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Title: Top 10 Machine Learning Algorithms in 2026 - Analytics Vidhya

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Implement Linear Regression from Scratchhttps://www.analyticsvidhya.com/blog/2021/05/all-you-need-to-know-about-your-first-machine-learning-model-linear-regression/
Train Linear Regression in Pythonhttps://www.analyticsvidhya.com/blog/2021/05/multiple-linear-regression-using-python-and-scikit-learn/
Implementing Linear Regression in Rhttps://www.analyticsvidhya.com/blog/2020/12/predicting-using-linear-regression-in-r/
Diagnosing Residual Plots in Linear Regression Modelshttps://www.analyticsvidhya.com/blog/2013/12/residual-plots-regression-model/
Generalized Linear Modelshttps://www.analyticsvidhya.com/blog/2021/10/everything-you-need-to-know-about-linear-regression/
Introduction to Logistic Regressionhttps://www.analyticsvidhya.com/blog/2017/08/skilltest-logistic-regression/
Odds Ratiohttps://www.analyticsvidhya.com/blog/2021/08/conceptual-understanding-of-logistic-regression-for-data-science-beginners/
Implementing Logistic Regression from Scratchhttps://www.analyticsvidhya.com/blog/2020/12/beginners-take-how-logistic-regression-is-related-to-linear-regression/
Introduction to Scikit-learn in Pythonhttps://www.analyticsvidhya.com/blog/2015/01/scikit-learn-python-machine-learning-tool/
Train Logistic Regression in pythonhttps://www.analyticsvidhya.com/blog/2022/01/logistic-regression-an-introductory-note/
Multiclass using Logistic Regressionhttps://www.analyticsvidhya.com/blog/2021/05/20-questions-to-test-your-skills-on-logistic-regression/
How to use Multinomial and Ordinal Logistic Regression in R ?https://www.analyticsvidhya.com/blog/2016/02/multinomial-ordinal-logistic-regression/
Challenges with Linear Regressionhttps://www.analyticsvidhya.com/blog/2017/07/30-questions-to-test-a-data-scientist-on-linear-regression/
Introduction to Regularisationhttps://www.analyticsvidhya.com/blog/2016/01/ridge-lasso-regression-python-complete-tutorial/
Implementing Regularisationhttps://www.analyticsvidhya.com/blog/2021/11/study-of-regularization-techniques-of-linear-model-and-its-roles/
Ridge Regressionhttps://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/
Lasso Regressionhttps://www.analyticsvidhya.com/blog/2021/09/lasso-and-ridge-regularization-a-rescuer-from-overfitting/
Introduction to K Nearest Neighbourshttps://www.analyticsvidhya.com/blog/2017/09/30-questions-test-k-nearest-neighbors-algorithm/
Determining the Right Value of K in KNNhttps://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/
Implement KNN from Scratchhttps://www.analyticsvidhya.com/blog/2021/04/simple-understanding-and-implementation-of-knn-algorithm/
Implement KNN in Pythonhttps://www.analyticsvidhya.com/blog/2018/08/k-nearest-neighbor-introduction-regression-python/
Bias Variance Tradeoffhttps://www.analyticsvidhya.com/blog/2020/08/bias-and-variance-tradeoff-machine-learning/
Introduction to Overfitting and Underfittinghttps://www.analyticsvidhya.com/blog/2020/02/underfitting-overfitting-best-fitting-machine-learning/
Visualizing Overfitting and Underfittinghttps://www.analyticsvidhya.com/blog/2015/02/avoid-over-fitting-regularization/
Selecting the Right Modelhttps://www.analyticsvidhya.com/blog/2021/07/how-to-choose-an-appropriate-ml-algorithm-data-science-projects/
What is Validation?https://www.analyticsvidhya.com/blog/2018/05/improve-model-performance-cross-validation-in-python-r/
Hold-Out Validationhttps://www.analyticsvidhya.com/blog/2022/02/k-fold-cross-validation-technique-and-its-essentials/
Understanding K Fold Cross Validationhttps://www.analyticsvidhya.com/blog/2021/03/introduction-to-k-fold-cross-validation-in-r/
Introduction to Feature Selectionhttps://www.analyticsvidhya.com/blog/2020/10/feature-selection-techniques-in-machine-learning/
