Skip to main content

Learn Machine Learning Algorithms

Machine Learning Algorithms with Python Code

Contents of Algorithms 

1. ML Linear regression

A statistical analysis technique known as "linear regression" is used to simulate the relationship between a dependent variable and one or more independent variables.

2. ML Logistic regression

 Logistic regression: A statistical method used to analyse a dataset in which there are one or more independent variables that determine an outcome. It is used to model the probability of a certain outcome, typically binary (yes/no).

3. ML Decision trees

Decision trees: A machine learning technique that uses a tree-like model of decisions and their possible consequences. It is used for classification and regression analysis, where the goal is to predict the value of a dependent variable based on the values of several independent variables.

4. ML Random forests

Random forests: A machine learning technique that uses multiple decision trees to improve the accuracy of predictions. It creates a forest of decision trees and then aggregates the results to make a final prediction. Random forests are used for both classification and regression analysis.


Machine Learning Algorithms

5. ML Gradient Boosting Machines

Gradient Boosting Machines: A machine learning technique that builds an ensemble of weak prediction models, usually decision trees, and combines them to create a strong model. The algorithm iteratively builds the model, adjusting the weights of misclassified samples. Gradient boosting is a powerful technique for classification and regression tasks.

6. ML Naive Bayes

Naive Bayes: A probabilistic machine learning algorithm based on Bayes' theorem, which assumes that the presence of a feature in a class is independent of the presence of other features. It is used for classification problems, such as text classification or spam detection.

7. ML K-Nearest Neighbors

K-Nearest Neighbours: A non-parametric algorithm that is used for classification and regression analysis. It works by finding the k nearest data points in the training set to a given data point and then predicting the label or value based on the labels or values of the nearest neighbours.

8. ML Support Vector Machines

Support Vector Machines: A machine learning algorithm that is used for classification and regression analysis. It works by finding the hyperplane that maximally separates the classes or predicts the values of the target variable. SVMs are commonly used in image classification, text classification, and bioinformatics.

9. ML Principal Component Analysis

Principal Component Analysis: A dimensionality reduction technique that is used to transform a high-dimensional dataset into a lower-dimensional representation. It functions by identifying the directions in which the data vary most and projecting the data onto these directions. PCA is commonly used in data visualization, pattern recognition, and image processing.

10. ML K-Means clustering

K-Means clustering: A clustering algorithm that is used to partition a dataset into k clusters. It works by assigning each data point to the cluster whose centroid is closest to it and then updating the centroids based on the mean of the data points in each cluster. K-means clustering is commonly used in image segmentation, customer segmentation, and market research.

11. ML Hierarchical clustering

Hierarchical clustering: A clustering algorithm that is used to group similar objects into a hierarchy of clusters. It works by successively merging the closest clusters until all objects are in a single cluster. Hierarchical clustering is commonly used in gene expression analysis, image analysis, and social network analysis.

12. ML  Apriori Algorithm

Apriori Algorithm: A data mining algorithm that is used to discover frequent item sets in a transaction database. It works by generating candidate item sets and then pruning the infrequent ones. Apriori is commonly used in market basket analysis and recommender systems.

13. ML Collaborative Filtering

Collaborative Filtering: A technique used in recommender systems to predict user preferences based on the preferences of similar users or items. It works by finding the users or items that are most similar to the target user or item, and then using their preferences to make predictions.

14. ML Singular Value Decomposition

Singular Value Decomposition: A matrix factorization technique that is used to reduce the dimensionality of a dataset or to find the latent factors that explain the variation in the data. It works by decomposing a matrix into three matrices, which represent the left singular vectors, right singular vectors, and singular values.

15. ML Ensemble methods

Ensemble methods: A machine learning technique that combines multiple models to improve the accuracy of predictions. Ensemble methods can be used with any type of model, such as decision trees, SVMs, or neural networks.

16. ML-Convolutional NN - Recurrent NN

Convolutional Neural Networks - Recurrent Neural Networks: A type of neural network architecture that is used for image and sequence analysis, respectively. Convolutional neural networks are designed to process images by using convolutional layers to extract features. Recurrent neural networks are designed to process sequential data by using recurrent layers to model temporal dependencies.

