Machine Learning Development

Machine Learning is the science of getting computers to learn and act as humans would, and improve their learning over time in an autonomous fashion, by feeding them with large volumes of data in the form of observations and real-world interactions. This definition nicely reflects on what are the objectives and aims of this technology.

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Machine Learning

What’s it like to teach machines to learn?

Did you ever try to google what Machine Learning (ML) is? The search would result in dozens of academia, researches, forums, articles and interviews. A true pandora box, with topics ranging from ‘the dawn of humanity’, or ‘computers will replace human beings’ to ‘the new future’, and ‘technology creating opportunities’. With the Internet (not to mention other media) and business buzzing about how important it is, there must be something beyond the hype! Oh, trust us – there is, so let’s dig in.

In order to simplify the job for you, we’re laying out the most important things about Machine Learning.
On this page, we’ll cover:

What Machine Learning really is

What are the differences between Machine Learning and Artificial Intelligence (AI)

What are the benefits of using Machine Learning solutions

What industries and sectors should be particularly interested in harnessing Machine Learning

We will focus particularly on the technical side of it, discussing what frameworks and libraries are most often used and recommended to build reliable Machine Learning solutions

More than just a definition

Many tried to encapsulate the essence of Machine Learning, and you’ll surely find plenty of definitions out there. As a result, it only makes things more complicated. So, let’s keep it simple: Machine Learning is the science of getting computers to learn and act as humans would, and improve their learning over time in an autonomous fashion, by feeding them with large volumes of data in the form of observations and real-world interactions. This definition nicely reflects on what are the objectives and aims of this technology.

Training algorithms to make decisions

The very name of Machine Learning also tells us its meaning, suggesting it is somehow related to teaching computers to learn. Indeed, ML focuses on training algorithms to make decisions. In plain English, ML processes of supervised learning train the model to identify what class the inspected object belongs to. It happens with the use of a classifier with labelled data as an input. Training the algorithm comes down to ‘telling him’ (by labelling) if there’s a cat on the picture or is it a dog. Then, the algorithm will analyse next pictures itself, looking for a pattern. Soon, it will be able to classify objects and find cats and dogs on pictures.

Deliver as much data as possible

In this scenario, it all comes down to the quality of input data. If every dog will be labeled as the cat, then the algorithm will detect dogs as cats – it’s as simple as that. It relies solely on what we teach him and doesn’t have the ability to learn completely by itself. Also, it’s important to deliver as much data as possible. The more cases the model is trained with, the more precise its learning will be. On the other hand, with unsupervised learning, the algorithms are detecting patterns without labels or input categories. And then there’s reinforcement learning, where algorithms learn from observations and environments in order to take particular actions.

Machine Learning vs Artificial Intelligence

Often, Machine Learning is confused with Artificial Intelligence and although by mistake sometimes mentioned interchangeably, these are not the same things. AI has definitely a broader scope, is defined as an engineering science of making computers behave human-like. Say, a machine completing a task basing on algorithms making ‘intelligent’ decisions. ML is a part of AI, focusing on learning computers to act through experience and produce desired outcomes.

A vast pool of ML benefits

Now that we are aware of what Machine Learning means, the next step is to understand what benefits it brings to the business in practical terms. And with growing amounts of data and more powerful computational power and affordable storage, the benefits are many! Here are just 5 most obvious ones, which make ML important for business:

More informed business decision making in real-time

Making the right decisions at the right time is crucial to being successful in business. With an ever-expanding pool of data, every organization is now in possession of, there is a hidden potential to tap into. ML allows companies to translate data into meaningful information and derive insights, by quickly and automatically produce models that identify opportunities for profits. Including this knowledge in processes and operations takes a business to a whole new level and helps to stay on top of the risk. On the fly, right here, right now.

Introducing automatization

Reducing manual tasks plays a vital role in the economy of the 21st century. With Machine Learning, businesses can take the most mundane and repetitive tasks off employees, increasing their productivity and allowing them to do more value-adding tasks. But ML can help with even most complicated tasks, such as applying predictive models to real-time changing data in order to support decision making.

Enhancing security

Effective detecting a breach in network security before its escalation is crucial. In order to increase the overall security of organizations, advanced ML algorithms monitor behavior to detect anomalies in the network. Applying self-training Machine Learning models results in continuous improvement of cyber-security, detecting malware, preventing data leaks or service outages, recognizing phishing and limiting spam issues.

