Understanding Python for Machine Learning – Part 1 (Data Types in Python)

Understanding Python For Machine Learning

Machine learning has become one of the most sought after fields in the current times. With advancements in both software and hardware, building complex mathematical and computational heavy models have become easier and quicker. Introduction of TPU (Tensor Processing Units) have further expedited the process of machine learning models. While all the advancements in the field of hardwares have enabled this change, one notable change has been in the use of python for machine learning algorithms. 

Marking a shift from traditionally used language R (which is still preferred by statisticians), python has gained popularity in recent times amongst many python developers and python consultants. Many new 3rd party machine learning tools now provide python support out of the box. Infact, many companies have now started calling themselves python development companies.

Hence, through this series of articles, we will understand python for machine learning and how python consultants can leverage python to build robust machine learning models. As a first article in this series, we will understand different data types in python and their syntax in python.

Let us begin!

There are majorly 7 types of data in python

TextString (str)
NumericInt, Float, Complex
SequenceList, Tuple, Range
MappingDictionary
SetSet, Frozenset
BooleanBool (True/False)
Binarybytes, bytearray, memoryview

We will now understand each of the above types in detail.

  1. Text

Strings in python are a stream of characters, contiguous in nature. They are often represented in between single quotes. Strings allow slicing on them, that is, we can use parts of a string to extract substring out of it. One important thing to note is that unlike R, indexes in python start from 0. 

Syntax and Output:

We can also use + operator to concatenate the strings.

  1. Numeric

This type of data includes use of integer (signed), float (real values of floating point) etc. These are number inputs. It is important to note that all integer types in Python 3 are long. Hence, there is no separate long type in Python 3.

Syntax and Output:

  1. Sequence

This is probably the most frequently used data type in Python. It finds several applications at different places. The basic data types like numeric and text are also used inside the sequence data type. That is, a sequence data type can contain data of type integer and float. To understand the concept better, let us understand what is a list, tuple and a range. 

  1. List

As mentioned above, since sequence data type can contain other data types as well, it is also called a compound data type. Lists are very versatile. List stores a sequence of values. It can be accessed using ‘[ ]’  and can be manipulated using operators like ‘+ and *’.

Syntax and Output:

  1. Tuple

Similar to lists, except the values in the tuple cannot be changed/updated. The size of the tuple can also not be changed once created. In other words, tuples can be thought of as “read-only lists”. One change also lies in the syntax of tuple and list. Tuples are formed using parentheses ( ‘( )’ )

Syntax and Output:

  1. Range

Range is often used in loops, to define a set of numbers over which the counter will iterate. It is used to create a sequence of numbers, with start(included) and end(not included) as its parameters. 

Syntax and Output:

  1. Mapping (Dictionaries)

Dictionaries are key-value pairs in python. Like hashtables, we have a key which corresponds to a particular value. When we want to access the value, we use the key corresponding to the value and the value is retrieved. 

The key is generally an integer or a string, though they can be any of the python data types. The value can be any python object.

Syntax and Output:

With this, we come to the end of this article. In this article, we learnt how python developers and python consultants can use different data types in different scenarios. A python development company, which is planning to expand into machine learning and AI, should be able to understand and deliver on this basic premise of data types in python. This base can then be used to build great machine learning models.

As promised, as a multi-series tutorial, we will cover “Linear Regression in Python” in the next post.

Until then, bye bye!

Object Recognition Software

Object Recognition Software

Do you know the difference between cat and cactus ? There are chances that you may or your team may , but this is not the most efficient way you utilise your time . That is where object recognition technology  are acting as a time saver for many business organisation . It can also facilitate add on functionality for your customer , specially when you are dealing in app which is concern about image categorisation and content management .\

What Is Object Recognition ? 

In simple words , we can define object recognition as technology which has capability to recognise objects in picture or videos with the help of AI software . If you have ever used Google Lens or have used Google Photos in your mobile phone , then tan tana… !! You are already familiar with object recognition .
Right from identification of cover of book to identification of plans in wild , object recognition technology has come a long way . 
As it is new technology , many business owners really did not know how this technology exactly works and hence had made an assumption that the access to this technology is only possible by heavily investing on this technology . However this is not the case with object recognition . In fact it has tools which can easily be incorporated by even small and mid sized business in their software .
These tools facilities identification of single or multiple objects in photoset or image . Although Facial recognition is also one type of object recognition technology , but is always treated separately due to its complexity in nature and legal concerns .

