Example Notebooks: https://github.com/thabresh-s/Data-Science/tree/main/Statistics/Notebooks
-
Probability - Probability is the study of the likelihood of events occurring. It is used in machine learning to quantify the uncertainty of predictions. Some common probability concepts include conditional probability, Bayes' theorem, and the probability density function.
-
Descriptive Statistics - Descriptive statistics is the branch of statistics that deals with summarizing and describing data. It is used in machine learning to explore the data and identify patterns and relationships. Some common descriptive statistics include measures of central tendency (mean, median, mode) and measures of variability (variance, standard deviation, range).
-
Inferential Statistics - Inferential statistics is the branch of statistics that deals with making predictions and drawing conclusions from data. It is used in machine learning to test hypotheses and make predictions based on the data. Some common inferential statistics techniques include hypothesis testing, confidence intervals, and regression analysis.
-
Regression Analysis - Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It is commonly used in machine learning to make predictions based on historical data. Some common regression techniques include linear regression, logistic regression, and polynomial regression.
-
Hypothesis Testing - Hypothesis testing is a statistical technique used to test a hypothesis or claim about a population using sample data. It is used in machine learning to test the significance of a model or determine if two populations are significantly different. Some common hypothesis tests include t-tests, ANOVA, and chi-squared tests.
-
Feature Selection - Feature selection is the process of selecting a subset of relevant features (variables) from a larger set of features. It is used in machine learning to reduce the complexity of the model and improve its accuracy. Some common feature selection techniques include correlation analysis, stepwise regression, and principal component analysis.
-
Model Evaluation - Model evaluation is the process of determining how well a machine learning model performs on new, unseen data. It is used in machine learning to assess the accuracy and performance of the model. Some common model evaluation techniques include cross-validation, confusion matrix, and ROC curve analysis.
It is important to have a good understanding of these concepts and know how to apply them to real-world problems. Be prepared to explain these concepts and techniques, and provide examples of how you have used them in your previous work. Good luck with your interview!