ML BASIC ALGORITHMS

  Common Machine Learning Algorithms

1️⃣ Linear Regression 

->Used for predicting continuous values.

->Models the relationship between dependent and independent variables by fitting a linear equation.

 -> A foundational model that predicts a continuous outcome variable based on one or more predictor variables.

2️⃣ Logistic Regression 

->Ideal for binary classification problems.

->Estimates the probability that an instance belongs to a particular class.

-> Used for binary classification tasks. It estimates the probability that a given instance belongs to a particular category

3️⃣ Decision Trees 

->Splits data into subsets based on the value of input features.

->Easy to visualize and interpret but can be prone to overfitting.

-> A flowchart-like structure where each node represents a feature, each branch a decision rule, and each leaf a class label

4️⃣ Random Forest 

->An ensemble method using multiple decision trees.

->Reduces overfitting and improves accuracy by averaging multiple trees.

5️⃣ Support Vector Machines (SVM) 

->Finds the hyperplane that best separates different classes.

->Effective in high-dimensional spaces and for classification tasks.

-> Finds the hyperplane that best divides a dataset into classes

6️⃣ k-Nearest Neighbors (k-NN) 

->Classifies data based on the majority class among the k-nearest neighbors.

->Simple and intuitive but can be computationally intensive.

-> Classifies a data point based on how its neighbors are classified

7️⃣ K-Means Clustering 

->Partitions data into k clusters based on feature similarity.

->Useful for market segmentation, image compression, and more.

-> An unsupervised clustering algorithm that groups data into 'K' number of clusters

8️⃣ Naive Bayes 

->Based on Bayes' theorem with an assumption of independence among predictors.

->Particularly useful for text classification and spam filtering.

-> Based on Bayes' theorem, it's particularly suitable for high-dimensional dataset

9️⃣ Neural Networks 

->Mimic the human brain to identify patterns in data.

->Power deep learning applications, from image recognition to natural language processing.

-> Inspired by the human brain, it consists of interconnected neurons

🔟 Gradient Boosting Machines (GBM) 

->Combines weak learners to create a strong predictive model. 

->Used in various applications like ranking, classification, and regression.

11 Principal Component Analysis (PCA)

-> A dimensionality reduction technique that transforms data into a new coordinate system

12 Ada Boost

-> An ensemble method that adjusts weights of misclassified data points

Understanding these algorithms is the first step towards leveraging the power of machine learning. Each has its own strengths and best-use scenarios. Explore and experiment to find the right fit for your data challenges!