This GitHub repository contains all the machine learning models that I practiced when I began learning. Each model offers a straightforward implementation on a dataset that is publicly accessible and can be obtained online.
There are numerous online resources available today for learning machine learning concepts, but the most effective method to learn them and understand how they function will always be to apply them to datasets and work on real-world problems. So I hope these examples will help you in learning and applying your ideas.
The datasets can be found at kaggle with the names specified
Data preprocessing is an essential step in any machine learning pipeline. It involves preparing raw data to make it suitable for modeling. This process includes handling missing data, scaling and normalizing data, encoding categorical variables, and splitting data into training and testing sets.
Regression is a type of supervised learning that predicts a continuous output variable based on one or more input variables. Simple Linear Regression predicts a target variable based on one input variable. Multiple Linear Regression predicts a target variable based on multiple input variables. Polynomial Regression fits a nonlinear relationship between the target and input variables. Support Vector Regression (SVR) is a regression algorithm that uses support vectors to find the best fit. Decision Tree Regression and Random Forest Regression are tree-based models that predict continuous variables.
Classification is a type of supervised learning that predicts a discrete output variable based on input variables. Logistic Regression is a classification algorithm that predicts a binary output variable. K-Nearest Neighbors (K-NN) predicts the class of a new observation based on the class of its k-nearest neighbors in the training set. Support Vector Machines (SVM) and Kernel SVM use hyperplanes to separate classes in high-dimensional space. Naive Bayes is a probabilistic classification algorithm. Decision Tree Classification and Random Forest Classification are tree-based models that predict discrete variables.
Clustering is an unsupervised learning technique that groups similar observations together based on their characteristics. K-Means is a popular clustering algorithm that partitions the data into k clusters. Hierarchical Clustering creates a tree-like structure of clusters that can be visualized using a dendrogram.
Association Rule Learning is a method for discovering relationships between variables in a large dataset. Apriori is an algorithm that finds frequent itemsets in transactional datasets. Eclat is an algorithm that finds frequent itemsets using a depth-first search.
Reinforcement Learning is a type of machine learning that focuses on how agents take actions in an environment to maximize a reward. Upper Confidence Bound (UCB) is an algorithm that balances exploration and exploitation of actions to maximize the reward. Thompson Sampling is a Bayesian algorithm that selects actions based on their probability of being the best action.
Natural Language Processing (NLP) is a field of study that focuses on the interaction between computers and humans using natural language. The bag-of-words model is a technique used in NLP to represent text as a numerical vector that can be used in machine learning models. There are several algorithms used in NLP, including sentiment analysis, named entity recognition, and topic modeling.
Deep Learning is a subfield of machine learning that uses neural networks to model complex relationships between input and output variables. Artificial Neural Networks (ANNs) are a type of neural network that can be used for both regression and classification problems. Convolutional Neural Networks (CNNs) are a type of neural network that is commonly used for image recognition and classification.
Dimensionality Reduction is a technique used to reduce the number of input variables in a dataset while retaining the most important information. Principal Component Analysis (PCA) is a method for reducing the dimensionality of data by finding the linear combinations of variables that explain the most variance in the data. Linear Discriminant Analysis (LDA) is a method for finding the linear combinations of variables that maximize the separation between classes. Kernel PCA is a non-linear extension of PCA that uses kernel methods to find non-linear combinations of variables.