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AI joins the fight against Cancer

AI joins the fight against Cancer

Cancer is the emperor of all maladies. Finding a cure to it is one of the biggest challenges in the world of medicine. More and more men and women, one in five men and one in six women worldwide likely to be afflicted, are falling prey to the malady. It is something that has spurred on the fight against the disease even more intensely.  AI and machine learning has increased the scope of groundbreaking research in the field and it is worth knowing a little about.

One reason why AI, which has made inroads into numerous sectors of the economy, has made immense advancements in the field of medical oncology is the vast amount of data generated during cancer treatment. With the assistance of AI, say scientists, this vast trove of data can be mined and worked to improve methods of diagnosis and preventive cures and treatments.

Detection of Cancer

Machine learning can lead to early detection and timely treatment in many cases. Because cancer is treated in stages, unlike other diseases, machine learning can come in handy when it comes to detection of precancerous lesions in tissues.

AI utilizing tools can assist radiologists in graphically and visually studying images by revealing suspicious lesions. This process not only reduces the work load of radiologists but it also makes possible the detection of miniscule lesions which could otherwise be overlooked.

Detection of Breast Cancer

“DeepMind and Google Health collaborated to develop a new AI system that helps in detecting breast cancer accurately at a nascent stage. Being the most common cancer in women, breast cancer, has seen an alarming rise over the past few years. Though early detection can improve a patient’s prognosis significantly, mammography, which is the best screening test currently available, is not entirely error-proof”, says a report.

To correct this, researchers at DeepMind and Google Health designed an algorithm on mammogram images and noticed AI systems reduced the recurrence of errors. They discovered that AI systems functioned better than human radiologists. A few startups in India are also laboring in the arena of cancer detection.

Predicting Cancer Evolution

Besides detection, AI is useful in the treatment of cancer as well. It is critical to the survival of patients in that it is used to predict growth and evolution of cancers which could help doctors prepare a treatment plan and save lives.

Identifying Effective Treatments

AI can play a significant role in the overall treatment of the patient, especially precision medicine which is the administering of personalized medicine from a pool of generic medication beneficial to the patient. AI can also be used to design new drugs.

Thus, AI has created a huge potential for changing the mode of treatment of cancer patients. According to the report, Exscientia is the first company, globally, to have overtaken conventional drug designing processes by automating the whole process using AI. Another company is trying to do the same in Bangalore.

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It is no surprise then that AI is being even more widely adopted across sectors of healthcare and medicine. More and more professionals, the world over, are enrolling in courses teaching AI, deep learning and machine learning. For the best such institute in India, or for the best artificial intelligence training institute in Gurgaon, do not forget to visit the DexLab website today.

 

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Budget 2020 Focuses on Artificial Intelligence in a Bid to Build Digital India

Budget 2020 Focuses on Artificial Intelligence in a Bid to Build Digital India

The Indian technology industry has welcomed the 2020 budget for its outreach to the sector, specially the Rs 8000 crore mission for the next five years on Quantum Computing. The budget has been praised in general for its noteworthy allocation of funds for farm, infrastructure and healthcare to revive growth across sectors in the country.

According to an Economic Times report, Debjani Ghosh, President, NASSCOM, reacting to the budget, said, “Budget 2020 and the finance minister’s speech has well-articulated India’s vision on not just being a leading provider of digital solutions, but one where technology is the bedrock of development and growth’.

Industry insiders lauded the budget for the allocation on Quantum Computing, the policy outline for the private sector to construct data center parks and the abolition of the Dividend Distribution Tax. The abolition of the Tax had been a long standing demand of the industry and the move has been welcomed. The building of data parks will help retain data within the country, industry experts said.

Moreover, while announcing the budget this year, Finance Minister Nirmala Sitharaman spelt out the government’s intentions of utilizing, more intensely, technology, specially artificial intelligence and machine learning.

These will be used for the purposes of monitoring economic data, preventing diseases and facilitating healthcare systems under Ayushman Bharat, guarding intellectual property rights, enhancing and improving agricultural systems and sea ports and delivery of government services.

Governments the world over have been emphasising the deployment of AI for digital governance and research. As per reports, the US government plans and intends to spend nearly 1 billion US dollars on AI-related research and development this year.

The Indian government has also planned to make available digital connectivity to citizens at the gram panchayat level under its ambitious Digital India drive with a focus on carrying forward the benefits and advantages of a digital revolution by utilizing technology to the fullest. One lakh gram panchayats will be covered under the Rs 6000 crore Bharat Net project wherein fibre connectivity will be made available to households.  

“While the government had previously set up a national portal for AI research and development, in the latest announcement, the government has continued to offer its support for tech advancements. We appreciate the government’s emphasis on promoting cutting-edge technologies in India,” Atul Rai, co-founder & CEO of Staqu said in a statement, according to a report by Live Mint.

