I'm a passionate and skilled Machine Learning enthusiast with expertise in building and deploying end-to-end ML and NLP projects. My journey has been deeply rooted in hands-on learning and mastering modern tools, frameworks, and best practices in MLOps. Below are the core skills and technologies I have acquired:

• Proficient in Supervised Learning techniques such as:
  • Decision Trees 🌳
  • Random Forests 🌲
  • Logistic Regression 📊
  • Support Vector Machines (SVM) ✖
• Expertise in Unsupervised Learning algorithms like:
  • K-Means Clustering 📊
  • Hierarchical Clustering 🏔
  • DBSCAN for anomaly detection 🌌
• Strong grasp of Dimensionality Reduction methods such as Principal Component Analysis (PCA).

• Deep understanding of Convolutional Neural Networks (CNNs) for complex tasks in image recognition and beyond.
• Proficient in implementing and training Deep Learning models to solve real-world problems.

• Expertise in cutting-edge NLP techniques, from sentiment analysis to advanced text classification.
• Developed and deployed NLP models using real-world datasets, focusing on various domains.

• Version control using Git and GitHub for managing ML projects.
• CI/CD pipelines for automating model development and deployment.
• Proficient in using Docker for containerizing applications to ensure smooth deployment across environments.
• Expertise in using MLFlow and BentoML for tracking experiments and deploying models.
• Collaborative ML project management using Dagshub.

• Hands-on experience in implementing the complete lifecycle of ML projects, from data preparation to model deployment.
• Skilled in working on real-world projects, focusing on both theoretical knowledge and practical application.

• Gradient Boosting Algorithms such as:
  • XGBoost and AdaBoost.
• Anomaly Detection 🔍
• Familiarity with cloud-based deployment strategies for scalable model hosting.