helps you to have a brief insight into two of the most demanding programming languages: Python and Scala.

Now, if you want to enrol yourself in Computer vision course Python, you can reach us right at Dexlab Analytics, the most reputable institute for Big Data Analytics. Also, if you are looking for all-inclusive Deep learning for computer vision Course, turn no further than our premium institute to shoot your career up!

 

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Most Demanding Programming Languages for Machine Learning: A Knowhow

Most Demanding Programming Languages for Machine Learning: A Knowhow

Machine Learning is among a handful of technologies which we can see going on for long. It is a process or a technology which applies Artificial Intelligence (AI) to enable the machines/computers to learn things all by them and continue improving them subsequently.

Andrew Ng, a computer scientist from Stanford University, describes Machine Learning as the science which helps the computers to act without any explicit programming.

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This new stream, as we are seeing it now, was originally conceived in the 1950s, however, it was not until the 21st century that Machine Learning started to revolutionise the world.

Several industries have already adopted this ground-breaking technology successfully to ensure the growth of their business. Moreover, this new technology has also boosted the demand for advanced programming languages, which were only rarely pursued earlier.

Here are some of the programming languages which seem quite promising with the rise of Machine Learning:

Python

This high-level programming language dates back to the early 1990s and has been widely popular since then, for Data Science, back-end development and Deep Learning for computer vision with Python. Python for data analysis is regarded as a powerful tool and is actively used in Big Data Technology.

R

R has been developed in the 1990s along with Python and was a part of the GNU project. Ever since it was discovered, R finds its uses extensively in Data Analysis, Machine Learning and the development of Artificial Intelligence. Furthermore, R is revered by the world of statisticians. 

Application to R and Python are effectively used to calculate the Arithmetic mean, Harmonic mean, Geometric Mean, Skewness & Kurtosis. Statistical Application Of R & Python: Know Skewness & Kurtosis And Calculate It Effortlessly shows you the way how.

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JavaScript, C++, Java are some other notable programming languages that are dominant. So, hurry up and join the exclusive computer vision course Python now. With Dexlab Analytics, a formidable institute in the Big Data Analytics industry, you can enroll for our tailor-made Artificial Intelligence course in Delhi with just a click from the comfort of your house.

 

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Statistical Application of R & Python: Know Skewness & Kurtosis and Calculate it Effortlessly

Statistical Application of R & Python: Know Skewness & Kurtosis and Calculate it Effortlessly

This is a blog which shall widen your approach on the Statistical Application using R & Python. You perhaps already have been calculating Geometric Mean using R & Python and are already aware of the Application of Harmonic Mean using R & Python. However, if you are eager to further your knowledge about Skewness & Kurtosis and interested to know of their application using R and Python, then this is the right place.

Skewness:

Skewness is a metric which tells us about the location of my dataset. That is, if you want to know where most of the values are concentrated on an ascending scale.

Skewness is of two kinds: Positive skew and Negative skew. A positively skewed dataset will have most of the values concentrated at the beginning of the scale. Eg: If a woman is asked to rate 100 tinder profiles based on the looks on a scale of 1 – 10, 1 being the ugliest and 10 being the most handsome. Then the resulting ratings will be positively skewed. This is to say that women are harsh critiques of looks.

Now, consider another example: Say if the wealth of the 1% richest people were to be plotted on a scale of say $0 – $200 billion. Then, most of the values will be concentrated at the end of the scale. This will be an example of a negatively skewed dataset.

In essence, skewness is the third central moment about mean and gives us a feel for the location of the data set values. It is recommended to go through STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY to have an understanding of the Central Tendency and its measures. Having no skewness will mean the data set is fairly symmetrical and has a bell shaped curve.

Where n is the sample size, Xi is the ith X value, X is the average and S is the sample standard deviation.  Note the exponent in the summation.  It is “3”.

Kurtosis:

Kurtosis is a statistical measure that’s used to describe, or Skewness, of observed data around the mean, sometimes referred to as the volatility to volatility. Kurtosis is used generally in the statistical field to describe trends in charts. Kurtosis can be present in a chart with fat tails and a low, even distribution, as well as be present in a chart with skinny tails and a distribution concentrated toward the mean.

Kurtosis for a normal distribution is 3.  Most software packages use the formula:


The types of kurtosis are:-


Application:

A person tries to analyze last 12months interest rate of the investment firm to understand the risk factor for the future investment.

The interest rates are:

12.05%, 13%, 11%, 18%, 10%, 11.5%, 15.08%, 21%, 6%, 8%, 13.2%, 7.5%.

