Machine Learning Models Portfolio

Welcome to my Machine Learning Models Portfolio! This collection showcases the various machine learning models I developed and refined during my learning journey from May to August. This period marked my dedicated efforts to understand and implement effective machine-learning techniques. Each model presented here represents a step forward in my proficiency, and I'm excited to share my progress with you.

Table of Contents

• Introduction
• Models
• Contacting

Introduction

In this repository, you'll find a series of machine-learning models that I meticulously worked on between May and August. This span of time was dedicated to mastering the art of crafting efficient and accurate machine-learning solutions. As a passionate learner in the field, I believe in continuous improvement and innovation, which is reflected in the progression of models presented here. The culmination of this effort resulted in me being awarded a prestigious Machine Learning Certificate from Cornell Tech. This certificate signifies the successful completion of my dedicated learning period and the practical application of machine-learning concepts to real-world problems

Models

In each folder, there are different types of models.

Final Project: Orchestrated a synergy between TF-IDF and Logistic Regression within a structured pipeline, meticulously optimizing hyperparameters via ROC_AUC analysis for an NLP machine learning model.

KNN & Decision Trees: Compiled Jupyter notebooks delineating the progressive acquisition of expertise in K-nearest neighbors and Decision Trees, encapsulating an analytical journey.

Logistic Regression: Navigated the nuanced terrain of model selection and intricate logistic regression model optimization techniques.

Neural Networks: Leveraged the Keras library to architecturally empower a sentiment analysis model, enabling precise binary classification of book reviews into positive or negative sentiments, exemplifying neural network application.

Regression Models: Engaged in a comprehensive study of diverse regression models, acquiring adeptness in strategic model stacking and comparative analysis, underscoring an advanced proficiency in regression methodologies.

(IN DEVELOPMENT...)

Each model directory contains its own README file, providing more details about the model's architecture, dataset used, training process, and evaluation metrics.

(IN DEVELOPMENT...)

Contacting

Feel free to reach out to me if you have any questions, suggestions, or collaboration opportunities. I'm always excited to connect with fellow developers and enthusiasts. You can contact me through the following channels:

Email: noahcdufresne@gmail.com

LinkedIn: noahdufresne

Twitter: noahthedufresne

Don't hesitate to drop a message. Looking forward to hearing from you!