I am now available to help with your Python machine learning projects. My specialties are Data Science, Data Analysis (inferential, descriptive), Data Visualization, Tensor Flow based Deep Learning, Pytorch based Deep Learning, and Python Machine Learning. Python and its IDE, frameworks, and libraries are all well-known to me. I have a number of online certifications in the same field. I have a number of conference and journal publications in the same domain.
As a data engineer, I've participated in the following projects:
Design and publication of Blind Image Quality Assessment using Gaussain Process Regression Algorithm.
Design and implementation of Reduced Reference Image Quality Assessment Algorithm using differential features and XGBOOST based regression algorithm.
Deep learning based Novel perceptual quality assessment algorithm design and implantation.
Image Super Resolution
Low light Image Enhancement
Noisy image Enhancement
Image Restoration
Medical Image segmentation
Brain conntecome superresolution
Image to Image Translation
Document clustering
BM-25 based search engine design
TF-IDF model design and implementation from scratch
Satellite Image Classification
Generative Adversarial network for image to image translation
COVID-19 Data Analysitics
Sociodemogrpahic study of effect of COVID-19 on the mental helalth of students
Transformer helath prediction
Sensor data prediction using LSTM based multi-time series network.
Man more
I can also manage any projects relating to the following:
Supervised learning, Unsupervised learning, Repetition learning (Classification,Regression,Clustering Related Projects)
CNN,CNN-with attention,RNNs, LSTM,GRU, Transformer Others (Experience with Time series analysis)
Object detection, face detection, and (Experience with Computer Vision and Deep Learning)
Moreover, I can manage any project connected to these:
Data Analysis (Pandas, Numpy,SCPIY Others)
Data Visualisation ( Matplotlib, Seaborn, Plotly)
Dimensionality Reduction ( PCA, LDA, Autoencoder )
Linear Regression, Logistic Regression, Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbours, Random Forest, Gaussian Process, and Gradient Boosting regression, and classification Variants
Frameworks:
Jupyter Notebook
Pycharm
Google Colab
Spyder
PaperSpace