Why Python for Machine Learning? – Everything You Need Know!
Wondering what is Python used for? Python has been appealing to many developers as it’s easy to learn.
Python code is understandable by humans, which makes it easy to build models for machine learning.
Python has long enjoyed growing popularity in various spectrum of software development – process automation, web development, scripting, and general applications. It is a popular programming language because of its ease of use, simplicity, open-source licensing and accessibility. And, this language has been used to develop an array of applications – Desktop GUI based applications to mathematics programs to science, Machine learning, and other big data computing systems.
It was formed by Guido van Rossum, who is known as the godfather of Python. Python offers a concise and readable code. Python offers succinct and readable code. Machine learning is nothing, but to identify patterns in your data.
Why Python for Machine Learning Applications with Python
Online Customer Support
Have you ever noticed that educators or shopping platform, often have the option of live chat that just pops out everything you visit it? Surely, you would have!
Some websites use a Chabot instead to pull information to the website and try to answer all queries. Just imagine, if your queries are been left unresolved or unanswered (as the customer), it can get frustrating user experience. Machine learning has given solutions to all these obstacles. With the help of Python Machine learning and Natural Language processing, concepts have made these situations sorted. Python has been adding to this.
Several organizations have been using video surveillance for an array of tasks, such as identifying threats of violence, detecting intruders and catching criminals, etc. All of this would have not been possible manually, however. That would have been quite time-consuming. This aspect has been made possible for the existence of Python Machine learning algorithms. It has been used for the software that is put inside these surveillance cameras. Product recommendation is one such feature that has been used by an assortment of shopping sites such as Amazon, Flipkart, Myntra and many more.
Cyber Security (Captchas)
“I am not a robot” – Isn’t this sentence sounds familiar? You must have encountered this button when a website suspects it is dealing with a machine, rather than a human. Do you remember filling these irritating CAPTCHA – Completely Automated Public Turing test? You are been asked to identify trees, bridges, crosswalks, and traffic lights, and all sorts of objectives to prove that we are, indeed human.
If you are thinking why websites are making things complicated for you? The answer to this lies with Machine Learning.
Detecting Fraud in Banking
Bank or credit card fraud is not alien words. It is a painful experience to go through all these frauds. The distress of the fraud is aggravated by the amount of paperwork the bank asks you to fill out.
Machine learning is solving distinct layers of this process. From fraud identifications to prevention, machine learning algorithms are redefining the way banks work to improvise customer’s experience. The latest machine learning solutions come up in action; this has made things sorted for both banks and customers.
Having said that machine learning has surely helped streamline the process? These algorithms can identify fraudulent transactions and flag them so the bank can connect with the customers and make the transaction accomplished.
A good example is to look at the spending patterns of consumers. If a purchase does not match this pattern, the algorithms alert the bank and put the transaction on hold.
A large about of data is been used on data tables – data frames in Python. And, this information is been used for calculating several other services and factors.
Another use of Python is a personalized case of recommendation engines. This one is targeted specifically for the banking domain. Personalized banking experience – banks targeting customer micro-segments and customizing offers to them – is a blessing for customers.
Small information can make a huge difference. The ability of traffic alerts on Google has made a life of people quite amazing and easy. The advanced application of a python machine learning approach has changed everything.
Machine learning approach has resulted in improvising social media’s features to create attractive and splendid features. Social media is using the machine learning approach to create splendid and attractive features to create a better and engaging experience for users.
If you are a passionate social media user, you would easily recall “People You May Know”. It uses machine learning to monitor your activity – who visited you are, people who sent a friend request, who accepted your friend request, people you tag, and much more. With this, Facebook or Instagram can provide you with a rich experience on its platform so you will use it frequently.
Here is a very simple concept where Machine Learning is dominating the most popular social media app Facebook:
Well, by suggesting various friend suggestions. Based on experience, Facebook keeps on noticing the profile you would like to connect to that you have ever visited. When you upload a picture with some friend, Facebook instantly identifies the person after going through your friend list.
