Machine Learning and Artificial Intelligence


Machine Learning and Artificial intelligence are the need of the hour! It’s one of the many ways that companies have become smart by using data to better serve customers And improve their own products.

When it comes to Artificial Intelligence it’s all about training a computer to work more efficiently and better than a human could. AI is created to make decisions and understand data points that correlate- these form a logical conclusion to any problem. Therefore, the use of AI leads to higher intelligence or wisdom or as we call it, insight into actions.

Machine learning is a subset of AI. It is the best method to automatically analyze, understand and identify patterns within data. Machine Learning is, therefore, the second aspect of a project where the computer learns from the insights and behaviours it’s trained to follow. Here the goal is to increase the accuracy of data understanding.

Deep learning is a subset of machine learning. It is much more complex and therefore refers to the number of layers in a neural network. The more layers of deep learning, the more complex understandings.

What makes the difference between ML and DL? – ML requires structured data to be presented, whereas DL requires a complex algorithm where layers of the ANN – artificial neural networks are activated.


Machine Learning


Within organisations, machine learning can be an asset within any department. With the ability to understand data, crunch and derive value from it – sales, marketing, operations; there’s an opportunity to understand data points more deeply.

With ML, there is a custom need and methodology per organization to derive the necessary solutions and insights. At Versatile, we have learned the languages and modules necessary to build out a custom solution for you, from scratch.

Within ML there are 4 main steps – developing the algorithm, training, tuning and then deploying at scale.

When developing the ML algorithm, our team of experts creates rules, creates norms and exclusions – teaching the system how to understand data.

The next step, training is where we input the linkages and correlations – this helps the algorithm understand structured and unstructured data. The next, tuning, is where we analyse what we have and fix the bugs and gaps so that your data is consistently accurate. The final step, deployment helps introduce ML to your existing application, making sure the transition is smooth and integration works as intended.

Our highly-proficient machine learning experts enrich standard machine learning development models using custom data preprocessing scripts by vectorizing data, removing correlations and outliers. By uploading prepared datasets, our developer team aligns the machine learning model with your objectives.

Some of the platforms we are trained in are:

  • Google Cloud AI
  • Microsoft Azure Cognitive Services
  • Amazon AWS Machine Learning
  • IBM Watson Machine Learning

An example of the languages we are experts within:

  • Python
  • R Languages
  • C++

Given that there is a multitude of industries ML can be introduced within, the possibilities of success are endless.

Artificial Intelligence


Now that we understand what ML is, AI is focused in and around all the use cases where ML needs to be used to gather and AI, to understand data.

One of the premier use cases of AI is for NLP – or Natural Language Processing. It revolves around understanding and manipulating human language – English, for example. NLP is used to make computers read the text, hear speech, interpret it and measure sentiment. Within NLP comes features and functions like contextual extraction, sentiment analysis, summarization of documents or even translation!

Within imagery, AI can understand, capture and use the information for various purposes. By understanding items such as colours, patterns and shapes of the image, AI can extract relevant data without human intervention.

Further, segmenting them by face, like the iPhone does currently till restoring and enhancing images that need to be fixed. AI has plenty of applications. Further, more advanced image detection – this can help in say gathering images from identity cards where they need to be gathered for verification.

For businesses, AI is used to build chatbot applications that can act as a front-facing figure to your business. Here they can obtain rich data to understand user behaviour and provide uninterrupted customer services to users 24×7. Apart from carrying out scripted conversations, bots and AI together can act as an operator too – forwarding and tagging relevant people in conversations via channelling.

Our goal is to ensure that you are empowered by Artificial Intelligence within your use cases – be it customer support, sales, marketing and more!.

Here are some of the skills we possess:

Python, Java, R, C++, C, JavaScript FrameworksScikit-learn, Tensor Flow, Apache Spark, Azure machine learning, IBM Watson FrontEndNodeJs, Kafka, Docker, Power BI, Tableau, Pentaho, QlikView DatabaseMongoDB, MS SQL Server, MySQL, Cassandra, PostgreSQL.


Data Science


Data science helps enterprises make data-driven decisions, optimising decisions, reducing costs and finding new ways to use resources.

When working with data science, it allows business owners and stakeholders to analyze, visualize and report on data points with ease offering a 360-degree view of a business. Real-life business problems are solved intuitively and realistically to support decision making.

Within data science, the data part of this process/activity is in millions of points/bytes. For companies, it is a strategy that collects and analyzes large amounts of data/bytes to uncover patterns, the existing correlations, individual customer preferences, and other vital information that can assist businesses to make informed decisions.

The first and most important step in data science is to develop an architecture that works across departments and functions. Our consultants help utilise structured and unstructured data that will help your business grow, keeping in mind your current capabilities.

Once we’ve built the architecture and linkages, the next part would be automation functions where data can be grabbed, studied and understood for the use case. Be it reading a blog, or understanding audio files – big data can clean, validate, understand, harmonize and verify data.

Technologies Used:

Frameworks : Hadoop, Apache Spark, Hortonworks, Amazon Web Services, Cloudera, MS Azure.

Databases: Oracle, MySQL, MS SQL, Sybase, IBM DB2, Teradata, Amazon Dynamo, Apache HBase, Neo4j.

Data science unlocks the next level of performance for your organization.

Deep Learning


Deep learning relates to artificial neural networks that are composed of many layers. It is a domain that is motivated by intuition, theoretical arguments from circuit theory, empirical results and current knowledge of neuroscience. Digital-native businesses have been more responsive towards implementing deep learning than more traditional industries like manufacturing or retail.

Some of the applications of deep learning are demand prediction, fraud detection, image recognition, speech recognition and NLP. Basically, intelligent utilization of data sets can help grow business operations, improve productivity and enable accelerated business outcomes.

Why did deep learning become so famous?

  • Amount of data available. From mobile phones to tablets to desktop, there is an innumerous amount of data companies and software have about you. Combined with the internet and things like AI – we are able to understand, aggregate and use data with deep learning to gather insight and data points to help solve a problem.
  • Developments in processor architecture have allowed a massive speedup of mathematical matrix operations. Items such as the Graphics Processing Units (GPUs) that was optimized for gaming, can now be leveraged for neural networks. With developed computational power neural networks have, it can increase significantly in size, adding to their mighty power within models. This expansion is done by adding “layers”, inspiring the name deep learning.

Frameworks and libraries we are familiar with:

  • Core ML
  • Cloud Image Recognition SDK
  • TensorFlow Lite
  • Caffe2

Web and desktop :

  • TensorFlow
  • Open CV
  • Caffe
  • Natural Language Toolkit

DL Frameworks :

  • Tensorflow
  • PyTorch
  • MXNet
  • Nvidia Caffe
  • Caffe2
  • Chainer
  • Theano

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