Location : Ahmedabad, India, ,
Experience : 2.5 Year
Rate: $12 / Hourly
Availability : Immediate
Work From : Offsite
Category : Information Technology & Services
Microsoft Malware Classification
● The goal of the project is to classify the type of Malware File using the Machine Learning technique.
● Performed Univariate Analysis on size of .asm & .bytes files as well as on some of the opcodes of asm files.
● Tools & Technologies: sklearn, XGboost, XGBClassifier, CalibratedClassifierCV, RandomForestClassifier, RandomSearchCV, SelectFromModel, Dask, log_loss, confusion_matrix, train_test_split, codecs, multiprocessing, tqdm, Pandas, numpy, pickle, imageio, seaborn Custom Ensemble
● Custom Ensemble using Decision Trees for Jane Street Market Prediction problem.
● Tools & Technologies: random, math, pickle, numpy, pandas, clone, f1_score, DecisionTreeClassifier CalibratedClassifierCV, RandomizedSearchCV, GroupTimeSeriesSplit.
● Using various techniques to count and detect the number of Faces from the image, and then perform Face Recognition using Deep Learning Techniques such as Face Net (Google pre-trained model) and Transfer Learning.
● Tools and Technologies: OpenCV, mtcnn, and TensorFlow. Legal document’s classification using ML & DL
● Natural Language Processing (NLP) based project, using OCR tool user can upload legal documents in Image (JPG, JPEG, PNG), Pdf and Docx format, then text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content. Text Classification we have proposed Machine Learning and Deep Learning approaches. For the Machine Learning approach KNN, SVC & Naïve Bayes are used while the BERT model is used for the Deep Learning approach.
● Tools and Technologies: Pandas, NumPy, OpenCV, Tensorflow, scikitlearn, Django, Nodejs.