Artificial Intelligence (AI) & Machine Learning (ML)
TekXenia's AI/ML CoE is dedicated to integrating artificial intelligence and machine learning into business solutions, providing predictive insights, intelligent automation, and data-driven decision-making capabilities. We help businesses build and deploy models that optimize performance and create smarter systems.
TekXenia Machine Learning Approach
TekXenia's Machine Learning Approach
Tools & Technologies:
Programming Languages:Python, R, SQL, Java, Scala, C++, MATLAB Statistical Analysis, Hypothesis Testing, A/B Testing, Statistical Methods, Model Validation
Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep Learning, Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory(LSTM), Gradient Boosting, Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), KyMeans
Machine Learning Libraries & Frameworks: TensorFlow, Keras, PyTorch, Scikitylearn, XGBoost, LightGBM, CatBoost
Data Science Skills: Data Cleaning, Data Visualization, Data Wrangling, Feature Engineering, Feature Selection, Model Evaluation, Model Selection, Model Validation, Cross-Validation, Hyperparameter Tuning, Regularization (L1 & L2, Dropout)
Natural Language Processing (NLP) Tools: NLTK, SpaCy, Gensim, HuggingFace's Transformers
Deep Learning Techniques:Huggingface Transformers (e.g., BERT, GPT-2/3), Generative Adversarial Networks (GANs), Autoencoders Reinforcement Learning: Q-learning, Deep Q Networks (DQN), Policy Gradient Methods, Proximal Policy Optimization (PPO)
Time Series Analysis:SARIMA, ARIMA, Prophet, LSTM for Time Series
Data Manipulation & Analysis: Pandas, NumPy, Dask, Matplotlib, Seaborn, Plotly, Tableau
Database & Storage Systems:SQL Databases (e.g., MySQL, PostgreSQL), NoSQL Databases (e.g., MongoDB, Cassandra), Cloud Storage Solutions (e.g., AWS S3, Google Cloud Storage) Cloud Platforms: AWS (e.g., SageMaker, EC2, Lambda), Google Cloud Platform (e.g., AI Platform, Compute Engine), Microsoft Azure (e.g., Azure Machine Learning)
Experimentation & A/B Testing: Design of Experiments, Multivariate Testing, Hypothesis Testing
Ensemble Methods: Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, Gradient Boosting), Stacking
Additional Skills: Computer Vision, Data Analysis and Visualization using Python Libraries, Cloud Computing Authority, Expert Data Analysis and Visualization, Statistical Analysis Proficiency