All social media platforms, be it Facebook or Instagram, continuously notices your activities like with whom you can chat, your likes or dislikes, study place, people you following. And, machine learning always acts on based on your activities. So, it does give you a suggestion based on your activities.
Transportation and Commuting
If you have ever used an app to book your drive, you are already using Python and machine learning to an extent. It offers a customized application that is unique to you. Automatically perceives your location and offers options to either go office or home any other commonplace based on your history and patterns. Do you remember getting suggestions on your app is a part of Machine Learning?
It uses a machine-learning algorithm covered on top of historic trip data to construct a more precise ETA prediction. Implementation of Machine Learning has resulted in 26% of accuracy in terms of correct delivery and pickup.
Have you ever thought about, why Uber shows you a high price in a crowded area or during peak hours? Uber is counted as the biggest users of Machine Learning application that comes in the form of surge pricing, a machine learning model nicknamed as “Geosurge”.
Virtual Personal Assistants
This is the latest application of Python. In the machine learning application, this system acts as follows: a machine learning-based system takes processes and input and gives the resultant output.
The machine learning approach is important as they act based on the experience? There are several facilities like face detection are often something you can see on the social media platform. When we want to tag a photo, Facebook automatically suggests the user’s name or profile owners to tags.
Online Video Streaming
Netflix employs data science to appreciate user behavioral drivers and viewing patterns. This helps Netflix to comprehend user likes or dislikes and forecast and recommend relevant items to view.
Applications like Walmart, Amazon, and Target are profoundly working around data science, machine learning and Python to understand users’ preferences. This supports both forecasting demands to boost inventory management and to suggest relevant products to online users or via email marketing.
Spam programs have been using python data science and machine learning algorithms to prevent and detect spam emails.
Self Driving Cars
Self-driving cars are like a fascination comes true. These cars receive data on nearby objects and their speeds and sizes via sensors. Based on how they perform, it categorizes objects like pedestrians, cyclists and other cars among others. It employs this data to evaluate stored maps to current conditions. Such cars make use of Machine Vision algorithms.
The language barrier is a problem for many. Communicating without worrying about the language barrier can be a piece of relief. Language recognition is the process of identifying the type of language. Apache Tika, Apache OpenNLP is the language discovering software. There are several approached to identify the language. Among these, the machine learning notion is proficient.
Speech recognition is the simple procedure of transferring verbal words into text. It is called computer speech recognition, automatic speech recognition, or speech to text. This field is benefited from the advancement of the machine learning approach and big data. Speech reorganization Python has turned out to be a life-changing experience for many.
Did you know that machine learning powers most of the features on your Smartphone? From the voice assistant that set your alarm and finds you the best restaurants to be simple use case of unlocking your phone via facial recognition – machine learning is truly embedded in all smart devices.
That is right. From the voice assistant that finds the nearest café around you, texting someone, to unlocking your phone via facial recognition. Here is a list of popular applications of Machine Learning:
- Speech Recognition
- Speech to Text Conversion
- Natural Language Processing
- Text to Speech Conversion
They are ubiquitous right now. You must have used the below popular voice assistants
- Amazon’s Alexa
- Microsoft’s Cortana
- Google Assistant
- Apple’s Siri
- Google Duplex
- Samsung’s Bixby
And so on. All these voice assistants are powered by the machine learning algorithm. It identifies speech by using Natural Language Processing (NLP), convert them into numbers using machine learning and formulate a response consequently.
Python is one of the major languages that are used for the development of both web applications and desktop. It takes care of common programming tasks that are leading towards a better way of living. Python is simple to learn and easy to use. Python applications can boost their speed by just maintaining the code and using custom runtime. It offers a hike in productivity, which makes the first choice of developers.
In other words, it is a powerful and flexible language with several paradigms and mechanisms that can boost productivity. Like any software tool and language, though having a limited understanding or appreciation of its capabilities can sometimes be more of an impediment than a benefit, leaving one in the well-known state of “knowing enough to be dangerous.”