Here is the table:

Months

(One Year)

Interest

Rate (%)

April12.05
May13
June11
July18
August10
September11.5
October15.08
November21
December6
January8
February13.2
March7.5


Calculate skewness & Kurtosis in R:

Calculate skewness & Kurtosis in R:
Calculating the Skewness & Kurtosis of interest rate in R, we get the positive skewed value, which is near to 0. The skewness of the interest rate is 0.5585253.

The kurtosis of the interest rate is 2.690519

Kurtosis is less than 3, so this is Platykurtic distribution.

Calculate Skewness & Kurtosis in Python:

Calculate Skewness & Kurtosis in Python:
Calculate Skewness & Kurtosis in Python:
Calculating the Skewness & Kurtosis of interest rate in Python, we get the positive skewed value and near from 0. The skewness of the interest rate is 0.641697.

The kurtosis of the interest rate is 0.241602.

Kurtosis is less than 3, so this is Platykurtic distribution.

Conclusion:

Firstly, according to the output of the data the value is positively skewed(R & Python), positive skewness indicates a distribution with an asymmetric tail extending toward more positive values.

And the kurtosis is less than 3 (R & Python), it is a platykurtic distribution. Positive kurtosis indicates a relatively peaked distribution. And the distribution is light tails.

Secondly, the value of the skewness and kurtosis are different in R and Python, but the actual effects are more or less the same. The results are different because skewness and kurtosis are calculated with different formulae or method for the measurement like Bowley’s measure, Pearson’s(First, Second) measures, Fisher’s measure & Moment’s measure. And different software (ex. R, Python, SAS, Excel etc) using different processes to calculate skewness & kurtosis brings the same ultimate result. The numerical values change only when the numbers are also changed. So, we sometimes get different results.

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There are numerous other blogs that you can follow with Dexlab Analytics. Also, if you want to explore computer vision course Python, neural network machine learning Python and more extensive courses on R & Python, then you can also join us and boost both your passion and career.

 

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A Deeper Understanding of Deep Learning

A Deeper Understanding of Deep Learning

To define Deep Learning, it can be summed up as a machine learning technique used to teach computers all those things which comes to humans quite instinctively. This is a sub-classification of the umbrella term Machine Learning and is based on artificial neural networks.

The technology of driver-less cars, of computers with the knowledge of lampposts and trees as non-living entities and with their discretion of differentiating between a pedestrian and a lamppost, all are being developed from Deep Learning. Besides, the voice assistant you find nowadays, that comes with the smartphones, tablets, TVs and hands-free electronic gadgets, everything is matured by Deep Learning.

Deep Learning is an immensely effective technique with huge prospective. Thus, Deep Learning is a highly regarded technology and more and more people are looking forward to finding their career in it.

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Deep Learning: The Path of Success

Among the ever-changing technologies, Deep Learning has its path paved to stand strong in the long run. Now, this is possible primarily because of the high accuracy levels that it has reached.

Pin-pointed Accuracy

With the convincing accuracy levels reached, Deep Learning is believed to be steadfast in situations which involves high risks and which calls for the least margin of errors. For example – driver-less cars.

Extensive Library

If you aim Deep Learning for computer vision with Python, you should be ready with enormous information that it can go through and process quite effortlessly, hence, putting forth an all-inclusive library to be used in real-time. For instance, millions of images, days of video and data should be fed to the system going forward to develop the technology of the driverless car.

Powerful Computing

If we talk about the power that Deep Learning needs, it is astonishingly unreal, the amount of power that this technology expects to perform in its optimum. None other than immensely powerful GPUs are used to get the best results.

As Deep Learning is quite a new thing, unknown in most of its dimensions, here are a few of the fields which have already absorbed or are trying to infuse Deep Learning in constructively.

  • Automobiles – As we have already mentioned that the automobile industry has already taken Deep Learning quite seriously and is effective moving in the direction, where, soon we would witness cars without any human drivers.
  • Defence and Aerospace – Deep learning is constantly taken into account when determining the objects that the satellites bring us. Via Deep Learning we can be sure of the areas/objects in the space. Furthermore, whether a particular zone is fit for the soldiers or not, can also be easily determined by Deep Learning.
  • Pharmacy – Deep Learning is highly significant even in modern medical science. For example, this technology is used to detect cancerous cells.

Deep Learning and AI using Python

With these being said, Deep Learning is simply superb in how it has performed still and the promise that it is showing to be on par with the age. Therefore, if you are seeking for the Deep learning for computer vision course, you can simply avail of Deep Learning for computer vision Training Center in Delhi NCR.

 

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