Statistics
Statistics is the collection of data, tabulation, and interpretation of numerical data, and it is applied mathematics concerned with data collection analysis, interpretation, and presentation.
- Mean, Standard Deviation, and Variance
- Sample Error and True Error
- Bias Vs Variance and Its Trade-Off
- Hypothesis Testing
- Confidence Intervals
- Correlation and Covariance
- Correlation Coefficient
- Covariance Matrix
- Normal Probability Plot
- Q-Q Plot
- Residuals Leverage Plot
- Robust Correlations
- Hypothesis Testing
- Null and Alternative Hypothesis
- Type 1 and Type 2 Errors
- p-value interaction
- Parametric Hypothesis Testing
- T-test
- Paired Samples t-test
- ANOVA Test
- Non-Parametric Hypothesis Testing
- Mann-Whitney U test
- Wilcoxon signed-rank test
- Kruskal-Wallis test
- Friedman test
- Theory of Estimation
- Difference between Estimators and Estimation
- Methods of Estimation
- Method of Moments
- Bayesian Estimation
- Least Square Estimation
- Maximum Likelihood Estimation
- Likelihood Function and Log-Likelihood Function
- Properties of Estimation
- Unbiasedness
- Consistency
- Sufficiency
- Completeness
- Robustness
- Confidence Intervals
Machine Learning Mathematics
if you want to build your career in the field of Machine Learning as a beginner or professional looking for a career change then before directly jumping into machine learning you have to know the few Mathematical Concepts which include Statistics, Probability Distribution, Linear Algebra and Matrix, Regression, Geometry, Dimensionality Reduction, Vector Calculus etc. Those Concepts are used very frequently in machine learning for example:- In ML what do we do? We Make a prediction model (Algorithms/classifiers) which is based on training data and then we use that model for making predictions for new data. To evaluate the quality of our model, we use a confusion matrix, which is based on the concept of conditional probability – a crucial mathematical concept. By understanding these mathematical concepts beforehand, it becomes easier for us to understand the concepts of machine learning.
So, That’s how mathematics is used in machine learning and that makes it a crucial part of Machine Learning.
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. Math is the core concept in machine learning which is used to express the idea within the machine learning model.
In this tutorial, we will look at different mathematics concepts and will learn about these modules from basic to advance with the help particular algorithm.
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