We have endeavoured to accumulate multidisciplinary skill sets required to deliver customer solutions through our panel of subject matter experts and researchers.
Semantic Text Analytics
STA extracts structured insights from unstructured textual data. This is done through Text Cleansing and Feature Extraction (TCFE), Natural Language Processing (NLP), and Post-processing.
Cognitive Virtual Agent
CVA combines the power of natural language understanding, machine learning, structured data, and business process flow to interact with users through mobile or web.
Social Graph Analytics
SGA identifies connections between the customers and categorizes the nature of connection to build social graph and micro segments for targetting and personalisation.
Intelligent Call Routing
ICR applies descriptive, predictive and prescriptive analytical techniques on psychographic and demographic data of customers and support agent to find the ideal mapping between them.
Automatic Machine Learning
AML eases the task of selecting the right algorithms to solve a problem by using meta-learning to automatically recommend the most appropriate algorithm for a dataset.
Deep Image Analysis
DIA uses deep learning and transfer learning to accuratly classify an image by reducing a pre-trained model on a new domain, thus enabling quick deployments.
Cross-domain Meta-learning for Time-series Forecasting
In this paper, we are investigating in what situations use of additional data can improve performance of a Meta-learning system, with focus on cross-domain transfer of Meta-knowledge.
Cognitive Computing to Optimize IT Services
In this paper, the challenges of maintaining a healthy IT operational environment have been addressed by proactively analyzing Service Desk tickets, customer satisfaction surveys and social media data.
Customer Churn Prediction, Segmentation and Fraud Detection in Telecommunication Industry
This work uses social network analysis to extracts relationships between different subscribers to improve the results produced by the traditional learning algorithms at individual subscriber level.
Intelligent Call Routing to Optimize Contact Center Throughput
This paper proposes that descriptive, predictive and prescriptive analytics can be applied to psychographic and demographic data; to find the ideal mapping between customer and the agent.
Automatic Diacritization for Urdu
Diacritics are optional and usually not represented in Urdu orthography which can create problems for computational systems. In this paper, a Hidden Markov Model approach for automatic Urdu diacritization is used to disambiguate Urdu text.
Urdu Text Classification
This paper compares statistical techniques for text classification using Naïve Bayes and Support Vector Machines, in context of Urdu language. Empirical results show that Support Vector Machines outperform Naïve Bayes in terms of accuracy.
This paper describes English to Urdu transliteration system. The mapping rules to generate Urdu text from English transcription and syllabification and Urduization phase is described and finally the issues related to Out-Of-Vocabulary are discussed.
Data Science promises so much to the organisations that they embrace it as essential element of their strategy. This course will help you develop the strategic understanding of Data Science and Applied Knowledge you need to seize the economic, social and business potential of data.
This course will help to develop comprehensive and practical understanding of Data Science. Theory and hands-on labs emphasize on developing your knowledge of Data Science concepts, practices, models, lifecycle, visualisation, platforms and modelling languages (statistics and machine learning) required for you to become advanced beginner in the area of data science. Develop the competence in finding, manipulating, managing, and interpreting data to communicate and operationalize results and insights.
- Data Science Lifecycle
- Statistical and Probability
- Statistical Computing Language - R
- Machine Learning
- Statistical Pattern Recognition
- Prescriptive Analysis
- Python Programming Language
- Natural Language Processing
- Information Retrieval
- Big Data
Machine learning studies the question “how can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. It is a fast-moving field of computer science with many recent applications within the sciences and medicine.
It is an introductory course which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, and Bayesian networks.
- Supervised Learning
- Bayesian Decision Theory
- Parametric Methods
- Multivariate Methods
- Dimensionality Reduction
- Nonparametric Methods
- Decision Trees
- Linear Discrimination
- Multilayer Perceptrons
- Local Models
- Kernel Machines
- Bayesian Estimation
- Graphical Models
Deep Neural Networks are increasingly taking over all Artificial Intelligence tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.
This course will cover the basics of deep neural networks, and their applications to various AI tasks. Also fundamentals and contemporary usage of the Tensorflow library for deep learning research will be covered. Tensorflow will be used to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embedding, translation, optical character recognition.
- Feedforward neural networks
- Convolutional neural networks
- Factor analysis
- Autoencoder and DBM
- Recurrent neural networks
- Natural language processing
Cognitive computing refers to systems that learn at scale, reason with purpose and interact with humans naturally. Rather than being explicitly programmed, they learn and reason from their interactions with us and from their experiences with their environment. Those systems have been deterministic; cognitive systems are probabilistic. They generate not just answers to numerical problems, but hypotheses, reasoned arguments and recommendations about more complex — and meaningful — bodies of data.
