🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

1. Supervised learning with scikit learn

   • Classification
   • Regression
   • Fine Tuning

2. Unsupervised learning in Python

   • Clustering
   • Hierarchical Clustering and t-SNE
   • Dimensionality Reduction using PCA

3. Linear Classifiers in Python

   • Logistic Regression and SVM
   • Loss Functions
   • Logistic Regression
   • Support Vector Machines

4. Machine Learning for Time-Series Data in Python

   • Machine Learning Primer and Time Series
   • Time series as input to Model
   • Predicting time series data
   • Validating and Inspecting Time Series Model

5. Machine Learning with Tree-Based Models in Python

   • Classification and Regression Trees
   • The Bias-Variance Tradeoff
   • Bagging and Random Forest
   • Boosting
   • Tuning

LARGE LANGUAGE MODELS

1. Basics of LLMs in Python

   • Basics of Large Language Models
   • Fine tuning LLMs
   • Evaluating LLM Performance

2. Deep Learning for Text with Pytorch

   • Basics of Deep Learning with Pytorch
   • Text Classification with PyTorch
   • Text Generation with PyTorch
   • Advanced Topics in Deep Learning with PyTorch

3. Transformer Models with PyTorch

   • The Building Blocks of Transformer Models
   • Building Transformer Architectures

4. Llama Fundamentals

   • LLMs and Llama
   • Fine-Tuning with LLama 3

5. Developing LLM Applications with LangChain

   • Introduction to LangChain & Chatbot Mechanics
   • Chains and Agents
   • Retrieval Augmented Generation (RAG)