How Does Object Recognition Work ? 

Before proceeding with how object recognition works , let’s quickly look at the difference between object detection and object recognition . Object detection is concern about locating object in image , wherein object recognition is being used to identify object in given image .

Object recognition is being used to find out more specific categories of objects , such as dog or cat . In some cases , object recognition can even be performed without using AI technology . In those cases template matching or image segmentation is being used to recognise objects .

Even more advance object recognition can be done by following one of the two approaches : machine learning and deep learning , both of the technology have their own pros and cons .

Also Read : What Is Machine Learning & Its Applications ? 

Machine Learning 

We can define machine learning is training of software to perform certain activity , as in this case recognition objects in photo . You have to provide software set of images and then list feature which can later be used in recognition of object .
Major advantage of machine learning is it necessarily does not require large datasets or computing power , and hence cost of software development is always low .
Disadvantage of this technology is it require more efforts of your team to set up completely . If you have inserted limited photos to include in your trading sets , then your software might not able to identify with accuracy as it has not access to broader set of images .

Deep Learning 

Deep learning has ability to adapt Manny different functions or activity , but at the same time it requires large data sets , substantial hardware as well as more computation power .  In this approach software has ability to train itself using artificial neural network .
In this images are simply to be labeled as ” Cat ” or ” No Cat ” , and then there will be no extra efforts to be put to train software to distinguish cat .
The downside of this approach is it requires massive datasets which may even contain millions of images which might not be easily accessible for smaller companies to access those information .

Why There Is A Need For Object Recognition Software ? 

Object recognition can actively be used in various industry to cater their needs for identification of objects and theory reducing human interaction and time . Human may or may not sort and analyse images at the speed that a well trained algorithm can .
Software also has capability of identifying objects that can’t be even be seen by humans . Object recognition software are also able to detect patterns . As a human being we can get tired of viewing same pattern again and again , while on the other hand software won’t . They have the ability to view more images in a year than human in their entire lifetime . 

Where To Find Vendor Solution ? 

As in case of any other software development , in object recognition also you necessarily need not to start finding solutions right from scratch . If you are not expertise in handling this technology or don’t have access to datasets , you can use TenserFlow Object Detection API from Google .
Apple also offers machine learning API which is called Core ML , which can actively be used to run object recognition model on iOS device or in cloud as well .

Hire Software Development Company For Your Object Recognition Software 

If your team does not have any previous experience with artificial intelligence , then it will be quite hard to crack machine learning  . That is why it is important to hire right software development team who have deep experience in handing variety of object recognition tools . Whether you are looking to create solution right from scratch or want pre-trained mobile app to fit for your purpose , Winklix can help you to find perfect solution for your need .

How Machine Learning Can Help Revamping Mobile App

How Machine Learning Can Help Revamping Mobile App

The era of generic service is diminishing . Customer now a days are more willing to get custom tailored offers as per their specific demand . In fact in recent studies it has been proven that almost 50 % of customers switch their brands if company is not able to meet their specific sets of needs and almost 57% shares their data with companies that send personalised offers .

It is because of digital transformation and technological advancement that has opened up many new doors for vendors which aids in attracting and retaining of customers . But there is a huge difference in fact and reality , in short you will never be able to  fulfil needs of your targeted audience with mobile app that does not contain any advance technology . Machine Learning (ML) is one such cognitive technology that has ability to create algorithms and understand human in a way that can assist them in completion of tasks and even entertain them .

So Machine Learning (ML) is technology which can be implanted in mobile app to make it more user friendly , thereby giving more user experience , customer loyalty and thereby aids in building consistent omnichannel experience .

Let’s now look at how machine learning can enrich your experience : 

Personalised Experience 

With machine learning you can redirect machine to learn and adopt continuously . It has algorithms which redirects analysis of various sources of information which can either be acquired from social media , credit ratings and more which later on given pop recommendation to customers devices .
In addition to above Machine Learning can help you classify users interest , collect information of users and can also guide you how your app should look alike . Machine Learning can be used to learn :

  • Who your customers are 
  • What they want 
  • What are their affordability power 
  • What are their hobbies , interest and pain points 
  • What they are specking about your products 

On the basis of all the information collected above , machine learning can actually help you in structuring as well as classifying your customer into groups . As a result you can deliver content relevant to them on the basis of information collected and hence convey the impression that your app is really talking to them .

