Welcome to this Machine Learning repository! This repository is dedicated to my learning and experimenting with various machine learning algorithms for classification and regression tasks.
To be updated...
To be updated...
Here is a brief explanation of each algorithm covered in this repository:
- Classification: Classification is a machine learning task where the goal is to categorize input data into predefined classes or labels. It's used when the output is a category or a class, such as spam or not spam.
- Regression: Regression is a task in which the algorithm predicts a continuous numeric value as an output, such as predicting house prices based on features like square footage and location.
- This refers to the process of teaching a machine learning algorithm to make predictions or decisions based on input data. It involves adjusting the model's parameters to minimize the error between predicted and actual values.
- Data preparation involves cleaning, transforming, and organizing raw data so that it can be used effectively for machine learning. This includes tasks like handling missing values, scaling features, and encoding categorical variables.
- K-Nearest Neighbors is a simple and intuitive classification algorithm. It classifies data points based on the majority class among their k nearest neighbors in the training dataset. The choice of k determines the number of neighbors to consider.
- This section likely provides practical code and demonstrations of how to implement the K-Nearest Neighbors algorithm for classification tasks.
- Naive Bayes is a probabilistic classification algorithm based on Bayes' theorem. It's particularly suited for text classification tasks, like spam detection. Despite its simplicity, it's quite effective.
- This section likely contains code and examples for implementing the Naive Bayes algorithm for classification tasks.
- Logistic Regression is a linear classification algorithm used for binary and multi-class classification. It models the probability of a sample belonging to a particular class using a logistic (S-shaped) function.
- This section probably provides code and guidance on implementing Logistic Regression for classification tasks.
- Support Vector Machine is a powerful classification algorithm that finds the best hyperplane to separate different classes in the data. It aims to maximize the margin between classes.
- Here, you may find instructions on how to implement Support Vector Machines for classification tasks, along with code examples.
- Neural Networks are a class of machine learning algorithms inspired by the human brain. They consist of interconnected nodes (neurons) organized in layers and are used for various tasks, including classification and regression.
- TensorFlow is a popular deep learning framework that simplifies the development and training of neural networks.
- This section likely guides you through building a neural network for classification tasks using TensorFlow.
- Linear Regression is a simple yet powerful algorithm for predicting numeric values. It models the relationship between input features and output values using a linear equation.
- This section may contain code and instructions on implementing Linear Regression for regression tasks.
- It might demonstrate how to use a neural network-based approach for Linear Regression, offering more flexibility in modeling complex relationships.
- This likely focuses on building neural networks for regression tasks using TensorFlow.
- K-Means Clustering is an unsupervised learning algorithm used for grouping data points into clusters based on similarity. It aims to minimize the variance within clusters.
- Principal Component Analysis is a dimensionality reduction technique used to reduce the number of features in the data while retaining the most important information. It helps in simplifying complex datasets.
- This section may include practical implementations of K-Means Clustering and Principal Component Analysis for data analysis and clustering tasks.
Special thanks to FreeCodeCamp for creating the video content and make to learn ML basics.
Contributions to this repository are highly encouraged. If you have additional machine learning algorithms, improvements, or tutorials to share, please feel free to create pull requests. Let's learn and grow together in the exciting field of machine learning!
Happy learning and coding! 🚀🤖