Akshay Jejurkar

Data cleaning through the EDA, Model Building
• Clear understanding of Machine Learning, Deep Learning, NLP, Algorithm
• Supervise Machine learning algorithms such as- linear and logistic regressions,
Decision tree, Random Forest, Naïve Bayes, k-NN, SVM and Neural Networks.
• Unsupervised machine learning- Hierarchical clustering, k-means clustering and
principal component analysis (PCA)
• Involved in converting unstructured data into structured and workable datasets.
• Ability to use a language like Python to work at scale with large data sets and tools
like SQL
• Involved in publishing the python code in the Git - Hub
• Good command on statistical techniques like Regression, Classification & clustering.
• Experience in data visualization techniques for final report communication to clients.
• Expertise in data exploration techniques using various statistical tools.
• Experience using NLP toolkits like Spacy, NLTK, TextBlob
• Experience using data science libraries Pandas, Numpy, Scipy, Seaborn
• Build predictive models and machine-learning algorithms
• Combine models through ensemble modelling
• Experience with exploratory data analysis, statistical analysis,
• Knowledge and skills to turn raw data into information and insight, which can be used
to make business decisions

Data cleaning through the EDA, Model Building
• Clear understanding of Machine Learning, Deep Learning, NLP, Algorithm
• Supervise Machine learning algorithms such as- linear and logistic regressions,
Decision tree, Random Forest, Naïve Bayes, k-NN, SVM and Neural Networks.
• Unsupervised machine learning- Hierarchical clustering, k-means clustering and
principal component analysis (PCA)
• Involved in converting unstructured data into structured and workable datasets.
• Ability to use a language like Python to work at scale with large data sets and tools
like SQL
• Involved in publishing the python code in the Git - Hub
• Good command on statistical techniques like Regression, Classification & clustering.
• Experience in data visualization techniques for final report communication to clients.
• Expertise in data exploration techniques using various statistical tools.
• Experience using NLP toolkits like Spacy, NLTK, TextBlob
• Experience using data science libraries Pandas, Numpy, Scipy, Seaborn
• Build predictive models and machine-learning algorithms
• Combine models through ensemble modelling
• Experience with exploratory data analysis, statistical analysis,
• Knowledge and skills to turn raw data into information and insight, which can be used
to make business decisions

BE engineering

Data Science certification