Classic Machine Learning Algorithms
Nitin Kumar
Python Developer | SDET | DevOps Enthusiast | Data Science Practitioner | Open to Exciting New Roles
Most of the Machine Learning Lovers don't know which algorithms we have to study and how to implement it in our project, Today i have made a list of some machine learning algorithms with small description about them. So a pythonista can easily approach and understand what is the goal.
Join me in an adventurous joruney of Machine learning( From linear regression to neural networks), discover new Features and learn new insights.
Build your own projects and predictions.
For detailed information please watch YouTube videos on the given algorithms.
Classic Machine Learning Algorithms:
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1. Linear Regression:- ????Simple & effective for modelling linear relationships
Widely used for predicting continuous values.
Interpretable and provides insights info feature importance.
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2. Decision Tree:-?????Versatile and easy to understand.
Can handle both categorical and numerical data.
Useful for capturing complex interactions and making
interpretable decisions.
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3. Random Forest:- ???Ensemble of decision trees for improved performance
????????????????????????????????????Reduces overfitting and increases generalization.
???????????????????????????????????Suitable for both classification and regression tasks.
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4. K-Nearest Neighbors:-??????Non-parametric algorithms for classification and regression.
??????????????????????????????????Makes predictions based on k nearest data points.
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?????????????????????????????????Simple yet powerful in handling complex patterns in data.
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5. Na?ve Bayes:- ??????Probabilistic algorithm based on Baye’s Theorem
?????????????????????????????????Suitable for text classification and spam filtering.
????????????????????????????????Efficient and performs well with high-dimensional data.
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6. Support Vector Machine(SVM):- Effective for both linear and non-linear classification.
????????Finds optimal decision boundaries in high- dimensional ?.
????????Handle large features sets and provides robust predictions.
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7. Neural Networks :- ???Deep learning models inspired by the human brains.
????????????????????????????????????????Capable of learning complex patterns and representations
???????????????????????????????????????Used for tasks like image recognition, natural language?processing and more.
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8. Gradient Boosting :- ???Ensemble technique for combining weak learners.
??????????????????????????Builds models sequentially to correct errors of previous models.
?????????????????????????Provides high predictive accuracy and handles complex datasets.
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9. Principal Component Analysis:- ???Dimensionally reduction technique.
????????????????????????????????????Captures important features and reduces data complexity.
????????????????????????????????????Useful for visualization and preprocessing large datasets.