Machine Learning and Artificial Intelligence


1. Statistical Modelling
2. Algorithms and Data Structures
3. Data Mining and Information Retrieval
4. Recommender Systems
5. Sentiment Analysis
6. Computer Vision on Neural Networks
7. Time Series Analysis and Forecasting

Service Scope

1. Mathematical Modelling of the Business Use-Cases
2. Building Models for specific Business Problems
3. Implementation of Data mining Techniques
4. Business to Consumers state of the art Recommender sysytems based on Clustering mechanisms
5. Automation of predciting User’s Sentiment with best Natural Language Processing Techniques for Facebook and Twitter
6. Disease diagnosis and detection using Neural Networks and Computer vision
7. Forecasting Sales, Growth, Inventory, Revenue etc.
8. Prototype, and analyze deep-learning, clustering, anomaly detection, and efficient graph search algorithms
9. Machine Learning Models (linear regression, ensemble methods, boosting, RNN, CNN, GCN, GAN, etc.)
10. ML frameworks such as Scipy/Numpy, Scikit-Learn, Pandas, Tensorflow, ・Keras, Chainer, PyTorch

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