Feature Selection Algorithmshttps://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/
Missing Value Ratiohttps://www.analyticsvidhya.com/blog/2021/04/beginners-guide-to-missing-value-ratio-and-its-implementation/
Low Variance Filterhttps://www.analyticsvidhya.com/blog/2021/04/beginners-guide-to-low-variance-filter-and-its-implementation/
High Correlation Filterhttps://www.analyticsvidhya.com/blog/2018/08/dimensionality-reduction-techniques-python/
Backward Feature Eliminationhttps://www.analyticsvidhya.com/blog/2020/10/a-comprehensive-guide-to-feature-selection-using-wrapper-methods-in-python/
Forward Feature Selectionhttps://www.analyticsvidhya.com/blog/2021/04/discovering-the-shades-of-feature-selection-methods/
Implement Feature Selection in Pythonhttps://www.analyticsvidhya.com/blog/2021/04/forward-feature-selection-and-its-implementation/
Implement Feature Selection in Rhttps://www.analyticsvidhya.com/blog/2016/03/select-important-variables-boruta-package/
Introduction to Decision Treehttps://www.analyticsvidhya.com/blog/2020/10/all-about-decision-tree-from-scratch-with-python-implementation/
Purity in Decision Treehttps://www.analyticsvidhya.com/blog/2021/03/how-to-select-best-split-in-decision-trees-gini-impurity/
Terminologies Related to Decision Treehttps://www.analyticsvidhya.com/blog/2022/04/complete-flow-of-decision-tree-algorithm/
How to Select Best Split Point in Decision Tree?https://www.analyticsvidhya.com/blog/2020/06/4-ways-split-decision-tree/
Chi-Squareshttps://www.analyticsvidhya.com/blog/2021/03/how-to-select-best-split-in-decision-trees-using-chi-square/
Information Gainhttps://www.analyticsvidhya.com/blog/2021/05/25-questions-to-test-your-skills-on-decision-trees/
Reduction in Variancehttps://www.analyticsvidhya.com/blog/2015/07/dimension-reduction-methods/
Optimizing Performance of Decision Treehttps://www.analyticsvidhya.com/blog/2021/08/decision-tree-algorithm/
Train Decision Tree using Scikit Learnhttps://www.analyticsvidhya.com/blog/2021/04/beginners-guide-to-decision-tree-classification-using-python/
Pruning of Decision Treeshttps://www.analyticsvidhya.com/blog/2020/10/cost-complexity-pruning-decision-trees/
Introduction to Feature Engineeringhttps://www.analyticsvidhya.com/blog/2021/03/step-by-step-process-of-feature-engineering-for-machine-learning-algorithms-in-data-science/
Feature Transformationhttps://www.analyticsvidhya.com/blog/2020/07/types-of-feature-transformation-and-scaling/
Feature Scalinghttps://www.analyticsvidhya.com/blog/2020/12/feature-engineering-feature-improvements-scaling/
Feature Engineeringhttps://www.analyticsvidhya.com/blog/2018/08/guide-automated-feature-engineering-featuretools-python/
Frequency Encodinghttps://www.analyticsvidhya.com/blog/2021/05/complete-guide-on-encode-numerical-features-in-machine-learning/
Automated Feature Engineering: Feature Toolshttps://www.analyticsvidhya.com/blog/2020/06/feature-engineering-guide-data-science-hackathons/
Introduction to Naive Bayeshttps://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/
Conditional Probability and Bayes Theoremhttps://www.analyticsvidhya.com/blog/2021/09/naive-bayes-algorithm-a-complete-guide-for-data-science-enthusiasts/
Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!https://www.analyticsvidhya.com/blog/2019/07/introduction-online-rating-systems-bayesian-adjusted-rating/
Working of Naive Bayeshttps://www.analyticsvidhya.com/blog/2022/03/building-naive-bayes-classifier-from-scratch-to-perform-sentiment-analysis/
Math behind Naive Bayeshttps://www.analyticsvidhya.com/blog/2021/01/a-guide-to-the-naive-bayes-algorithm/
Types of Naive Bayeshttps://www.analyticsvidhya.com/blog/2022/10/frequently-asked-interview-questions-on-naive-bayes-classifier/
Implementation of Naive Bayeshttps://www.analyticsvidhya.com/blog/2021/03/introduction-to-naive-bayes-algorithm/
