Following are the implementations of supervised machine learning algorithms from scratch without using any Machine learning libraries

Ridge Polynomail Regression with Learning Curve Analysis GitHub Link

Implementation of Ridge Polynomial Regression with Learning Curve Analysis from scratch using python. The implementation includes the implementation of the MSE(Mean Square Error) Cost Function, Numerical Optimization Algorithm Gradient descent, Bias-variance learning curve analysis, and model selection

One-vs-all Classification Using Logistic Regression GitHub Link

Implementation of One-vs-all Classification Using Logistic Regression from scratch in python.

Support vector machine using LinearKernel and gaussianKernel GitHub Link

Implementation Support vector machine using LinearKernel and Gaussian kernel from scratch in python. The implementation includes the implementation of linear and Non-linear hypothesis, Regularized Hinge loss function, Numerical Optimization Algorithm ]and visualization of DecisionBoundary with margin

Logistic Regression on Linear Data GitHub Link

Implementation Logistic Regression on Linear Data from scratch using python. The implementation includes the implementation of logistic linear hypothesis, the cross-entropy Cost Function, Numerical Optimization Algorithm Gradient descent, and visualization of DecisionBoundary

Logistic Regression on Nonlinear GitHub Link

Implementation Logistic Regression on Non-Linear Data from scratch using python. The implementation includes the implementation of logistic Non-linear hypothesis, Regularized cross-entropy Cost Function, Numerical Optimization Algorithm Gradient descent, and visualization of DecisionBoundary

Univariate Linear Regression GitHub Link

Implementation of Univariate Linear Regression from scratch using python. The implementation includes the implementation of the MSE(Mean Square Error) Cost Function and Numerical Optimization Algorithm Gradient descent and feature Normalization

Multivariate Linear Regression GitHub Link

Implementation of multivariate linear Regression from scratch using python. The implementation includes the implementation of the MSE(Mean Square Error) Cost Function and Numerical Optimization Algorithm Gradient descent , feature Normalization and Selecting of learning rates

Locally Weighted RegressionGitHub Link

Implementation of Locally Weighted Regression from scratch using python. The implementation includes the implementation of the MSE(Mean Square Error) Cost FunctionWith Weighted Kernel and Numerical Optimization Algorithm Gradient descent

Time Series Analysis GitHub Link

Time series analysis in python using statsmodels and Prophet libraries

Ensemble Learning Bagging(RandomForestRegressor) using SKlearn GitHub Link

Ensemble Learning Bagging(RandomForestRegressor) using SKlearn

Ensemble Learning Boosting(XGboost) using SKlearn GitHub Link

Ensemble Learning Boosting(XGboost) using SKlearn

Deep Neural Network (DNN) GitHub Link

Implementation of Neural Network with a Forward Propagation for the cross-entropy cost function and Backward Propagation for Gradient descent from scratch in Python

Courses & Certificates

• Introduction to Database Systems (2019) edX-uc berkeley university-Joe Hellerstein

• Introduction to Computer Science and Programming Using Python(2017) edX-MIT-Eric Grimson

• CS 61B: Data Structures(2018) OCW-uc berkeley university-Jonathan Shewchuk

• Mathematics for Computer Science/Discrete mathematics OCW- MIT-Prof. Tom Leighton

• Linear Algebra(2019) OCW-MIT-Prof. Gilbert Strang

• Single Variable Calculus(2020) OCW-MIT-Prof. David Jerison

• Introduction to Probability (2020)OCW-MIT-Prof. John Tsitsiklis

• Introduction to Compiler Construction(2019)OCW-The Paul G. Allen Center for Computer Science & Engineering-Hal Perkins

• Artificial Intelligence(2018) edX-uc berkeley university-Dan Klein

• Machine Learning-Stanford(2020) Coursera - Stanford - Andrew Ng

• CS229 - Machine Learning(2020)Stanford - Andrew Ng

• Introduction to Algorithms(2018)OCW-MIT-Srini Devadas & Erik Demaine

Conclusion

The following link to my Programming showcase repository on Github includes implementation of every algorithm discussed here