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درسنامه:
AUGUST 12, 2020/#PYTHON
Writer:Estefania Cassingena Navone
Hi! Please take a moment to think about this question:
How is Python applied in real-world scenarios?
If you are learning Python and you want to know the answer, then this article is for you.
Having a clear idea of the applications and vast potential of this programming language will give you the motivation that you will need throughout your journey.
Let's begin! ✨
Python is used in virtually every industry and scientific field that you can imagine, including:
This article covers a wide range of applications of this programming language in these industries with examples, use cases, and Python libraries. Let's start with the applications of Python in data science.
Perhaps one of the most popular applications of Python is data science. The power of the Python libraries developed for data analysis and visualization is amazing. Let's see why.
With a Python data visualization library, you can create a wide variety of plots and visual representations, such as:
You can add text, labels, annotations, color, shapes, collections, animations, and interactivity to your plots depending on the package or library that you choose to work with.
? Tip: You can see some examples of data visualizations generated with Python in the image above.
Let's see some of the most popular packages and libraries to work with Python in data science:
If you want to learn data analysis and visualization using Python, Jupyter Notebooks, Numpy, Pandas, CSV files, data frames, and more, you can start your journey with freeCodeCamp's free Data Analysis with Python Certification:
During the certification, you work on and complete these projects:
freeCodeCamp's YouTube channel also has these great free tutorials to get you started:
In addition, these are helpful resources if you want to learn how to work with these libraries:
Python is an essential tool for every developer who wants to enter the fascinating area of Machine Learning. Let's see a brief introduction to Machine Learning.
Machine Learning is an area of Computer Science that creates systems that are able to learn on their own.
This type of system uses algorithms that are continuously improved based on input data that helps the system "learn". It learns how to respond autonomously to new scenarios by generating an appropriate output in new scenarios based on previous knowledge.
One of the most amazing things about these systems is that they are continually refined.
They are not like the programs that we typically write in a Python script where we specify every possible action that the program can take. In Machine Learning, the system is trained to "think" and make decisions based on previous knowledge.
This is why we say that machines "learn" from the data.
? Tip: This is an interesting talk by Google: Machine Learning Zero to Hero (Google I/O'19).
Neural networks are the processing units of the system. They try to simulate a real network of neurons of the brain. They virtual "neurons" receive input, learn how to process that input, and generate an output based on their previous knowledge.
This is very similar to what out brain does every single moment of every single day.
Thanks to neural networks, a Machine Learning algorithm can learn how to predict the expected output from a given input based on previous knowledge.
For example, when you see recommended videos on YouTube, those recommendations were generated by neural networks that predict what videos you might like to watch based on your previous patterns. Amazing, right?
? Tip: This is an interesting article by Google if you would like to read more about this YouTube example.
I'm sure you must be asking: what is the role of Python in this area? It is one of the most popular and powerful tools used to program this type of system.
One of the most popular libraries used by developers around the world to work with Python applied to Machine Learning is TensorFlow. It's a free open-source library developed by the Google Brain Team. This library is used for research and production at Google.
According to Jeff Dean, the lead of Google's Artificial Intelligence division:
Today it is used heavily in our speech recognition systems, in a new Google Photos product, Gmail, and Google Search. (source)
The best part is that developers all over the world can use this library to tackle real-world problems.
? Tip: This is a great video about TensorFlow made by Google.
These are two other popular Python libraries used for Machine Learning:
The potential of Machine Learning is really endless. It can be applied to virtually any area and context that you can think of. If the task requires learning from patterns and predicting output, then a Machine Learning model can definitely help.
For example, to give you an idea of the type of projects that you can create, freeCodeCamp's curriculum includes a free Machine Learning with Python Certification:
During the certification, you work on and complete these projects:
You can find more examples of the applications of Machine Learning in Kaggle, an "online community of data scientists and machine learning practitioners" owned by Google.
In this platform, you can practice your Python and Machine Learning skills by working on projects and participating in competitions.
To give you an idea of the type of projects that you can tackle with Machine Learning, previous competitions in Kaggle include:
As you can see, just in this short list of projects, the applications range from medicine to business, from biology to risk detection, and from fraud detection to image processing. The possibilities are truly endless when you tackle real-world problems using Machine Learning.
freeCodeCamp's YouTube channel has these helpful tutorials to get you started with Machine Learning in Python:
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