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