Machine Learning A-Z course using Python Topics covered so far:
DATA PREPROCESSING
REGRESSION: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest Regression
CLASSIFICATION: Logistic Regression, K-Nearest Neighbor, Support Vector Machines, Kernel SVM, Naive Bayes, Decision Tree, Random Forest
CLUSTERING: K-Means, Hierarchical
ASSOCIATION RULE LEARNING: Apriori, Eclat
REINFORCEMENT LEARNING: Upper Confidence Bound, Thompson Sampling
NATURAL LANGUAGE PROCESSING: Bag of words model
DEEP LEARNING Artificial Neural Networks, Convolutional Neural Networks
DIMENSIONALITY REDUCTION: Principal Component Analysis Linear Discrimant Analysis Kernel PCA