The Finance Minister also put forward a plan to give a fillip to manufacturing of mobiles, semiconductor packaging and electronic equipment. She iterated that there will be a cost-benefit to electronics manufacturing in India.

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Thus, this article shows how much the government of India is concentrating on artificial intelligence and machine learning with a push towards digital governance. It shows that the government is recognising the need to capitalise on the “new oil” that is data, as the saying goes. So it is no surprise then that more and more professionals are opting for Machine Learning Course in India and artificial intelligence certification in delhi ncr. DexLab Analytics focuses on these technologies to train and skill professionals who want to increase their knowledge base in a digital first economy.

 

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8 Skills a Python Programmer Should Master

8 Skills a Python Programmer Should Master

Python has become the lingua franca of the computing world. It has come to become the most sought after programming language for deep learning, machine learning and artificial intelligence. It is a favourite with programmers because it is easy to understand and learn and it achieves a lot more in terms of productivity as compared to other languages.

Python is a dynamic, high-level, general-purpose programming language that is useful for developing desktop, web and mobile applications that can also be used for complex scientific and numeric applications, data science, AI etc. Python focuses a lot on code readability.

From web and game development to machine learning, from AI to scientific computing and academic research, Data science and analysis, python is regarded as the real deal. Python is useful in domains like finance, social media, biotech etc. Developing large software applications in Python is also simpler due to its large amount of available libraries.

The Python developer usually deals with backend components, apps connection with third-party web services and giving support to frontend developers in web applications. Of course, one might create applications with use of different languages but pretty often Python is the language chosen for it – and there are several reasons for that.

In this article, we will walk through a structured approach to top 8 skills required to become a Python Developer. These skills are:

  • Core Python
  • Good grasp of Web Frameworks
  • Front-End Technologies
  • Data Science
  • Machine Learning and AI
  • Python Libraries
  • Multi-Process Architecture
  • Communication Skills

Core Python

This is the foundation of any Python developer. If one wants to achieve success in this career, he/she needs to understand the core python concepts. These include the following:

  • Iterators
  • Data Structures
  • Generators
  • OOPs concepts
  • Exception Handling
  • File handling concepts
  • Variables and data types

However, learning the core language (as mentioned above) is only the first step in mastering this language and becoming a successful Python developer.

Good grasp of Web Frameworks

By automating the implementation of redundant tasks, frameworks cut development time and enable developers to focus greatly on application logic rather than routine elements.

Because it is one of the leading programming languages, there is no scarcity of frameworks for Python. Different frameworks have their own set of advantages and issues. Hence, the selection needs to be made on the basis of project requirements and developer preference. There are primarily three types of Python frameworks, namely full-stack, micro-framework, and asynchronous.

A good Python web developer has incredible honing over either of the two web frameworks Django or Flask or both. Django is a high-level Python Web Framework that encourages a good, clean and pragmatic design and Flask is also widely used Python micro web framework.

Front-End Technologies (JavaScript, CSS3, HTML5)

Sometimes, Python developers must work with the frontend team to match together the server-side and the client-side. This means Python developers need a basic understanding of how the frontend works, what’s possible and what’s not, and how the application will appear.

While there is likely a UX team, SCRUM master, and project or product manager to coordinate the workflow, it’s still good to have a basic understanding of front-end tasks.

Data Science

Data science offers a world of new opportunities. Being a Python developer, there are several prerequisites you need to know starting with things you learn in high school mathematics, such as statistics, probability, etc. Some of the other parts of data science you need to understand, and use include SQL knowledge; the use of Python packages, data wrangling and data cleanup, analysis of data, and visualization of data.

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning (as well as Deep Learning) are constantly growing. Python is the perfect programming language which is used in all the frameworks of Machine Learning and Deep Learning. This will be a huge plus for someone if he/she knows about this domain. If someone is into data science, then definitely digging in the Machine Learning topic would be a great idea.

Python Libraries

Python libraries certainly deserve a place in every Python Developer’s toolbox. Python has a massive collection of libraries, both native and third-party libraries. With so many Python libraries out there, though, it’s no surprise that some don’t get all the attention they deserve. Plus, programmers who work exclusively in one domain don’t always know about the goodies available to them for other kinds of work.

Python libraries are extensively used in simplifying everything from file system access, database programming, and working with cloud services to building lightweight web apps, creating GUIs, and working with images, ebooks, and Word files—and much more.

Multiprocessing Architecture

Multiprocessing refers to the ability of a system to support more than one processor at the same time. Applications in a multiprocessing system are broken to smaller routines that run independently. The operating system allocates these threads to the processors improving performance of the system. As a Python-Developer one should definitely know about the MVC (Model View Controller) and MVT (Model View Template) Architecture. Once you understand the Multi-Processing Architecture you can solve issues related to the core framework etc.

Communication Skills

In best software development firms the teams are made out of amazing programmers which work together to achieve the final goal – no matter if it means to finish the project, to create a new app or maybe to help a startup. However, working in a team means that a developer has to communicate well – not only to get the stuff done but also to keep the documentation clear so others can easily read and follow the thinking path to fully understand the idea.

