Machine Learning Basics

Machine learning: Structured Learning: 1)Classification 2)Regression Unstruture Learning: 1)Clustering 2)Association Active Learning Passive Learning Simple Linear Regression: Multiple Linear Regression: Ploynomial Regression: Ridge Regression/Tihknov regularisation Gradient Descent Batch Gradient descent Stochastic descent Logicl regression Binary classification multi class classification multi label classificaton Decision trees (non linear classification) Random Forest(Eager learners) clustering with K-means K-nearest neighbours(Lazy learners) Feature selection/Extraction One hot encoding,bag of words stemming and lemmatization(from nltk) Picture OCR-Optical Character Regognition POS=Point of Interest SIFT-Scale Invariant Feature Transform SURF-Speeded up Robust Feature Dimensationality Reduction with PCA (Principal Component Analysis) SVM-Support Vector Machine Artifical neaural network CNN/Convnet -convoluntial neural network Statistical Learning Probability distribution Predictor PAC method overfitting RFE(Recursive feature elimination) outlier removal Explanatory analysis Gradient descent inductive bias Finite hypothesis class K-means elbow method Distortion support vector meachine