17. ML Reinforcement Learning Algorithms

Reinforcement Learning Algorithms: A type of machine learning algorithm that is used to teach agents how to interact with an environment to maximize a reward. Reinforcement learning algorithms are commonly used in robotics, game AI, and recommendation systems.

18. ML Decision Boundary Algorithms

Decision Boundary Algorithms: A machine learning algorithm that is used to classify data by creating a decision boundary that separates different classes. Decision boundary algorithms include logistic regression, SVMs, and decision trees.

19. ML Association Rule Mining Algorithms

Data mining algorithms called association rule mining are used to identify intriguing connections between variables in massive datasets. Association rule mining algorithms are commonly used in market basket analysis, web mining, and bioinformatics.

 20. ML Bayesian networks

Bayesian networks: Algorithms for Mining Association Rules, an approach to data mining that looks for intriguing connections between different variables in a large dataset. Bayesian networks are commonly used in medical diagnosis, risk assessment, and decision support systems.

                                    CONTINUE TO (Linear regression)


Labels:
Location: Guntur, Andhra Pradesh, India

Comments

Post a Comment

Popular posts from this blog

What is Linear regression

Linear regression A lgorithm Concept of Linear regression In order to model the relationship between a dependent variable and one or more independent variables, linear regression is a machine learning algorithm. The goal of linear regression is to find a linear equation that best describes the relationship between the variables. Using the values of the independent variables as a starting point, this equation can then be used to predict the value of the dependent variable. There is simply one independent variable and one dependent variable in basic linear regression. The linear equation takes the form of y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope of the line, and b is the y-intercept. For example, let's say we have a dataset of the number of hours studied and the corresponding test scores of a group of students. We can use linear regression to find the relationship between the two variables and predict a student's test scor
Post a Comment
Read more

What is Decomposition Algorithm

Singular Value Decomposition Algorithms Singular Value Decomposition concepts Singular Value Decomposition (SVD) is a matrix factorization technique used in various machine learning and data analysis applications. It decomposes a matrix into three separate matrices that capture the underlying structure of the original matrix. The three matrices that SVD produces are:   U: a unitary matrix that represents the left singular vectors of the original matrix. S: a diagonal matrix that represents the singular values of the original matrix. V: a unitary matrix that represents the right singular vectors of the original matrix. Here is an example of how SVD works : Suppose we have a matrix that represents the ratings of users for different movies. We can use SVD to decompose this matrix into three separate matrices: one matrix that represents the preferences of users, one matrix that represents the importance of each movie, and one matrix that captures the relationship between users and m
Post a Comment
Read more

What is Logistic regression

Logistic Regression  Algorithm Concept of Logistic Regression A machine learning approach called logistic regression is used to model the likelihood of a binary outcome based on one or more independent factors. The goal of logistic regression is to find the best-fitting logistic function that maps the input variables to a probability output between 0 and 1. The logistic function, also known as the sigmoid function, takes the form of:   sigmoid(z) = 1 / (1 + e^-z)   where z is a linear combination of the input variables and their coefficients. For example, let's say we have a dataset of customer information, including their age and whether they have purchased a product. We can use logistic regression to predict the probability of a customer making a purchase based on their age. Logistic Regression  Algorithm: Define the problem and collect data. Choose a hypothesis class (e.g., logistic regression). Define a cost function to measure the difference between predicted and actual
Post a Comment
Read more

What is Naive Bayes algorithm

Naive Bayes Algorithm with Python Concepts of Naive Bayes Naive Bayes is a classification algorithm based on Bayes' theorem, which states that the probability of a hypothesis is updated by considering new evidence. Since it presumes that all features are independent of one another, which may not always be the case in real-world datasets, it is known as a "naive". Despite this limitation, Naive Bayes is widely used in text classification, spam filtering, and sentiment analysis. Naive Bayes Algorithm Define the problem and collect data. Choose a hypothesis class (e.g., Naive Bayes). Compute the prior probability and likelihood of each class based on the training data. Use Bayes' theorem to compute the posterior probability of each class given the input features. Classify the input by choosing the class with the highest posterior probability. Evaluate the model on a test dataset to estimate its performance. Here's an example code in Python for Naive Bayes: Python cod
Post a Comment
Read more