Hi there ?? Visit me at www.bhaskartripathi.com for more details. ?? ?My GitHub Statistics For collaborations and discussions, book a meeting with me: Schedule Here ???? ?Contact Me My Machine Learning experience mindmap root{{BHASKAR TRIPATHI : Machine Learning Experience}} (Deep Learning) Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Generative Adversarial Networks (GANs) Autoencoders Transformers Deep Reinforcement Learning (Supervised Learning) Classification Regression Emsemble Methods based on problem type (Unsupervised Learning) Clustering Principal Component Analysis (PCA) Independent Component Analysis (ICA) t SNE Self Organizing Maps (SOMs) Generative Models (Natural Language Processing) Text Classification Named Entity Recognition (NER) Sentiment Analysis Language Models Neural Machine Translation (Time Series Analysis) Most econometric methods like ARIMA,ARCH/GARCH family,VAR models Smoothing Methods Filtering : Kalman, Savitzky Golay etc Emprical Mode Decomposition : EMD, CEEMDAN, my own Adaptive methods Wavelet Analysis Spectral Analysis : Analysis of EEG Data (Entropy and Information Theory) Permutation Entropy Approximate Entropy Sample Entropy Lempel Ziv Complexity Mutual Information Shannon Entropy My own Hybrid Methods Multi Scale Entropy My overall experience Summary mindmap root{{BHASKAR TRIPATHI : Overall Experience}} (REPORTING/ETL/ DATALAKES) Hyperion 9.0 (Oracle BI) Qlikview Tableau ArcGIS Spatial Analytics Databricks Azure Data Factory Confluent Snowflake Azure SQL (STATISTICAL PACKAGES) MATLAB SPSS SAS SQL R Python Hadoop/Spark HIVE (Cloud Data) Azure Data factory Azure SQL Snowflake Databricks Data Lakes on Cloud (CLOUD ML) Aluxio Azure Machine Learning Studio AWS & Glue Google Cloud Platform (GCP) Alibaba Cloud PAI studio (DATABASE) MS SQL Server Sybase IQ Oracle Microsoft SQL Server Analysis Services (SSAS) Microsoft SSRS (AI/MACHINE LEARNING/DEEP LEARNING LIBRARIES) Azure ML Tensorflow 2.0 Pytorch Keras Spark Transformers Convex optimization libraries Google Operations Research toolkit Hybrid Neural Network models with LSTM, ANN, CNN, GAN, Attention based networks etc. (PRODUCT AND CONSULTING SPECIALIZATION) Product strategy Requirements Discovery Implementation UAT Business process optimization Strong Data Engineering Practices Cloud Consultancy Applied mathematical optimization Product Quality Product roadmap Streaming Databases Algorithmic trading Retail (GLOBAL EXPERIENCE) United States Canada Asia Pacific markets China My Experience in Mathematical Optimization mindmap root{{BHASKAR TRIPATHI : Optimization & Evolutionary Algo experience}} (Mathematical Optimization) Dynamic Programming Quadratic Programming Convex Optimization Combinatorial Optimization Bayesian Optimization Tree Parzen Optimization (Single and MOPSO) (Evolutionary Algorithms) Genetic Algorithm Particle Swarm Optimization Ant Colony Optimization Differential Evolution Simulated Annealing Grey Wolf Optimization Hybrid Memetic Algorithms (Optimization) Single Objective Benchmark functions Multi Objective Benchmark funtions Worked on more than 100+ benchmark functions Pick Path Optimization Pegion Hole Optimization (Metaheuristics) Tabu Search Greedy Algorithms Hill Climbing Local Search Randomized Algorithms Simulated Annealing Tabu Search (Reinforcement Learning) Single Agent RL Multi Agent RL Multi Criteria Optimization with MARL Recurrent RL