By: Jyoti Upadhyay
– Supervised Learning – Unsupervised Learning – Reinforcement Learning
This model learns from the labeled data and makes a future prediction as output
This model uses unlabeled input data and allows the algorithm to act on that information without guidance.
A situation that occurs when a model learns the training set too well, taking up random fluctuations in training data as concepts.
– Regularization – Making a simple model – Cross-validation methods
– IsNull() & dropna() will help to find the columns/rows with missing data & drop them – Fillna() will replace the wrong values with a placeholder value
– Actual – Predicted
– Model Building – Model Testing – Applying the Model
Deep learning is a subset of machine learning that involves systems that think and learn like humans using artificial neural networks.
– Email Spam Detection – Healthcare Diagnosis – Sentiment Analysis – Fraud Detection
– Clustering – Association
– Top-down fashion. It will traverse nodes and trim subtrees starting at the root – Bottom-up fashion. It will begin at the leaf nodes
Logistic regression is a classification algorithm used to predict a binary outcome for a given set of independent variables.
– Multivariate normality – No auto-correlation – Homoscedasticity – Linear relationship – No or little multicollinearity