Machine Learning and Data Science Development

Building a custom AI application with Twistellar will help you process your data and make the most of your CRM investments. We master the list of Machine Learning and Data Science technologies:

Customer Behavior Prediction

Customer Behavior Prediction Increases Profits and ROI

Provides more effective customer relations management and prioritization of efforts based on customer segments analysis
Increases conversion of all customer interactions
Directly increases sales via recommendations of desired goods
Helps to predict and prevent customers churn
Predicts customer's decisions and helps to adopt individual proposals
Provides possibility of making decisions based on data analysis that can't be done by humans
Allows detection of anomalies in customer behavior, employees' behavior, performance of systems, hardware — leading to prevention of losses and prediction of malfunctioning
Predicts meaningful events, peaks of demand
Selected techniques

Examples of Applied Algorithms

Clustering, RFM, Association Rule-Mining, Apriori algorithm Discriminant analysis Tree-based methods, XGBoost, Production Rules Deep Learning Algorithms, Multi-Layer Perception Regressions. Naïve Bayes. Bayesian Networks K-Nearest Neighbor, Learning Vector Quantization Restricted Boltzmann Machine Sequence analysis, classification

Natural Language Processing and Speech Generation

Business Advantages of Natural Language Processing

Ability to implement text and speech analysis previously unavailable due to huge labor costs, possibility to plan intelligent actions
Decrease of response time for any customer queries, improve service, increase retention and customer loyalty
Saving costs on manual information input, providing new interfaces for customer interactions
Generation of effective marketing language to improve conversion and decrease labor costs for advertising
Thorough analytics of brand recognition, prevention of customers churn, discovering leads, increase of profits and ROI
Selected techniques

Examples of Applied Algorithms

Convolutional Neural Networks: AlexNet, VGG, ResNet, YOLO Recurrent Neural Networks: LSTM, GRU, Bi-directional LSTM Word-embedding models. Siamese network. Word2vec, Gensim, GloVe Deep Structure semantic models Deep Convex Net Knowledge Graph RCNET Deep Belief Networks Deep Speech 2 WaveNet

Machine Learning for Computer Vision

Business Advantages of Computer Vision Implementation

Automation of image and video recognition and classification, object detection tasks: increase of performance
Decrease of costs, increase of ROI provided by reassigning people from routine to business development tasks
Provision of consistent stable image/video processing results, decrease of dependency on experts, decrease of educating costs
Possibility to overcome accuracy of the best human experts
Possibility to implement image / video processing systems previously unavailable due to various limitations
Selected techniques

Examples of Applied Algorithms

Deep Learning Methods: Convolutional Neural Networks: AlexNet, VGG, Inception, ResNet Auto-encoders: Denoising Auto-encoders, Variational Auto-encoders Deconvolutional Neural Nets Recurrent Neural Networks, LSTM, GRU, Feedback Networks Fast R-CNN, YOLO, RefiNet Sparse Coding: Super vector coding, Laplasian SPM Deep Belief Network Vanilla GAN, InfoGan, Conditional GAN Visual Dialogue Model etc
dlib C++ Library
scikit learn

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