Murphy’s Law Probably one of the most famous of all laws, mostly because it is not only applicable to Software Development. If something can go wrong, it will. First derivation: If it works, you probably didn’t write it. Second derivation: Cursing is the only language all programmers speak fluently. Conclusion: A computer will do what you write, not what you want. Defensive programming, version control, doom scenario’s (for those damned zombie-server-attacks), TDD, MDD, etc.

Source https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/ It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation $$Y= a *X + b$$ The best way to understand linear regression is to relive this experience of childhood.

Don’t get confused by its name! It is a classification not a regression algorithm. It is used to estimate discrete values (binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s). In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function. Hence, it is also known as logit regression. Since, it predicts the probability, its output values lies between 0 and 1 (as expected).

Broadly, there are three types of Machine Learning Algorithms.. 1. Supervised Learning How it works: This algorithm consist of a target or outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.

Source cran Chapter 1 Introduction Prediction Versus Interpretation, Key Ingredients of Predictive Models; Terminology; Example Data Sets and Typical Data Scenarios; Overview; Notation (15 pages, 3 figures) Part I: General Strategies Chapter 2 A Short Tour of the Predictive Modeling Process Case Study: Predicting Fuel Economy; Themes; Summary (8 pages, 6 figures, R packages used) Chapter 3 Data Pre-Processing Case Study: Cell Segmentation in High-Content Screening; Data Transformations for Individual Predictors; Data Transformations for Multiple Predictors; Dealing with Missing Values; Removing Variables; Adding Variables; Binning Variables; Computing; Exercises (32 pages, 11 figures, R packages used)