Understanding how to solve Multiclass and Multilabled Classification Problemhttps://www.analyticsvidhya.com/blog/2021/07/demystifying-the-difference-between-multi-class-and-multi-label-classification-problem-statements-in-deep-learning/
Evaluation Metrics: Multi Class Classificationhttps://www.analyticsvidhya.com/blog/2021/06/confusion-matrix-for-multi-class-classification/
Introduction to Ensemble Techniqueshttps://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/
Basic Ensemble Techniqueshttps://www.analyticsvidhya.com/blog/2021/08/ensemble-stacking-for-machine-learning-and-deep-learning/
Implementing Basic Ensemble Techniqueshttps://www.analyticsvidhya.com/blog/2021/01/exploring-ensemble-learning-in-machine-learning-world/
Finding Optimal Weights of Ensemble Learner using Neural Networkhttps://www.analyticsvidhya.com/blog/2015/08/optimal-weights-ensemble-learner-neural-network/
Why Ensemble Models Work well?https://www.analyticsvidhya.com/blog/2021/10/ensemble-modeling-for-neural-networks-using-large-datasets-simplified/
Introduction to Stackinghttps://www.analyticsvidhya.com/blog/2020/10/how-to-use-stacking-to-choose-the-best-possible-algorithm/
Implementing Stackinghttps://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/
Variants of Stackinghttps://www.analyticsvidhya.com/blog/2020/12/improve-predictive-model-score-stacking-regressor/
Implementing Variants of Stackinghttps://www.analyticsvidhya.com/blog/2021/03/advanced-ensemble-learning-technique-stacking-and-its-variants/
Introduction to Blendinghttps://www.analyticsvidhya.com/blog/2021/03/basic-ensemble-technique-in-machine-learning/
Bootstrap Samplinghttps://www.analyticsvidhya.com/blog/2020/02/what-is-bootstrap-sampling-in-statistics-and-machine-learning/
Introduction to Random Samplinghttps://www.analyticsvidhya.com/blog/2019/09/data-scientists-guide-8-types-of-sampling-techniques/
Hyper-parameters of Random Foresthttps://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/
Implementing Random Foresthttps://www.analyticsvidhya.com/blog/2018/10/interpret-random-forest-model-machine-learning-programmers/
Out-of-Bag (OOB) Score in the Random Foresthttps://www.analyticsvidhya.com/blog/2020/12/out-of-bag-oob-score-in-the-random-forest-algorithm/
IPL Team Win Prediction Project Using Machine Learninghttps://www.analyticsvidhya.com/blog/2022/05/ipl-team-win-prediction-project-using-machine-learning/
Introduction to Boostinghttps://www.analyticsvidhya.com/blog/2021/09/adaboost-algorithm-a-complete-guide-for-beginners/
Gradient Boosting Algorithmhttps://www.analyticsvidhya.com/blog/2022/01/boosting-in-machine-learning-definition-functions-types-and-features/
Math behind GBMhttps://www.analyticsvidhya.com/blog/2020/02/4-boosting-algorithms-machine-learning/
Implementing GBM in pythonhttps://www.analyticsvidhya.com/blog/2016/02/complete-guide-parameter-tuning-gradient-boosting-gbm-python/
Regularized Greedy Forestshttps://www.analyticsvidhya.com/blog/2021/04/distinguish-between-tree-based-machine-learning-algorithms/
Extreme Gradient Boostinghttps://www.analyticsvidhya.com/blog/2018/09/an-end-to-end-guide-to-understand-the-math-behind-xgboost/
Implementing XGBM in pythonhttps://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
Tuning Hyperparameters of XGBoost in Pythonhttps://www.analyticsvidhya.com/blog/2021/06/5-hyperparameter-optimization-techniques-you-must-know-for-data-science-hackathons/
Implement XGBM in R/H2Ohttps://www.analyticsvidhya.com/blog/2016/01/xgboost-algorithm-easy-steps/
Adaptive Boostinghttps://www.analyticsvidhya.com/blog/2015/11/quick-introduction-boosting-algorithms-machine-learning/
Implementing Adaptive Boosinghttps://www.analyticsvidhya.com/blog/2021/03/introduction-to-adaboost-algorithm-with-python-implementation/
LightGBMhttps://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/
