AI is the new hot topic in the technology industry, but it starts when you learn the basics of machine learning🤖
Without learning classical ML algorithms, It is not a good decision to get into advanced transformer models
Here are the top 10 ML algorithms you should know
1. Linear Regression 📈:
A simple yet powerful algorithm used for regression tasks.
It finds the best-fit line through data points, making predictions based on a linear relationship between input features and the target variable.
2. Logistic Regression 📊:
Although its name contains "regression," it is primarily used for binary classification problems.
It estimates the probability of an input belonging to a particular class with its probability
3. Decision Trees 🌳:
A versatile algorithm that can be used for both classification and regression tasks.
It creates a tree-like structure to make decisions based on feature splits. The main aim is to make homogenous splits for accurate DTs.
4. Random Forests 🌳🌳:
An ensemble method that builds multiple decision trees and combines their outputs to improve accuracy and reduce overfitting.
One of the best ML algorithms and a highly effective one.
5. Support Vector Machines (SVM) 🎛️:
A powerful algorithm for both classification and regression tasks.
It tries to find the optimal hyperplane that separates data into different classes while maximizing the margin between them.
6. k-Nearest Neighbors (k-NN) 🏙️:
A simple yet effective algorithm for classification and regression tasks.
It predicts the target value based on the majority class or average of the k-nearest data points in the feature space.
7. Naive Bayes 📚:
A probabilistic algorithm commonly used for text classification tasks.
It applies Bayes' theorem, assuming that features are conditionally independent given the class.
It is very useful for textual features available in the modeling.
8. Gradient Boosting Machines (GBM) 🚀:
Another ensemble technique builds multiple weak learners (usually decision trees) sequentially, with each learner trying to correct the errors of its predecessor.
9. Principal Component Analysis (PCA) 📊:
Although not a predictive algorithm itself, it's a crucial technique for dimensionality reduction and data visualization.
It helps to find the principal components that capture the most important information in the data.
10. Neural Networks 🧠:
A class of algorithms inspired by the human brain. Deep Learning, a subfield of Neural Networks, has revolutionized various tasks like image recognition, natural language processing, etc.
This is where your deep learning followed by AI journey begins🚀
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Avi Kumar Talaviya
@avikumart_
Simplifying Data Science and Machine learning for beginners🤖 I share valuable threads & resources on DS/ML/DL @kaggle Master|Python|ML|Data|Analytics|Tech