Reducing the costs of operations

With the use of Machine Learning, companies can go without a large number of employees focused on doing repetitive work. For example, ML improves time-intensive documentation in data entry. Moreover, algorithms help with streamlining existing processes and at the same time are detecting opportunities to increase performance and cut costs. 

Taking products to another level

Machine Learning can also support a business with products – in the way they are designed, personalized, promoted and sold. As ML comprehensively consumes incredible amounts of relevant data, it can be used for data-driven marketing to review and modify assumptions regarding products or services, including customer behaviors and segmentation, optimizing offers and improving personalization in order to successfully increase and accurately predict lifetime value.

Machine Learning use cases –
who can harness the benefits?

 

With that many impactful benefits of Machine Learning, this technology can be easily adopted and applied
to many industries and sectors – especially there, where data is core to the offering and services.
Most businesses already recognized the value of ML. Self-driving cars, recommendation engines,
healthcare or finance are examples of very important uses of ML in our world today.
See how technology is shaping the world we live in:

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Finance and Insurance

Fraud prevention and deriving insights from huge data sets are two most popular uses of Machine Learning in modern world of finance and insurance. This data mining results in investment opportunities, identification of high-risk profile customers, using cybersurveillance, performing algorithmic trading operations at speed and scale, pinpointing fraud or managing portfolios. 

Healthcare

Machine Learning plays significant role in improving accuracy of diagnoses and perfects treatment of patients. Algorithms identify risks and recommend medicines, also facilitating recovery. Increasing number of wearable IoT devices and sensors produce data used to assess health and condition in real-time. With ML, it’s possible to address medical issues that have been unsolved to date, such as understanding risk factors of diseases in large populations, tackling diabetes or cancer. 

Recommendation engines, personalisation and online search

Especially in retail, customers expect some sort of personalisation. ML models are trained basing on previous purchases and history of search in order to create unique shopping experience, target and adapt marketing campaigns, optimize offers and gather customer insights. Every time Netflix suggests you what to see next or when Google or Facebook attacks you with adverts of products you’ve recently searched for – that’s Machine Learning in action, analysing your behaviour and comparing it with other users.

Transportation

Thanks to Machine Learning, smart cars driving autonomously are no longer a science-fiction scenario, but rather a reality we have to adapt to. A vehicle that communicates with other cars on the road and that learns as it drives (itself, of course) is a welcomed concept of a smart city. Understanding and analysing data is crucial for the whole transportation industry, making routes more efficient and introducing predictive maintenance features.

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The most popular Machine Learning technologies

ML becomes more and more prominent, no doubt about it. As its popularity increases, a variety of tools
and frameworks developers and scientists have available constantly multiplies. With the support of the biggest
players like Microsoft, IBM, AWS or Google, offering ML APIs and cloud services, combined with a range
of coding languages, frameworks, and libraries working well with Machine Learning, developers can build
and train models with ease. If you expect high-quality results, you should work with these technologies primarily:

 

Python

Python is the most popular and sought after language when it comes to Machine Learning and Artificial Intelligence, for a number of reasons. Firstly, simple syntax is its big advantage. It resembles everyday English and has a low entry barrier, so a lot of data scientists and programmers pick it up quickly and start development without much effort. Secondly, code written in Python does not need to be recompiled, which makes implementation of any changes quicker. What is more, it works well with other languages, so developers can make use of other skills they have whilst working in Python. It is also very readable and flexible and allows for combining styles to solve problems. And, as it’s completely free and open source, it has a great community support and lots of resources available to facilitate the work. 

But what really makes Python most popular in terms of ML is a great choice of libraries. A library is a module or often a group of published models, which include ready pieces of code performing some certain actions and bringing functionalities on board. With a library, developers don’t need to code everything from scratch every time, they just use ready-made solutions, what significantly spares time and hustle. When it comes to Python, it’s libraries are used mostly to access, handle and transform data.

Django

Fully written in Python, Django is probably the most popular open source web framework of this programming language, used for high-level web development. It’s handy for ML as it integrates with other Python libraries, it’s efficient and very stable. Its other perks include multi-site and multi-language support, MVC layout, AJAX support, free API, URL routing, easy migrations of databases and straightforward session handling. What’s more, due to its popularity there’s already a big and engaged community of Django geeks providing resources and help, for example on Github. Django is also a great choice when you have security in mind. It hides the source code of your web solution, protecting against CSRF and XSS attacks, clickjacking and SQL injections.

