Machine Learning 



Machine Learning (ML) is the subfield of computer science that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.


ML “evolved from the study of pattern recognition and computational learning theory in artificial intelligence." It “explores the study and construction of algorithms that can learn from and make predictions on data—such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.


AI and ML can be used in the following ways:

  • Voice recognition
  • Voice search
  • Sentiment analysis
  • Flaw detection
  • Fraud detection
  • Recommendation engine
  • Facial recognition
  • Machine vision
  • Motion detection

Machine learning can be broken down into the following three categories:


  1. Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teach.
  2. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).er”, and the goal is to learn a general rule that maps inputs to outputs.
  3. Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent..



MACHINE LEARNING MODELS



Cluster Analysis is a method used to classify objects into groups where those similar in characteristics are grouped together in a cluster. It is also known as segmentation analysis or taxonomy analysis. Clustering helps us look at the data as a whole so we can classify the points and logical structures based on the groupings we choose to create. Clustering has several uses, including in marketing, where it is used to identify homogenous groups of buyers; in medical science, where it is used for disease classification; in geology it is used to identify the weaknesses of earthquake-prone regions.


k-means Clustering


Spatial Analysis


Spatial data, also known as “Geospatial Data” refers to records in the dataset that have a geographic aspect. It can be the location of a store, the distance between two bus stops, or the boundaries of a state. 


Decision Trees are some of the easiest models to understand and illustrate as they are straightforward and don't require complex interpretation. Decision Tree models are good for handling complex, non-linear relationships, and outliers on the data, but it also has its disadvantages. Some of its predictions tend to be weak since it is prone to overfitting. They are not very robust, because small changes in the training data can give a larger change in the output. However, they are one of the most commonly used analytical models around. 



Decision Trees

Logistic Regression


Logistic Regression is a binary classification algorithm. Meaning, it predicts either true or false, churn or not, pass or fail and other binary categorical values. This algorithm also uses a regression function which classifies the target variable based on probability. Logistic regression fits the data to an “S” shaped logistic function also known as the Sigmoid Curve. It curves from 0 to 1, where 0 is the least probable or false, while 1 is the most probable or True. This splits the data into one of the two categories and each row of data is classified by comparing the probability to a threshold between categories. 

SERVICES

Intelligencia is partnered with such well-known software vendors as Adobe, Alteryx, Domo, SAS, Pegasystems, Qlik, Tableau, Vantana, HDS, Salesforce, and ITRS, as well as such uniquely casino-specific vendors as Casino Data Imaging, which produces a data visualization tool that helps casino executives understand a patron’s detailed activity on the casino floor. Intelligencia also understands how to integrate open source systems with commercial software, which allows our clients to cut down on data processing time as well as cost.

Analytics

Intelligencia can help its clients understand the rapidly evolving analytics world, with AI and machine learning taking center stage. ROI must be considered when it comes to purchasing analytics and we can show you what's available in the market, as well as what should be avoided. Our clients call us 'trusted advisors' and we wear that moniker with great pride.

Cloud

Cloud offerings today are anything but simple - private, public, hybrid, edge cloud - all of which have a unique purpose and raison d'etre. At Intelligencia, we can show you how to navigate through this tricky terrain, terrain that gets very costly very quickly if not done right. Do you go with a prepackaged option like Red Hat's Open Stack or will you build something from scratch on your own? So many choices. So many dead-ends. We're here to help. 

Business Intelligence

Intelligencia can help you implement complex BI solutions from the ground up. We can help you understand your data in ways that will simplify your BI initiatives. Our data integration and BI experts can help you navigate the challenging data visualization world so that the data you're expecting to see is exactly what you see in your final dashboard.

Data Integration

The goal of data integration is to extract data from an operational system, transform it, merge it into new datasets, and then deliver it to an integrated data structure built for marketing, analytics, loyalty, and/or social media purposes. DI challengers are intensifying because of the increased demand to integrate machine data and support Internet of Things (IoT) and digital business ecosystem needs for analytical processes. It's a complex world. Don't go it alone. 

Customer Experience

CX solutions include aspects of CRM, loyalty, MCM, and even social. Implementations are highly complex, taking into account multiple source systems, strong data cleansing tools, detailed loyalty programs that track every dollar and every secret and non-secret loyalty point, as well as marketing systems that both send out digital content and track offers used.

Marketing

Intelligencia can help businesses implement complex marketing and customer experience solutions with their legacy software or help you build them from scratch. We'll help you understand your data in ways that simplify your digital marketing initiatives. Our experts can show you how to augment your legacy data environment to add powerful real-time streaming and in-memory elements that will immediately be recognized by your customers.

CONTACT US

We are available 24/7, on multiple social channels, or connect with us below.

contact@intelligencia.co

505 Hennessy Road, Suite 613, Causeway Bay, Hong Kong

+852 5192 1277

Rua da Estrela, Macau

+853 6616 1033

www.intelligencia.co

Intelligencia

Intellligencia is a Hong Kong- and Macau-based software consulting company that works specifically in the hospitality, gaming, fintech, esports, manufacturing, retail, sports betting, and travel industries. Although located in Asia, we work with clients as far away as North America, Mexico, India, Armenia, Australia, and the Philippines.

Connect

Address: 505 Hennessy Road, #613, Causeway Bay, Hong Kong

Phone: +852 5196 1277

Email: contact@intelligencia.co

Website: www.intelligencia.co

Network

Sign up to receive email for the latest information.
© Intelligencia Limited. All Rights Reserved. 2022

Search