Table of contents: Introduction Properties of KNN Steps of KNN algorithm Distance metrics in KNN KNN hyperparameter K How to select the value of K Data Preparation for KNN KNN for Classification KNN for Regression Pros of using KNN Cons of using KNN Applications of KNN References Introduction: K-Nearest Neighbor…

Table of contents: Introduction Assumptions of Naive Bayes Bayes Theorem Example of Naive Bayes Problem with Naive Bayes Laplace or Additive Smoothing Bias Variance Tradeoff in Naive Bayes using hyper-parameter α Example of Laplace smoothing Applications of Naive Bayes Pros of using Naive Bayes Cons of using Naive Bayes How…

Table of contents: Overfitting Overfitting Examples How to overcome Overfitting? What parameters (θ’s) to penalize? Regularization Types of Regularization Regularization Parameter λ Why L1 creates Sparsity? References Overfitting: Sometimes the machine learning model performs well with the training data but does not perform well with the test data. It means…

Table of contents: Generate data Split data into the train (75%) and test (25%) Standardize the data Initialize the weight_vector and intercept Compute Sigmoid Compute Log Loss Calculate Gradient w.r.t. ‘w’ Calculate Gradient w.r.t. ‘b’ Train the custom model Compare custom model with sklearn SGDClassifier model End Notes References In…

Table of contents: Introduction Use of Linear Regression for classification issue Why Logistic Regression has term Regression in it The cost function for Logistic Regression Log Loss with example Log Loss cost function for Logistic Regression End Notes References Introduction: Logistic Regression is one of the most popular statistical models…

Table of contents: Introduction Assumption in Linear Regression Hypothesis Cost Function Gradient descent Role of derivative term and learning rate Python example of Linear Regression using Boston House pricing dataset References In my previous article, I explained Linear Regression concepts. Please go through it if you want to know the…

Table of contents: Introduction Steps in Linear Regression Fitting a line to data using the least square method Calculate R² Calculate p-value for R² End Notes References Introduction: Linear Regression is a Supervised Machine learning model (where training data with the label is provided) which is used for Regression tasks…

Table of contents: Introduction Margin Maximizing Hyperplane Why Margin Maximizing Hyperplanes Mathematics behind the scene Hard Margin SVM Soft Margin SVM and Hinge Loss Hyperparameter- C (or slack variable) Kernel Function RBF Kernel and associated hyperparameter- γ (or Gamma) Python code example with the linear kernel References Introduction: SVM is…