Regression covering data science and machine learning in 100 days:

Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Clustering: K-Means, Hierarchical Clustering

Association Rule Learning: Apriori, Eclat

Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Natural Language Processing: Bag-of-words model and algorithms for NLP

Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Dimensionality Reduction: PCA, LDA, Kernel PCA

I have created a portfolio on Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoostSelenium, Beautiful Soup, Flask, Pandas, NumPy, Scikit Learn and many more. It is among my goals to prove my skills in the field of machine learning and artificial intelligence. You can access the source files