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Common Machine Learning Algorithms!
- 𝗔𝗱𝗮𝗕𝗼𝗼𝘀𝘁:
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!