The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives save. This course covers background concepts needed to understand and work with cognitive technologies.
- Deep QA Architecture
- Semantic Integration and Machine Learning
- Natural Language Processing
- Structured Knowledge
- Domain Adaptation
- Distributional Semantics
Natural language processing can make sense of the 80 percent of the world’s data that computer scientists call unstructured. This enables them to keep pace with the volume, complexity and unpredictability of information and systems in the modern world.
This course will explore statistical, model-based approaches to natural language processing. There will be a focus on corpus-driven methods that make use of supervised and unsupervised machine learning approaches and algorithms. Also some of the core tasks in natural language processing will be examined, starting with simple word-based models for text classification and building up to rich, structured models for syntactic parsing and machine translation.
- Encoding and Processing
- Normalization and Collation
- Tokenization and Word Segmentation
- Morphology and Finite State Transducers
- Probabilistic Modelling and Spelling
- Word classes and POS tagging
- Rule-Based and Probabilistic Parsing of CFGs
- Annotated Grammars and Lexical Functional Grammar
- Hidden Markov Models
- Meaning and Pragmatics
Hadoop becomes the core component of distributed massive data processing - as necessary to understanding your information as servers are to storing it. This course provides introduction to Hadoop, its file system (HDFS), its processing engine (MapReduce), and its many libraries and programming tools.
Spark has been taking the big-data world by storm after showing consistent benchmarks that boast of it being up to a hundred times faster than traditional architecture - Map-Reduce. This course will attempt to articulate the expected output of Data Scientists and using Spark how to deliver against these expectations. The course assignments include Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach how to manipulate data sets using parallel processing with Spark.
- Introduction to big data
- Map Reduce
- Introduction to Spark
- Big Data Bottlenecks
- Spark architecture
- Spark in Scala
- Using SparkSQL and Dataframes
- SparkSQL actions with Scala programs
- Spark Streaming
- Spark Machine Learning Techniques
Enterprise IT has came a long way from the era of monolithic software and bundled hardware and has evolved into the modern micro-services and exible deployment paradigms. Past few years have seen rapid developments in many technologies such as containerization and software de ned networks. Cloud o erings have gone way beyond simple storage and compute services. Big Data is yet another world in its own right and there are dozens of components to choose from.
The areas of Big Data, IoT and Cloud are converging and it can be daunting to gure out the optimal architecture to achieve an objective.
This introductory course touches several topic including di erent deployment models on cloud, mixing public and private clouds, container orchestration platforms and service architectures. This course will also cover di erent components in the Hadoop ecosystem and discuss architectural choices.
- Infrastructure as a Service
- Platform as a Service
- Software as a Service
- Container Orchestration
- Clustering Platforms
- Private Cloud Platforms
- Serverless Computing
- Scaling & Load Management
- Databases and Data Formats
- Data Pipelines & Message Queuing
- Mediation & ETL Tools
- Analytics Execution Engines
- Data-warehousing & Visualization
- Machine Learning & AI Platforms
- Application Optimization & Delivery
- Continuous Integration
Our courses are taught academically as standard 45 hours courses. All the courses include a well crafted plan to ensure learning through assignments, quizzes and hands-on exercises.
The courses can be customized for each customer considering the training recipients and objectives.Download Learning Handbook
Watch this space for our upcoming publicly available courses and registration details.
Muzaffar Haider Ali, PhDChief Executive Officer
Muzaffar has held senior leadership positions at multinational engineering conglomerates and has steered several mega projects to completion.
Abbas Raza AliChief Data Scientist
Abbas has succesfully delivered solutions to several industries in four continents and has several publications in machine learning, big data & NLP.
Jaffar HussainChief Architect
Jaffar has diverse experience in several technology domains including telecommunications, cloud, big data, solution and enterprise architecture.
Our subject matter experts belong to several industries and scientific areas. Most of them are full time researchers, others are involved in commercial ventures.
Damien Fay, PhDBig Data Analytics
Hasan Jamal, PhDParallel Computing
Hassan Mohy-ud-Din, PhDMedical Imaging
Maissom Abbasi, PhDUser Experience
Martin Sewell, PhDFinance, Environment
Mehreen Saeed, PhDMachine Learning
Nakhat Fatima, PhDNatural Language Processing
Ricardo Prudêncio, PhDMeta Learning, Social Graphs
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