Muhammad Rizwan S.

I have participated in various research and commercial machine learning projects. I also have good research experience and some publications that allow me to understand and implement state-of-art machine learning models and use them for new applications. I am a professional developer that enjoys creating Deep Learning, Machine Learning, and Computer Vision solutions. I have used many types of neural networks to solve complicated real-world issues in just four short years. Through online courses like (CS229 Stanford University, taught by Prof. Andrew Ng), I have studied a wide range of significant ML and NLP topics, but the ones I have covered practically and theoretically are logistic and linear regression, locally weighted linear regression, PCA, distribution fitting, neural network, and backpropagation algorithm, Convolutional neural network (ConvNet), dropout regularisation technique, K means, linear SVM, gaussian mixture model, and Gaussian process are a few of the terms used to describe deep learning and unsupervised feature learning. Additionally, I have worked on preprocessing, clustering, and feature engineering. Skills: ☆ Python: Open CV, Pandas, Numpy, Scikit-Learn, Tensorflow, Keras, Matplotlib, Seaborn ... ☆ Big Data: Pyspark, Hive, Scala ☆ Machine Learning: Ensemble Learning, Decision tree, Liner Regression, SVM, SVC Random Forest, Xgboost, LightGBM, Catboost, LR, Kmeans ... ☆ Deep Learning: CNN, DNN, RNN, LSTM, Wide&Deep, Cross Validation, Yolov5,yolov6,yolov7, Segmentation, Semantic Segmentation, Classification, Hyperparameter tunning... ☆Image Preprocessing: PCA, ZCA, Brightness Level Set, Image Resize, Image Augmentation, Rotation ☆ Data Analysis ☆ SQL ☆ Git