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Hi 👋, I'm Atikul Islam Sajib

Welcome to my profile! My name is Atikul Islam Sajib, and I am currently an international Computer Science student in Germany, having earned my Bachelor of Science in Computer Science and Engineering (CSE) from the Department of Computer Science and Engineering at United International University (UIU).

I specialize in the fields of Machine Learning Theory and Algorithms, Bioinformatics, and Computer Systems. My passion lies in the construction and innovation of algorithms within the Machine Learning domain, where I aim to develop models that significantly enhance algorithmic performance and applicability.

With over four years of experience as a Machine Learning Engineer and Research Assistant, I have a proven track record of developing successful algorithms and predictive models that have notably increased business efficiency across various industries. My expertise extends to implementing a wide range of Machine Learning and Deep Learning techniques, including but not limited to Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forest, Adaboost, Gradient Boosting, and XGboost. I excel in clustering, regression, and classification tasks, utilizing my skills to tackle complex business challenges.

I am particularly proficient in Exploratory Data Analysis (EDA) and feature engineering, which allows me to uncover underlying patterns in data and optimize model performance. Throughout my career, I have contributed to over 50 Data Science projects, addressing a variety of business problems through the application of Machine Learning and Deep Learning methodologies such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). My work encompasses domain-specific solutions, and I have extensive experience in deploying models on cloud platforms and edge devices, further demonstrating my commitment to leveraging technology to solve real-world issues.

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Languages and Tools:

aws azure docker flask java mysql pandas python pytorch scikit_learn tensorflow

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 atikul-islam-sajib

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Atikul Islam Sajib's Projects

ac-gan icon ac-gan

The Auxiliary Classifier GAN, or AC-GAN for short, is an extension of the conditional GAN that changes the discriminator to predict the class label of a given image rather than receive it as input.

bt-seg icon bt-seg

This repo will be used for segmentation task, based on Brain Tumor using U-Net & AttentionUNet architecture and several loss functions

deepml-credit-card-defaulter-prediction icon deepml-credit-card-defaulter-prediction

This work explores ways of combining the advantages of deep learning and traditional machine learning models by building a hybrid classification scheme. The first few layers of a convolutional neural network are utilized for feature extraction and selection.

dggan-pytorch icon dggan-pytorch

DCGANs are designed for the purpose of generating high-quality synthetic data, often images.

dncnn icon dncnn

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

fakeimagegenerate icon fakeimagegenerate

This repo will create the Fake Images using Celeb Images using GAN. It can generate anime images, skin cancer images etc.

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