Jayadev Jigajinni

Data Analyst

Talent

Benachanhalli, Bengaluru Rural1000 INR/hourMember since November 16, 2023

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machine learning data scientist deep learning data analysis data analyst

About

Aspiring Data Scientist eager to contribute to team success through hard work, attention to detail and excellent organizational skills. Clear understanding of analysis of Data, Data modeling and coming with solid mathematics background, programming skills. Motivated to learn, grow and excel in Artificial Intelligence industry.

PROFESSIONAL SUMMARY

Strong Mathematical foundation and good in Statistics, Probability, Calculus and LinearAlgebra.

Experience of Machine learning algorithms like Simple Linear Regression, Multiple Regression, Polynomial Regression, Logistic Regression, SVM, kNN, Naive Bayes, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, XGBoost, K-fold cross validation, Grid Search, RandomSearch.

SkilledinMinimizingthecostfunctionbasedalgorithmslike:GradientDescent,StochasticGradientDescent and Batch Gradient Descent and Regularizing Linear Models with the help of Ridge, Lasso and ElasticNet.

Good knowledge of Clustering algorithms like K means, Agglomerative Hierarchical Clustering,DivisiveHierarchicalClustering,DBScan,AssociationRulelearningandDimensionalityReductionlikePCA,LDA.

Feature Engineering in Python – Missing value treatment, outlier handling, data transformation, Feature Selection and reshaping data using Python packages like Numpy, Pandas and ScikitLearn.

Data Visualization techniques with help of Matplotlib, Seaborn andTableau.

Good knowledge of Deep Learning (DL) and hands-on with Neural Network Architecture, Loss Function, Cost Function, Optimizers, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), LSTM.

Skilled in libraries like Numpy, Pandas, Matplotlib, Seaborn, Scikit Learn, Tensorflow, Keras, OpenCV and NLTK.

Basic Understanding of Computer Vision techniques like Image pre-processing, Image Segmentation, Object detection, Object recognition, Objecttracking.

Basic Understanding of Natural Language Processing (NLP) techniques like tokenization, stemming, lemmatization, Text Analysis, Matrix TFIDF andword2vec.

Hands on Experience in working with Dockers, Containers, GPU, multiple GPU environment and Automatic Mixed Precisiontechniques.

Experience in working with MySQL database using SQL and basic knowledge ofMongoDB.

Basic knowledge of Amazon Web Services (AWS) CloudComputing.

Good knowledge of Google Cloud Platform (GCP) and working with VMs andServers.

Self-motivated team player with good communication and presentationskills.

Good analytical and problem solvingskills.

Experience

Strong Mathematical foundation and good in Statistics, Probability, Calculus and LinearAlgebra.

Experience of Machine learning algorithms like Simple Linear Regression, Multiple Regression, Polynomial Regression, Logistic Regression, SVM, kNN, Naive Bayes, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, XGBoost, K-fold cross validation, Grid Search, RandomSearch.

SkilledinMinimizingthecostfunctionbasedalgorithmslike:GradientDescent,StochasticGradientDescent and Batch Gradient Descent and Regularizing Linear Models with the help of Ridge, Lasso and ElasticNet.

Good knowledge of Clustering algorithms like K means, Agglomerative Hierarchical Clustering,DivisiveHierarchicalClustering,DBScan,AssociationRulelearningandDimensionalityReductionlikePCA,LDA.

Feature Engineering in Python – Missing value treatment, outlier handling, data transformation, Feature Selection and reshaping data using Python packages like Numpy, Pandas and ScikitLearn.

Data Visualization techniques with help of Matplotlib, Seaborn andTableau.

Good knowledge of Deep Learning (DL) and hands-on with Neural Network Architecture, Loss Function, Cost Function, Optimizers, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), LSTM.

Skilled in libraries like Numpy, Pandas, Matplotlib, Seaborn, Scikit Learn, Tensorflow, Keras, OpenCV and NLTK.

Basic Understanding of Computer Vision techniques like Image pre-processing, Image Segmentation, Object detection, Object recognition, Objecttracking.

Basic Understanding of Natural Language Processing (NLP) techniques like tokenization, stemming, lemmatization, Text Analysis, Matrix TFIDF andword2vec.