Machine Learning Workshop Project

Introduction

Welcome to my Machine Learning Workshop Project! This ongoing project kicks off with a deep dive into feature extraction, the crucial first step in preparing our data for more complex machine learning tasks. As the project evolves, I will be exploring additional aspects of AI and machine learning, adapting and expanding our objectives and methodologies.

Current Stage: Feature Extraction

Overview

Feature extraction lays the groundwork for any machine learning project by transforming raw data into a more manageable, informative set of features that effectively represent the underlying problem to our algorithms.

Data Source

I'm utilizing the rich datasets provided by MindBigData, which offer a diverse range of data points perfect for our initial and future experiments.

Techniques Employed

• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Autoencoders

I'm implementing these techniques to reduce dimensionality, visualize high-dimensional data, and learn efficient data representations.