Machine Learning developers use Django to create a REST application to deploy ML models. It’s a toolkit used for building web APIs, where complex models and algorithms can easily be deployed as simply as calling an API endpoint. You just start a project, create a Django app, edit few files, create migrations and superuser, then run the server and test the API. Easy!

Flask

This Python micro-framework for web is made for Python applications, used by internet hotshots such as Linkedin or Pinterest. Written by Armin Ronacher, it helps with implementing Machine Learning applications in Python. Thanks to Flask, ML app can be easily plugged, extended and deployed as a ‘regular’ web application.

Flask relies on two major components: WSGI toolkit which is a specification for web applications, and template engine Jinja2, which renders web pages. It’s like a collection of preset code packages, that can be used as building blocks for a web app and further extended, when needed. Flask covers a wide range of built-in functionalities, such as uploading files, object relation mapping, validating forms, authentication or mail and jquery integration. As it’s a very light web framework, Flask also facilitates deployment of Machine Learning models, training a model, saving it and handling an API request.

Pandas

When it comes to Machine Learning, advanced analysis and high-level data structuring is crucial.  That’s where Pandas comes handy, to gather data from external resources (like Excel spreadsheets and CVCs), merge data, filter it and then handle and manipulate it. For many it’s the best Python library. Some would say, when your dealing with excel datasets and CVCs for Machine Learning and data science, Pandas is mandatory!

As with other libraries, with Pandas there’s just less writing with more work done, allowing to focus on analysed data rather than code itself. This library significantly streamlines forms of representing data, which helps in understanding patterns and deriving insights. Filtering, segmenting or segregating even large and complex data sets efficiently with Pandas is as simple as a pie. Same with edition, customization and pivoting the data.

 

NumPy

Data science and Machine Learning is all about math and advanced calculations. In order to perform this sort of complicated computation in linear algebra effectively and efficiently, developers need math libraries like NumPy. Made especially for Python, NumPy supports complex and multi-dimensional matrices and arrays. It allows for performing logical and mathematical operations and functions on top of them both. A true foundation of Machine Learning tech stack!

This library is used for statistical analysis, as it consumes noticeably less memory to store the data and is an efficient storage. NumPy uses simple APIs, making mathematical calculations for Machine Learning and data science easier and more approachable by developers. 

Matplotlib

In order to make the data more readable and understandable, data scientists use charts, histograms, 2D plots and other forms of visualisations. A Python and NumPy library Matplotlib enables data presentation, visualization and comprehension, also helping with clear reporting. It’s object-oriented API embeds plots into apps using GUI toolkits. 

Matplotlib is efficient and fast-working with many backends and operating systems. Aparts from different charts and histograms, it includes a vast variety of tools to graphically present data in high quality, such as plots or heat maps, useful depending on the type and amount of data available. Not to mention a large community support, which is always handy!

Let’s wrap it up

 

When it comes to making the most out of Machine Learning, applying appropriate technology by experts with hands-on experience
is very important. With a diversified tech stack available, only working with the best tools, languages and frameworks can really
enable tapping into the pool of Machine Learning benefits, regardless the industry.

But there’s another key takeaway when it comes to ML. The most important success factor of data science and
Machine Learning is the data itself. The significance of having adequate amount of high quality data,
structurising it and making it fit for training the algorithms must not be underestimated.
Otherwise, you’ll end up with clutter rather than meaningful insights. 

 

Why doing Machine Learning projects with us?

 

In the world of ever-expanding volume and complexity of data each organisation is in possession, it’s important to have a partner that will understand the technology to the extend which will allow for making use of it. 

 

Asper Brothers is a software house with experience in Machine Learning and data science, combining exceptional, hands-on technical expertise with a can-do attitude. We understand both decision trees and neural networks, but also how the business is run and what results it expects. 

We know how to translate newest technology into meaningful business results, that will support operations and streamline processes, bringing your business the insights and predictions you need. Our convenient location in the heart of Warsaw, a capital of a country with one of the best pool of developers in the world, is yet another reason to take into consideration. 

Plus, we’re just a bunch of young, energetic, nice and easy-going geeks, always eager to support your business goals with technology. We treat both our employees and customers as members of the family, making your tech experience even more unique.

 

Make use of Machine Learning with Asper Brothers and propel your business!

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