ML for Beginners (Examples)

This repository contains a collection of Machine Learning code examples, showcasing various techniques and algorithms using Python. It includes implementations for the following tasks:

• Linear Regression (Multiple Variables)
• Linear Regression (Single Variable)
• Topic Modeling using BERT
• Classification & Variability Task for Parkinson’s Data
• Naive Bayes Classifier

Each file is designed to demonstrate the implementation of the respective algorithm and provide insights into how these models are used for different types of data and tasks.

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Table of Contents

• Linear Regression (Multiple Variables)
• Linear Regression (Single Variable)
• Topic Modeling using BERT
• Classification & Variability Task for Parkinson's Data
• Naive Bayes Classifier

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Linear Regression (Multiple Variables)

This code demonstrates linear regression with multiple independent variables (features) used to predict a target variable.

Code:

• Linear_regression_multi_variable.ipynb

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Linear Regression (Single Variable)

In this example, we implement a simple linear regression model to predict a dependent variable using a single independent variable.

Code:

• Linear_regression_one_variable.ipynb

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Topic Modeling using BERT

This example showcases how to perform topic modeling on a text dataset using BERT (Bidirectional Encoder Representations from Transformers). BERT is used to capture the contextual meaning of words in a sentence.

Code:

• ML_BERTopic_model.ipynb

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Classification & Variability Task for Parkinson’s Data

In this task, we use machine learning to classify and analyze the variability in Parkinson's disease data. The goal is to classify patients into different categories based on the features of their medical records and assess variability in their condition.

Key Concepts:

• Feature engineering for medical data
• Classification using algorithms such as Logistic Regression, Random Forest, etc.
• Analyzing variability within the dataset

Code:

• ML_Classification_Parkinson.ipynb

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Naive Bayes Classifier

This example demonstrates the implementation of the Naive Bayes algorithm, a simple but effective method for classification tasks, particularly useful for text classification.

Code:

• Naive_Bayes.ipynb

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