Machine Learning Exercises Repository
Welcome to the Machine Learning Exercises repository! This repository contains a collection of exercises covering various topics in machine learning. These exercises are designed to help you understand and implement fundamental concepts in machine learning through hands-on practice.
Overview Machine learning is a field of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming. It encompasses a wide range of techniques and methodologies, each serving different purposes and applicable in various domains.
In this repository, you will find exercises covering the following topics:
Linear Regression: Linear regression is a linear approach to modeling the relationship between a dependent variable and one or more independent variables. These exercises will help you understand how to implement and apply linear regression models to real-world datasets.
Logistic Regression: Logistic regression is a statistical method used for binary classification tasks. These exercises will guide you through implementing logistic regression models and understanding their applications.
Decision Trees: Decision trees are a popular machine learning technique used for classification and regression tasks. These exercises will explore the construction and evaluation of decision trees and their variants like Random Forest and Gradient Boosting.
Support Vector Machines (SVM): SVM is a supervised learning algorithm that can be used for classification or regression tasks. These exercises will cover the implementation and optimization of SVM models.
Clustering: Clustering is an unsupervised learning technique used to group similar data points together. These exercises will introduce you to popular clustering algorithms such as K-Means and hierarchical clustering.
Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving its important information. These exercises will cover methods like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE).
Evaluation Metrics: Evaluation metrics are used to assess the performance of machine learning models. These exercises will help you understand various evaluation metrics such as accuracy, precision, recall, F1-score, and ROC curves.