Advance Search 

Machine learning helps you building search more intuitive and less burdensome for your customers as they will deliver results on the basis of their most recent searches . Machine learning algorithms helps learning from customers queries and thereby showcase the result which most matters to them . Due to its cognitive in nature , it helps grouping articles , videos , FAQs and documents to provider smarter result and immediate answers to their solutions .
Once the data is collected , machine learning utilised that data to helps customers perform searches , search histories and typical actions with ease . In addition to it , you can also upgrade your mobile app with voice search and spelling corrections .
Reddit is making use of ML which aids them in improving overall search performance for hundred of millions of community members .

User Behaviour Prediction 

Marketers get detailed data about user behaviour by analysis of data collected on the basis of age , gender , location , search request , frequency of app usage and so on . Marketers then make use of data collected to facilitate customers as per their interest as well as increasing overall effectiveness of your app and your marketing efforts . For instance say on the basis of data collected , you have found that females under age of 30 are more using your app in comparison with male , then you may either find ways to attache male audience or move your target entirely on women audience .
Machine learning can also facilitate you with even creating of individual recommendation to boost customer engagement as well as time spent on your app . Have you even been browsing on Amazon ? If yes then you must have experience that Amazon suggest on the basis of machine learning algorithms about your likes and dislikes . In addition to it , almost 80 % of TV shows watched on Netflix are the result of their suggestion system based on machine learning algorithms .

Showing Advertisement On The Basis Of Interest 

One of the hardest part to deal with when it comes to advertisement is showcasing right ads to right audience . Thanks to Machine Learning technology which aids advertisers to showcase advertisement  to right people more accurately .
With Machine learning you can even avoid showcasing advertisement in respect to items that has just been brought by customer and thereby showcasing ads to customers who are more likely interest inn buying products or services . This technology will not only helps you saving your time and money , but will also help you with improving brand’s reputation .
Coca Cola is great example for making use of this technology on social media advertisement . Company make use of image recognition technology for identification of people who have posted their product images . This has helped Coca Cola know about the situation when customers talk about their product and what can be the best way to showcase them advertisement . Ads designed by way of machine learning algorithms has greater changes of getting clicks in respect to other targeted ads .

Security Improvement 

Video , audio and voice recognition makes it easier for customers to add on extra layer of security to their mobile apps by secure app authentication . It is smart decision for any kind of mobile app .
Machine learning algorithms can also help you in detecting and banning suspicious activities . Traditional technology on one hand can only help you with knowing of threat , machine learning mythology can help you protect your customers with identification of previously unidentified malware attacks on real time basis .
Banking and financial companies are also making use of machine learning to inspect previous transactions of customers , borrowing history which helps in determining their credit rating .

Also Read : What Is Machine Learning & Its Applications ? 

User Engagement 

Machine learning has superpower which offers solid customer support and range of specific features and entertainment which given customer a reason to use your app on day to day basis .

Support 

Both Amazon and Facebook is making use of Machine Learning technology for user engagement in a way to handle their request intelligently . Machine learning technology has capability of analysing large sets of data and make decision in real time .
Some people have habit of not making calls or writing long emails until and unless somebody responds . Many companies now a days are implementing machine learning to build conversational UX or Virtual assistant often known as AI chatbots .

Entertainment 

Beyond AI chatbots which can handle customer request even at 3 am , thereby are various other machine learning entertainment tools for customers . Take for instance say , Erwin is bot that lives in Facebook messenger which helps users to solve complicated puzzle by sending them clue if they struck somewhere .
Snapchat is using AR and ML to let customers revamp their pictures using funny filters . Their face is detected by camera and AR helps adding filters on their face .

Valuable Features 

Machine learning also supports real time speech translation . So if your target is on international customers as well , then ML can facilitate you with making successful communication within your app without any need of third party online translators .
We can take example of Airbnb wherein more than 60 % of their booking are done by users in different languages . They are making use of cloud translations API which helps them translating listing , reviews and even conversion between its users . Azar , a chat app is using Cloud Speech API as well as cloud Translation API to translate audio between matches .
Another great example of machine learning application is Realtor.com , which is real estate listing which use Vision API to facilitate people to take pictures for sales sign and get immediate information about property .

Conclusion 

Machine learning has great super power to play with , which has ability transform your mobile app development with new technologies .