Build Machine Learning models in python (or R) and some notes about machine learning algorithms

• Logistic Regression (Regularized Logistic Regression. Use different metrics to evaluate models, such as precision, recall, f1 score, ROC and AUC to evaluate model performance. Implemented Softmax Regression with Gradient Descent.)
• Support Vector Machine (SVM classifier and SVM regressor. Use linear kernel and Gaussian RBF kernel.)
• Decision Trees
• Ensemble learning (voting classifier, bagging, boosting, stacking)
• Dimensionality Reduction (PCA, LDA, tSNE and other popular dimensionality reduction methods. Visualizations of reduction result.)
• linear regression (in R. EDA, Residual analysis, model validation, error diagnosis and remediation)

make sure you have python3 installed on your machine. install pip3 for installing python module run this pip command to install all the required modules

pip3 install --upgrade jupyter matplotlib numpy pandas scipy scikit-learn

Also, make sure you have R kernel available to Jupyter notebook

In R console, run

install.packages(c('repr', 'IRdisplay', 'evaluate', 'crayon', 'pbdZMQ', 'devtools', 'uuid', 'digest'))
devtools::install_github('IRkernel/IRkernel')
IRkernel::installspec()