Implementing LightGBM in Pythonhttps://www.analyticsvidhya.com/blog/2021/08/complete-guide-on-how-to-use-lightgbm-in-python/
Catboosthttps://www.analyticsvidhya.com/blog/2017/08/catboost-automated-categorical-data/
Implementing Catboost in Pythonhttps://www.analyticsvidhya.com/blog/2021/04/how-to-use-catboost-for-mental-fatigue-score-prediction/
Different Hyperparameter Tuning methodshttps://www.analyticsvidhya.com/blog/2021/04/evaluating-machine-learning-models-hyperparameter-tuning/
Implementing Different Hyperparameter Tuning methodshttps://www.analyticsvidhya.com/blog/2021/10/an-effective-approach-to-hyper-parameter-tuning-a-beginners-guide/
GridsearchCVhttps://www.analyticsvidhya.com/blog/2021/06/tune-hyperparameters-with-gridsearchcv/
RandomizedsearchCVhttps://www.analyticsvidhya.com/blog/2022/11/hyperparameter-tuning-using-randomized-search/
Bayesian Optimization for Hyperparameter Tuninghttps://www.analyticsvidhya.com/blog/2020/09/alternative-hyperparameter-optimization-technique-you-need-to-know-hyperopt/
Hyperopthttps://www.analyticsvidhya.com/blog/2021/05/bayesian-optimization-bayes_opt-or-hyperopt/
Understanding SVM Algorithmhttps://www.analyticsvidhya.com/blog/2020/03/support-vector-regression-tutorial-for-machine-learning/
SVM Kernels In-depth Intuition and Practical Implementationhttps://www.analyticsvidhya.com/blog/2021/10/support-vector-machinessvm-a-complete-guide-for-beginners/
SVM Kernel Trickshttps://www.analyticsvidhya.com/blog/2021/06/support-vector-machine-better-understanding/
Kernels and Hyperparameters in SVMhttps://www.analyticsvidhya.com/blog/2021/05/support-vector-machines/
Implementing SVM from Scratch in Python and Rhttps://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
Introduction to Principal Component Analysishttps://www.analyticsvidhya.com/blog/2021/02/diminishing-the-dimensions-with-pca/
Steps to Perform Principal Compound Analysishttps://www.analyticsvidhya.com/blog/2020/12/an-end-to-end-comprehensive-guide-for-pca/
Computation of Covariance Matrixhttps://www.analyticsvidhya.com/blog/2021/05/simplifying-maths-behind-pca/
Finding Eigenvectors and Eigenvalueshttps://www.analyticsvidhya.com/blog/2021/09/pca-and-its-underlying-mathematical-principles/
Implementing PCA in pythonhttps://www.analyticsvidhya.com/blog/2016/03/pca-practical-guide-principal-component-analysis-python/
Visualizing PCAhttps://www.analyticsvidhya.com/blog/2021/02/visualizing-pca-in-r-programming-with-factoshiny/
A Brief Introduction to Linear Discriminant Analysishttps://www.analyticsvidhya.com/blog/2021/08/a-brief-introduction-to-linear-discriminant-analysis/
Introduction to Factor Analysishttps://www.analyticsvidhya.com/blog/2020/10/dimensionality-reduction-using-factor-analysis-in-python/
Introduction to Clusteringhttps://www.analyticsvidhya.com/blog/2020/11/introduction-to-clustering-in-python-for-beginners-in-data-science/
Applications of Clusteringhttps://www.analyticsvidhya.com/blog/2022/11/hierarchical-clustering-in-machine-learning/
Evaluation Metrics for Clusteringhttps://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/
Understanding K-Meanshttps://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/
Implementation of K-Means in Pythonhttps://www.analyticsvidhya.com/blog/2021/04/k-means-clustering-simplified-in-python/
Implementation of K-Means in Rhttps://www.analyticsvidhya.com/blog/2021/04/beginners-guide-to-clustering-in-r-program/
Choosing Right Value for Khttps://www.analyticsvidhya.com/blog/2021/01/in-depth-intuition-of-k-means-clustering-algorithm-in-machine-learning/
Profiling Market Segments using K-Means Clusteringhttps://www.analyticsvidhya.com/blog/2020/10/a-definitive-guide-for-predicting-customer-lifetime-value-clv/
Hierarchical Clusteringhttps://www.analyticsvidhya.com/blog/2021/06/single-link-hierarchical-clustering-clearly-explained/
Implementation of Hierarchial Clusteringhttps://www.analyticsvidhya.com/blog/2019/05/beginners-guide-hierarchical-clustering/
