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# Maximum Likelihood Estimation - Python Guide - Analytics India Magazine

Data is everywhere. The present human lifestyle relies heavily on data. Machine learning is a huge domain that strives hard continuously to make great things out of the largely available data. With data in hand, a machine learning algorithm tries to find the pattern or the distribution of that data. Machine learning algorithms are usually defined and derived in a pattern-specific or a distribution-specific manner. For instance, Logistic Regression is a traditional machine learning algorithm meant specifically for a binary classification problem. Linear Regression is a traditional machine learning algorithm meant for the data that is linearly distributed in a multi-dimensional space. One specific algorithm cannot be applied for a problem of different nature.

To this end, Maximum Likelihood Estimation, simply known as MLE, is a traditional probabilistic approach that can be applied to data belonging to any distribution, i.e., Normal, Poisson, Bernoulli, etc. With prior assumption or knowledge about the data distribution, Maximum Likelihood Estimation helps find the most likely-to-occur distribution parameters. For instance, let us say we have data that is assumed to be normally distributed, but we do not know its mean and standard deviation parameters. Maximum Likelihood Estimation iteratively searches the most likely mean and standard deviation that could have generated the distribution. Moreover, Maximum Likelihood Estimation can be applied to both regression and classification problems.

Therefore, Maximum Likelihood Estimation is simply an optimization algorithm that searches for the most suitable parameters. Since we know the data distribution a priori, the algorithm attempts iteratively to find its pattern. The approach is much generalized, so that it is important to devise a user-defined Python function that solves the particular machine learning problem.

#developers corner #likelihood #log likelihood #maximum likelihood estimation #mle #probability distribution #python #regression #statistics

1622472540

## Maximum Likelihood Estimation - Python Guide - Analytics India Magazine

Data is everywhere. The present human lifestyle relies heavily on data. Machine learning is a huge domain that strives hard continuously to make great things out of the largely available data. With data in hand, a machine learning algorithm tries to find the pattern or the distribution of that data. Machine learning algorithms are usually defined and derived in a pattern-specific or a distribution-specific manner. For instance, Logistic Regression is a traditional machine learning algorithm meant specifically for a binary classification problem. Linear Regression is a traditional machine learning algorithm meant for the data that is linearly distributed in a multi-dimensional space. One specific algorithm cannot be applied for a problem of different nature.

To this end, Maximum Likelihood Estimation, simply known as MLE, is a traditional probabilistic approach that can be applied to data belonging to any distribution, i.e., Normal, Poisson, Bernoulli, etc. With prior assumption or knowledge about the data distribution, Maximum Likelihood Estimation helps find the most likely-to-occur distribution parameters. For instance, let us say we have data that is assumed to be normally distributed, but we do not know its mean and standard deviation parameters. Maximum Likelihood Estimation iteratively searches the most likely mean and standard deviation that could have generated the distribution. Moreover, Maximum Likelihood Estimation can be applied to both regression and classification problems.

Therefore, Maximum Likelihood Estimation is simply an optimization algorithm that searches for the most suitable parameters. Since we know the data distribution a priori, the algorithm attempts iteratively to find its pattern. The approach is much generalized, so that it is important to devise a user-defined Python function that solves the particular machine learning problem.

#developers corner #likelihood #log likelihood #maximum likelihood estimation #mle #probability distribution #python #regression #statistics

1624442510

## Hire Python Django Developers | Dedicated Python Programmers India

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1626775355

## Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.

## 5 Reasons to Utilize Python for Programming Web Apps

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.

Utilized by the best

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.

Massive community support

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.

Progressive applications

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

### Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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## Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

### Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

``````>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName
>>> print(FirstName, LastName)
('Jordan', 'kalebu')
``````

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1602666000

## How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

### Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

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