Machine Learning Engineer

Home

People

ID : 1810

Education level : Bachelor's degree, Master's degree

Work experience level : Beginner- (internship- 1 year experience)

Work experience in total : Years

Job type : Online

Job time : Maandelijks

Last date of registration :

2022-12-30

Motivated self-learning fresher looking for a job to build a strong base to attain my goal ‘Renowned career women’. Resolutely give in hard and smart work for our company’s success and willing to update myself with the required skills needed for our company’s growth.

Machine Learning Engineer - The Sparks Foundation- (April 2022 - May 2022)

Predicted the optimum number of clusters & represent it visually from "Iris" data in Python.

Business Analyst Intern - Technocolab software solutions - (April 2022 - May 2022)

Predicted used cars price using machine learning models & made a deployment using Django framework

Languages
- Python, R
Packages/libraries
- Pandas, NumPy, Seaborn, SciPy, Matplotlib, Scikit-learn, BeautifulSoup, ggplot2, NLP, plyr.
Machine Learning
- Linear Regression, Logistic Regression, Decision trees, Random Forest, Association Rule Mining (Market Basket Analysis), Clustering (K-Means, Hierarchal), SVM (Support Vector Machines).
Statistical Tools
- Time Series, Regression models, confidence intervals, principal component analysis,
  Dimensionality Reduction.
Visualization Tools
- PowerBI
Databases
- MYSQL, Mongodb

Self-Learner
Time Management
Teamwork
Problem Solving
Situation Adapter

Attended a workshop on career development skills and effective presentation.
Attended conference HICAAMMC(19) and ACTSUM.
Participated in the intensive training program in effective communication and
placement preparation (TECH & BEC).
Topper of the school in SSLC with 96%.
Certifications
- Introduction to Industry 4.0 and Industrial Internet of Things. - January -Apr 2021
- Data Visualization with R - January 2021
- Python program - June 2021 - beginner to advance
- SQL for Data Science - June 2021

Python, R, PowerBI, MYSQL, Mongodb, Machine Learning: Linear Regression, Logistic Regression, Decision trees, Random Forest, Association Rule Mining, Clustering (K-Means, Hierarchal), SVM, Time Series, Regression models, confidence intervals, principal component analysis, Dimensionality Reduction.