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Conclusion

In this write-up, we have elaborated on the top skills one needs to have to be a successful Python Developer. One must have a working knowledge of Core Python and a good grasp of Web Frameworks, Front-End Technologies, Data Science, Machine Learning and AI, Python Libraries, Multi-Process Architecture and Communication skills. Though there are a few more skills not listed in this blog, one can achieve success in developing large software applications by mastering all the above skills only.

As delineated in the article, Python is the new rage in the computing world. And it is no surprise then that more and more professionals are opting to take up courses teaching Machine learning using Python and python for data analysis.

 

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Artificial Intelligence and IT Operations: A new algorithm

Artificial Intelligence and IT Operations: A new algorithm

Artificial intelligence used to automate IT operations has begun being widely termed as AIOps, a new algorithm of deep learning put to use in the field of information technology to speed up businesses and response timings to incidents occurred. It is the new rage after AI itself. And, justifiably so.

Information technology is constantly in flux, changing every minute. To keep up with it, old systems will not work. What is needed for its management is smart and fast computer programs which can keep learning and re-use learnt skills with more and more operations carried out. Trends show that worldwide spending on AI systems will hit the $77.6 billion mark in 2020, three times the amount forecasted for 2018, the IDC revealed recently.

Trends show AIOps will take centre stage when it comes to problem solving and accelerating detection of incidents and remediation.  As AIOps tools mature, IT systems will be able to work on and process a larger variety of data types in a faster and better manner, enhancing performance for more specific jobs assigned to it.

AI experts in the field say AIOps will be used to enhance and increase natural language processing, analysis of the root cause of problems, detection of anomalies, and correlation and analysis of events, among other IT functions, thus giving IT operations professionals greater control over their systems.

AI technology can help improve efficiency in vital industries like healthcare and agriculture. A case in point is the development of the Chatbot which has come to contextualize and give more intuitive and human like responses to customers.

In 2020, it is expected of IT firms to introduce data-source-agnostic solutions. This new tool will be a big boost for the industry as the more varied and variegated the data fed into an AIOps platform, the greater the insights and value the algorithms can come up with. This will directly translate to mean users can determine, more accurately, issues, foresee impacts and fathom how change can affect business-critical activities.

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One drawback of the current AIOps systems are that they take a lot of time on-boarding and its takes time training company professionals in the use of the AI software as well as feeding the software with vast amounts of data and information. This is a challenge that will have to be met in the coming few years as more and more of the IT world is adopting AI in its systems.

The AIOps is being used increasingly in Indian IT firms as well, they recognizing the need to embrace the AI juggernaut the world has bowed down to. For artificial intelligence certification in Delhi NCR one can sign up for a course at DexLab Analytics which might have the perfect machine Learning course in India for you.

 

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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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Automation is to Highly Impact the Knowledge Workers

Automation is to Highly Impact the Knowledge Workers

Automation will mainly target the knowledge workers, who are highly paid and educated and involved in thinking and analytical jobs.

The robot revolution is anticipated for quite some time now and with the ongoing advancements in Machine Learning, Artificial Intelligence and Data Science, the future is near. However, it is also one of the most dreaded events for the workers going forward, who would be vulnerable to losing their respective jobs.

Going back to the 2017 McKinsey study, around 50% of the jobs in the manufacturing industries are automatable using the latest technology. However, according to the latest report, the white-collar workers, who are well-read and engaged in thinking and analytical jobs, are more likely to suffer the most.

According to a new study conducted by Michael Webb, Stanford University Economist, the powerful technologies of computer science like Artificial Intelligence and Machine Learning, which can make human-like decisions and grow using real-time data, will eventually target the white-collar workers. Artificial Intelligence has already made marked intrusions in the white-collar jobs, like telemarketing, which are primarily overseen by the bots. However, with the tireless efforts of the Data Scientists, along with the expansion of the Machine Learning course in India, it is believed to oust the majority of the knowledge workers, like chemical engineers, market researchers, market analysts, physicists, librarians and more.

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The new research focuses on the intersecting subject-noun pairs in AI patents and job descriptions to find out the jobs that will be heavily affected by the Ai technology. For example, the job descriptions of market research analysts comprise of “data analysis”, “identifying markets” and “track market trends”, which are in fact, all covered by the AI patents that are existing. This new study looks far more progressive than the previous ones because it analyzes patents for the technology which are yet to develop completely.

With the rising trends of Data Science and Machine Learning, Artificial Intelligence has really come a long way from what an imaginary concept. Thus, courses like Machine Learning Using Python and Python for Data Analysis, are in heavy demands. 

 

This article has been sourced fromwww.vox.com/recode/2019/11/20/20964487/white-collar-automation-risk-stanford-brookings

 

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Statistical Application in R & Python: Negative Binomial Distribution

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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How to Structure Python Programs? An Extensive Guide

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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