Machine Learning Portfolio
Summarized the main branches of ML algorithms and reflected on my personal interest in ML.
You can see the document here
Statistical functionalities in C++.
You can see the document here and the code here
Perform data cleaning and data exploration on medium-sized data sets, perform machine learning using linear models, and evaluate model performance.
You can see the Regression document here and the Regression code here
You can see the Classification document here and the Classification code here
Gain deeper understanding of machine learning algorithms by coding from scratch.
You can see the Logistic Regression code here and the Naive Bayes code here.
You can see the report here.
Gain experience with machine learning using similarity models kNN and Decision Trees, clustering methods kMeans and hierarchical clustering, and dimensionality reduction techniques LDA and PCA.
You can see the repo/code here.
You can see the Regression document here, Classification document here, Clustering document here, and Dimensionality document here.
Gain experience with machine learning using SVM linear, polynomial, radial kernels, and ensemble techniques.
You can see the Regression document here and the Classification document here.
You can see the narrative report here.
Gain experience with machine learning in sklearn on a small data set.
You can see the pdf here.
Gain experience with Keras, image classification, and deep learning model variations and embeddings.
You can see the pdf here.