The hello world of machine learning and computational neural networks usually start with a technique called regression that comes in statistics. Whether you are a seasoned developer or even a mathematician, having been reminded of the overall concept of regression before we move on to polynomial regression would be the ideal approach to take.
We will get ourselves familiar with the concept of regression with a simple example. Let’s say we are looking to buy houses from the same city. Houses of this city are mainly dependent on how many square feet it has. We are concerned with the price and therefore, we need to know whether we are paying the best price for the house we are going to be purchasing. We have the house pricing information with us from one of our friendly realtors. What we are going to do is find a connection between the square feet and the price of the house, so that we can determine whether we are buying the right property. Let’s look at the data.
sqr_feet = [1350, 1472, 1548, 1993, 1998, 2100, 2450] price = [643000, 679000, 702500, 759000, 760000, 795000, 855000]
Each of the data in these lists, correspond to the same elements at the same index. We can plot these values using Matplotlib to get an idea about how they look like.
import matplotlib.pyplot as plt fig=plt.figure() axes=fig.add_axes([0,0,1,1]) axes.scatter(sqr_feet, price, color='r') plt.ylabel('Price(USD)') plt.xlabel('Area(sqft)') plt.show()
This is a scatter plot. However, we can also connect the lines to get a better idea on how the points have been distributed.
plt.plot(sqr_feet, price) plt.ylabel('Price(USD)') plt.xlabel('Area(sqft)') plt.show()
We can see that the price has been increased proportionally with the available floor area of the house. Therefore, we can now come up with an equation to calculate a ballpark value. That way, if we are given a new house and its floor area, we can see whether we are paying a reasonable amount of not. Since this looks like it can be modeled using a straight-line, we can choose an equation after y = mx + c. However, it is not that easy as there are so many straight-lines that can take the shape of this curve. This is where regression comes in.
Regression can be used to statistically determine the gradient and the intercept of the potential straight-line using methods such as the least-squares method that is based on finding the minimum sum of the square residuals. The general idea is that we can use regression to determine the relationship between one dependent and one or more independent variables. In the data science jargon, the dependent variable is also known as y and the independent variables are known as x1, x2, … xi.
Let us use regression and find gradient (m) and intercept © for the above example.
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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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.
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.
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.
Simple to read and compose
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.
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.
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 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.
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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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.
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
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
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
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The course will lead you from beginning level to advance in Python Programming Language. You do not need any prior knowledge on Python or any programming language or even programming to join the course and become an expert on the topic.
The course is begin continuously developing by adding lectures regularly.
Please see the Promo and free sample video to get to know more.
Hope you will enjoy it.
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What will you learn
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