I am very skilled in domain of Machine Learning, Deep Learning and computer vision. I have done several projects to enhance not only my technical skills but also my interpersonal skills. Currently I am serving as a Data Scientist in ITU working on various aspect of Deep Learning.
am active in research with interest in Machine Learning, Deep Learning, Computer Vision and Data Mining. I have done projects with TensorFlow, PyTorch, scikit, OpenGymAI, NLTK, mediaPipe, algolia, faiss, regular expression, huggingface, OpenCV (Python), Weka and Rapid Miner.
Perform some tasks based on the above conditions.
My area of expertise covers:
evolutionary algorithms
neural networks
CNN (Sequetnial,Alex net, Google net, Resnet etc)
Siamese neural networks
Fewshot learning
Decision trees
Navie bayes
Apriori
FPGrowth
FPMAX
Linear Regression
Logistic regression
SVM
Object detection
Object Localization
Object recognition
Data preprocessing
Dimensionality reduction
PCA
HoG/ Sift features
Random Forest
K means
KNN
DBSCAN
Tools:
Python
Anaconda
Juypter Notebook
Kaggle
Tensor flow
Weka
Rapid Miner
PyTorch
computer vision
PIL
Mediapipe
Projects:
SMS Spam Detection Model
- Used Multinomial Naïve Bayes algorithm implementation method to make predictions on our dataset for SMS Spam detection.
- Successfully achieved actionable insights from dataset
- Used different libraries to visualize the data for better understanding
- Wrote Technical report on the project
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Bitcoin Price Prediction
- Predict Bitcoin Price for the next 30 days with Machine Learning Model Support Vector Machines(Regression)
- Split the data into 80% training and 20 testing
- Excellent Model Accuracy Score
Tools & Frameworks Used
TensorFlow Weka MySQL
Keras Mediapipe AWS, Azure
PyTorch OpenAI Gym OPenAiGym
OpenCV Stata Sickit-learn
Regular Expressions NLTK
Stella (for decision making)
Econometrics
Numpy, pandas, matplotlib, Scipy, Grafana
Algolia, faiss, huggingface, Docker &
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