DBSCANhttps://www.analyticsvidhya.com/blog/2020/09/how-dbscan-clustering-works/
Defining Similarity between clustershttps://www.analyticsvidhya.com/blog/2017/02/test-data-scientist-clustering/
Build Better and Accurate Clusters with Gaussian Mixture Modelshttps://www.analyticsvidhya.com/blog/2019/10/gaussian-mixture-models-clustering/
Understand Basics of Recommendation Engine with Case Studyhttps://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-recommendation-engine-python/
8 Ways to Improve Accuracy of Machine Learning Modelshttps://www.analyticsvidhya.com/blog/2015/12/improve-machine-learning-results/
Introduction to Daskhttps://www.analyticsvidhya.com/blog/2018/08/dask-big-datasets-machine_learning-python/
Working with CuMLhttps://www.analyticsvidhya.com/blog/2022/01/cuml-blazing-fast-machine-learning-model-training-with-nvidias-rapids/
Introduction to Machine Learning Interpretabilityhttps://www.analyticsvidhya.com/blog/2021/06/beginners-guide-to-machine-learning-explainability/
Framework and Interpretable Modelshttps://www.analyticsvidhya.com/blog/2017/06/building-trust-in-machine-learning-models/
model Agnostic Methods for Interpretabilityhttps://www.analyticsvidhya.com/blog/2021/01/explain-how-your-model-works-using-explainable-ai/
Implementing Interpretable Modelhttps://www.analyticsvidhya.com/blog/2019/08/decoding-black-box-step-by-step-guide-interpretable-machine-learning-models-python/
Understanding SHAPhttps://www.analyticsvidhya.com/blog/2019/11/shapley-value-machine-learning-interpretability-game-theory/
Out-of-Core MLhttps://www.analyticsvidhya.com/blog/2022/09/out-of-core-ml-an-efficient-technique-to-handle-large-data/
Introduction to Interpretable Machine Learning Modelshttps://www.analyticsvidhya.com/blog/2020/03/6-python-libraries-interpret-machine-learning-models/
Model Agnostic Methods for Interpretabilityhttps://www.analyticsvidhya.com/blog/2021/01/ml-interpretability-using-lime-in-r/
Game Theory & Shapley Valueshttps://www.analyticsvidhya.com/blog/2019/12/game-theory-101-decision-making-normal-form-games/
Introduction to AutoMLhttps://www.analyticsvidhya.com/blog/2021/04/does-the-popularity-of-automl-means-the-end-of-data-science-jobs/
Implementation of MLBoxhttps://www.analyticsvidhya.com/blog/2017/07/mlbox-library-automated-machine-learning/
Introduction to PyCarethttps://www.analyticsvidhya.com/blog/2021/07/anomaly-detection-using-isolation-forest-a-complete-guide/
TPOThttps://www.analyticsvidhya.com/blog/2021/05/automate-machine-learning-using-tpot - explore-thousands-of-possible-pipelines-and-find-the-best/
Auto-Sklearnhttps://www.analyticsvidhya.com/blog/2021/10/beginners-guide-to-automl-with-an-easy-autogluon-example/
EvalMLhttps://www.analyticsvidhya.com/blog/2021/04/breast-cancer-prediction-using-evalml/
Pickle and Joblibhttps://www.analyticsvidhya.com/blog/2021/08/quick-hacks-to-save-machine-learning-model-using-pickle-and-joblib/
Introduction to Model Deploymenthttps://www.analyticsvidhya.com/blog/2020/09/integrating-machine-learning-into-web-applications-with-flask/
Deploying Machine Learning Model using Streamlithttps://www.analyticsvidhya.com/blog/2021/06/build-web-app-instantly-for-machine-learning-using-streamlit/
Deploying ML Models in Dockerhttps://www.analyticsvidhya.com/blog/2021/06/a-hands-on-guide-to-containerized-your-machine-learning-workflow-with-docker/
Deploy Using Streamlithttps://www.analyticsvidhya.com/blog/2021/04/developing-data-web-streamlit-app/
Deploy on Herokuhttps://www.analyticsvidhya.com/blog/2021/06/deploy-your-ml-dl-streamlit-application-on-heroku/
Deploy Using Netlifyhttps://www.analyticsvidhya.com/blog/2021/04/easily-deploy-your-machine-learning-model-into-a-web-app-netlify/
Introduction to Amazon Sagemakerhttps://www.analyticsvidhya.com/blog/2022/02/building-ml-model-in-aws-sagemaker/
