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Python, Machine Learning, Data Analysis

Location:
Vasant Nagar, Karnataka, India
Posted:
January 06, 2020

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Resume:

CURRICULUM VITAE

KARAN SINGH

E-Mail ID : ada72j@r.postjobfree.com

LinkedIn :https://www.linkedin.com/in/karan-singh-652937182/

Git Hub :https://github.com/kstonny

Date of Birth : 24 MAY 1993

Contact No. : 871-***-****

Career Objective

To work with a company that appreciates innovation so that I can enhance my knowledge and skills to give my best for the growth of the company.

Snapshot

•MSC student having 74 % throughout, from Pt. Shambhunath Shukla University.

•Willingness to learn new tools & technologies.

•Punctual & self-motivated.

Certification Details

•IBM Certification in Machine Learning & Artificial Intelligence.

•Certification course in Data Science from Internshalla.

•Certified in Python Programming.

Technical Skill Sets

Programming Language : C, C++, Java, Python, R

Web Technologies : HTML, JavaScript.

Database Package : Oracle 10g, MySQL.

IDE : Jupyter Notebook, Pycharm, Microsoft Visual Studio.

Libraries : Pandas, Numpy, Scikit Learn, Matplotlib, Seaborn

Machine Learning : Linear Regression, Logistic Regression, KNN, K- Means, Decision Tree, Random Forest, NLP

Deep Learning : ANN, CNN, RNN, LSTM, PCA.

Project Details Project I

Duration : August 2019

Title : Google-Text-To-Speech

Description : The model coverts text into audio.

Environment/Languages : Python, Jupyter Notebook.

Academic Details

2016 - 2018 Master of Science, Pt. Shambhunath University,Shahdol,Madhya Pradesh

•74%

2012 - 2015 Bachelor of Science, Pt, Shabhunath Sukla P.G. College, APS University

•57.60%

2009 - 2011 H.S.C, Madhya Pradesh Board.

St. Jude's C.M. Hr. Sec. School, Shadol, Madhya Pradesh

•68%

2008 - 2009 S.S.C, Madhya Pradesh Board.

St. Jude's C.M. Hr. Sec. School, Shadol, Madhya Pradesh

•66%

Training & workshops

Internshala : Book's inventory n detail using python

Internshala : Logistic and Linear Regression model applied on given data

Social prachar : Bulid a model to predict the most purchase using jupyter noetbook and python liberary



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