Shakib Khan

Data Analyst &

Machine Learning Engineer

Dhaka, Bangladesh

(+880) 131*******

adlyoh@r.postjobfree.com

https://www.linkedin.com/in/sakibkhan6 6

https://github.com/Shakib-IO

https://sakibkhan.hashnode.dev/

EXPERIENCE

The Sparks Foundation, Internship

January 2021 - March 2021

Preliminary analysis & Cleaning data using pandas, numpy Visualizing data using Matplotlib, Seaborn

Devised high-performance algorithms that give high accuracy Conceptualized and implemented Clustering

Using Tableau & Power BI for Visualization and Analysis Covid-19 Dataset

Apply Exploratory Data Analysis

Worked with Supervised, Unsupervised Algorithms

Build a market prediction with 98% accuracy

Project#03 Cluster Analysis (Data Analysis)

Construct an ideal cluster music for Spotify playlist Collecting the Spotify datasets

DBSCAN clustering

Hierarchical Clustering

Visualization using t-SNE

Apply Exploratory data analysis

Project#02 Diminishing Image Noise Using Deep Learning (CV) Collecting the image datasets

Apply fundamental & traditional filters to find what kind of noise the picture has.

Firstly implement AUTOENCODER for denoising.

Apply REDNet(Residual net) for image segmentaion

Used the Multi-level Wavelet-CNN(MWCNN)

PRIDNet is a scalable network that gives a pretty good result. Project#01 Applied Machine Learning Algorithms (ML) Implemented Cross-Validation in Logistic Regression K nearest neighbors (KNNalgorithms) apply to find whether Diabetes or not using

Naive Bayes Classifier on Text Classification

Unsupervised Learning ( PCA, Clustering )

Support Vector Machine (SVM)

RandomForest classifier on Iris datasets

PROJECT

SKILLS

PROGRAMMING

Python

iPython Noteboo k

SQL, MYSQL

C, C++, Java

DATA & DATABASE

Database Design & Managemen t

Pattern & Trend Identificatio n

Visualization of Insights Dat a

Data Cleaning/ELT

Data Modelling

MACHINE LEARNING

Linear & Logistic Regressio n

Cluster Analysi s

Random Forest & Decision Tree s

Convolutional Neural Networ k

Support Vector Machine (SVM )

Neural Network

Computer Vision (CV )

Recurrent Neural Network(RNN)

SOFTWARE & FRAMEWORK

Gi t

TensorFlo w

Keras & Scikit-Lera n

Matplotli b

Djang o

Tablea u

Amazon Web Services(AWS )

Power B I

Hadoop

Coursera Certifiticate

ACM Voluntary Program

Awards

EDUCATION (Optional)

Udayan Secondary School ( SSC )

Science, 2011 - 2013

Model School & Collage ( HSC )

Science, 2013 - 2015

North South University ( BSC )

2017 - 2021

Computer Science & Engineering

CGPA: 3.38/4.00

Project#04 ExploreBD (Advance Database SQL)

Design Schema for data warehouse

Create DTS packages for CRUD operations

Used XML, SOAP,RESTAPI and Postman

Working on structured and unstructured data

Docker implementaion with raw php

Write queries and analysis result

PROJECT

EXTRAS

(Awards/Certificates/Activities)

Coursera, Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning May, 2020 - Present

The Specialization course helps me learn how to use a framework like TensorFlow, Machine learning algorithms, and Deep learning and implement those principles to build and apply scalable models to real-world problems. To develop a deeper understanding of how neural networks work. Learn how to train images with TensorFlow and some text-based problem analysis with the neural network. After complete this course, I’m able to solve some real-world problems.

Coursera, Introduction to Data Science in Python

August, 2020 - Present

Learned how to Program in Populer data science Languages like python and R. Properly manipulate data, visualize data, clean messy data, mining data, make predictions about modes using statistics and machine learning, and how to use tools like tableau, Powe BI. Coursera, AWS Fundamentals

September, 2020 - Present

This Specialization course helps me learn some basics about AWS and its other tools like AWS cloud & migration to cloud services. I know about S3, EC2 instances, EBS, EFS, and Virtual network. Learn building serverless applications with a wide range of AWS services, including AWS Lambda, Amazon API Gateway, Amazon DynamoDB, and Amazon Lex. For exploring more about ML/AI learn amazon Sagemaker. By doing this course, I am now able to build applications using AWS.

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