Machine Learning Services
Artificial Intelligence to the Rescue
Our Machine Learning Services
Model Building

Our Data Science team can create a Machine Learning Model based on your business requirement. We can build and train models using supervised, unsupervised and reinforcement learning.

Model Optimization

We optimize the model for maximum accuracy. For optimization, we use hyper-parameter tuning, gradient descent, SGD, ensemble & boosting and many more processes, to derive the best outcomes from the model.

Model Evaluation

To test the performance of Machine Learning Models, we use Cross Validation, RSS, RSME, MSE, Log-loss, F-measure, Precision-Recall etc. to ensure models perform well.

Feature Selection

Our expert team can help you with feature selection using techniques like PCA, LSH, SVD etc., for the best performance from a Machine Learning Model.

Feature Transformation

We are experienced in feature transformation using techniques like PCA, nn-gram, Tokenizer, StopWordsRemover, OneHotEncoder, VectorIndexer, Normalizer and much more.

Feature Extraction

Our team has the expertise to deal with raw data using TF-IDF (HashingTF and IDF), Word2Vec, CountVectorizer.

Machine Learning Expertise

Supervised Learning

  • Regression
  • Classification

Unsupervised Learning

  • Anomaly Detection
  • Clustering & Retrieval
  • Dimensionality Reduction
  • Collaborative Filtering

Reinforcement Learning

  • Websites
  • Text & Images
  • Audios & Videos
  • Social networks
  • Log files
Technology
SaaS

Google Machine Learning, Amazon Machine Learning, Azure Machine Learning

Frameworks

Spark MLlib, TensorFlow, Scikit-learn

Machine Learning Models

Linear, Ridge, Lasso, Elastic Net, Generalized, Decision tree, Random Forest, Gradient Boosted, Survival, Isotonic, Kernals, Logistic regression, Random Forest, AdaBoost, Support Vector Machine (SVM), Naive Bayes, Kernals, K-means, Latent Drichlet Allocation (LDA), Bisecting K-means, Gaussian Mixture Model (GMM), K Nearest Neighbors (kNN), Local Outlier Factor, Moving Z-score, Bayesian Changepoints, Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)

Optimization Algorithms

Gradient descent, Coordinate descent, Boosting, Grid Search, Mini Batch Gradient Descent, One-Vs-The-Rest, One-Vs-One, Expectation-maximization (EM), Locality Sensitive Hashing (LSH), Brute Force, KD Tree, Ball Tree, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Stochastic Gradient Descent (SGD), Alternating Least Squares (ALS), Uni/Multivariate Gaussian Distribution, Adam Optimizer, Relu, Sigmoid Languages SCALA, Python, R, JAVA

Languages

SCALA, Python, R, JAVA

SaaS Google Machine Learning, Amazon Machine Learning, Azure Machine Learning
Frameworks Spark MLlib, TensorFlow, Scikit-learn
Machine Learning Models Linear, Ridge, Lasso, Elastic Net, Generalized, Decision tree, Random Forest, Gradient Boosted, Survival, Isotonic, Kernals, Logistic regression, Decision Tree, Random Forest, AdaBoost, Gradient boosted, Support Vector Machine (SVM), Naive Bayes, Kernals, K-means, Latent Drichlet Allocation (LDA), Bisecting K-means, Gaussian Mixture Model (GMM), K Nearest Neighbors (KNN), Local Outlier Factor, Moving Z-score, Bayesian Changepoints, Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)
Optimization Algorithms Gradient descent, Coordinate descent, Boosting, Grid Search, Mini Batch Gradient Descent, One-Vs-The-Rest, One-Vs-One, Expectation-maximization (EM), Locality Sensitive Hashing (LSH), Brute Force, KD Tree, Ball Tree, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Stochastic Gradient Descent (SGD), Alternating Least Squares (ALS), Uni/Multivariate Gaussian Distribution, Adam Optimizer, Relu, Sigmoid
Languages SCALA, Python, R, JAVA
Languages SCALA, Python, R, JAVA
Success Stories
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