Setting up Amazon SageMakerhttps://www.analyticsvidhya.com/blog/2022/01/huggingface-transformer-model-using-amazon-sagemaker/
Using SageMaker Endpoint to Generate Inferencehttps://www.analyticsvidhya.com/blog/2020/11/deployment-of-ml-models-in-cloud-aws-sagemaker in-built-algorithms/
Deploy on Microsoft Azure Cloudhttps://www.analyticsvidhya.com/blog/2020/10/how-to-deploy-machine-learning-models-in-azure-cloud-with-the-help-of-python-and-flask/
Introduction to Flask for Modelhttps://www.analyticsvidhya.com/blog/2021/10/easy-introduction-to-flask-framework-for-beginners/
Deploying ML model using Flaskhttps://www.analyticsvidhya.com/blog/2020/04/how-to-deploy-machine-learning-model-flask/
Model Deployment in Androidhttps://www.analyticsvidhya.com/blog/2015/12/18-mobile-apps-data-scientist-data-analysts/
Model Deployment in Iphonehttps://www.analyticsvidhya.com/blog/2019/11/introduction-apple-core-ml-3-deep-learning-models-iphone/
Homehttps://www.analyticsvidhya.com/blog/
Machine Learning https://www.analyticsvidhya.com/blog/category/machine-learning/
https://www.analyticsvidhya.com/blog/author/sunil-ray/
Sunil Ray https://www.analyticsvidhya.com/blog/author/sunil-ray/
Types of Machine Learning Algorithmshttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-types-of-machine-learning-algorithms
Supervised Learninghttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-supervised-learning
Unsupervised Learninghttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-unsupervised-learning
Reinforcement Learninghttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-reinforcement-learning
List of Top 10 Common Machine Learning Algorithmshttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#List_of_Common_Machine_Learning_Algorithms
1. Linear Regressionhttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-1-linear-regression
2. Logistic Regressionhttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-2-logistic-regression
3. Decision Treehttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-3-decision-tree
4. SVM (Support Vector Machine)https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-4-svm-support-vector-machine
5. Naive Bayeshttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-5-naive-bayes
6. kNN (k- Nearest Neighbors)https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-6-knn-k-nearest-neighbors
7. K-Meanshttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-7-k-means
8. Random Foresthttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-8-random-forest
9. Dimensionality Reduction Algorithmshttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-9-dimensionality-reduction-algorithms
10. Gradient Boosting Algorithmshttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-10-gradient-boosting-algorithms
Practice Problemshttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#Practice_Problems
Conclusionhttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#Conclusion
Frequently Asked Questionshttps://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/#h-frequently-asked-questions
Everything You Need to Know about Machine Learninghttps://www.analyticsvidhya.com/blog/2021/03/everything-you-need-to-know-about-machine-learning/
Regression Modelhttps://www.analyticsvidhya.com/blog/2022/01/different-types-of-regression-models/
regularization techniqueshttps://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/
Simplified Version of Decision Tree Algorithmshttps://www.analyticsvidhya.com/blog/2015/01/decision-tree-simplified/
SVM algorithmhttps://www.analyticsvidhya.com/blog/2014/10/support-vector-machine-simplified/
Everything You Need to Know about Machine Learninghttps://www.analyticsvidhya.com/blog/2021/03/everything-you-need-to-know-about-machine-learning/
ensemble learninghttps://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/
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