Introduction into Machine Learning

What is Machine Learning

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Machine learning is a type of artificial intelligence (AI) that allows computer systems to learn and improve from experience without being explicitly programmed. It involves the use of algorithms and statistical models to analyze and draw insights from data, and then use those insights to make predictions or take actions.

What is Supervised Learning?

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Supervised learning is a type of machine learning where the algorithm learns to predict an output variable (also known as the "target" or "dependent" variable) based on a set of input variables (also known as "predictors" or "independent" variables) that have been labeled with corresponding output values.

What is Unsupervised Learning?

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Unsupervised learning is a type of machine learning where the algorithm learns to identify patterns and relationships in a dataset without being given labeled output data. The goal of unsupervised learning is to find interesting structures in the data that can be used to understand the underlying distribution of the data.

Give me 1 case each of Supervised learning and unsupervised learning?

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Supervised Learning: Bank Loan

Unsupervised: Customer Segementation

Explain to me what Linear Regression is and How it works?

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Linear regression is a type of supervised learning algorithm used to model the relationship between a dependent variable (also known as the target variable) and one or more independent variables (also known as predictors). It is called "linear" regression because it assumes that the relationship between the variables can be modeled by a linear equation.

The goal of linear regression is to find the best-fitting straight line that can explain the relationship between the independent and dependent variables. The equation of the line is given by:

y = b0 + b1x1 + b2x2 + ... + bnxn

where y is the dependent variable, x1, x2, ..., xn are the independent variables, and b0, b1, b2, ..., bn are the coefficients that determine the slope and intercept of the line.

To find the values of the coefficients, the linear regression algorithm uses a technique called "ordinary least squares." This involves minimizing the sum of the squared differences between the predicted values and the actual values of the dependent variable. This is known as the "cost function" or "error function."

What is Classification and give me 1 case of classification

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Classification is a type of supervised learning algorithm used to predict the categorical class of a given input. The goal of classification is to find a function that can accurately assign new inputs to one of several pre-defined classes based on their features.

Case: Spam Email filtering

What is Logistic Regression and How does it work?

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Logistic regression is a type of classification algorithm used to predict the probability of a binary (yes/no) outcome based on one or more predictor variables. The goal of logistic regression is to find the best-fitting S-shaped curve (also known as the "sigmoid function") that can accurately model the relationship between the predictor variables and the probability of the outcome.

The logistic regression algorithm works by first calculating the weighted sum of the predictor variables, similar to linear regression. However, instead of directly predicting the outcome, the logistic regression algorithm applies the sigmoid function to the weighted sum, which transforms the output into a probability value between 0 and 1.

Explain to me about the Gauss-Markov assumption

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The Gauss-Markov assumption is a set of assumptions that are made when using linear regression to model the relationship between a dependent variable and one or more independent variables. The assumptions are important because they ensure that the estimates of the regression coefficients obtained from the data are unbiased, efficient, and have the smallest variance possible.

The Gauss-Markov assumption states that:

- Linearity: The relationship between the dependent variable and the independent variables is linear.

- Independence: The errors (the differences between the observed values of the dependent variable and the predicted values from the model) are independent of each other and have a mean of zero.

- Homoscedasticity: The errors have constant variance, which means that the variability of the errors is the same for all levels of the independent variables.

- No multicollinearity: There is no perfect multicollinearity among the independent variables, which means that the independent variables are not highly correlated with each other.

- Normality: The errors are normally distributed, which means that the distribution of the errors is symmetric and bell-shaped.

Explain about what is an independent variable and dependent variable

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In statistics and research, an independent variable is a variable that is manipulated or controlled by the experimenter or researcher. It is often denoted by the letter "X" and is used to predict or explain changes in the dependent variable. The independent variable is also known as the predictor variable, explanatory variable, or input variable.

In contrast, a dependent variable is a variable that is affected by changes in the independent variable. It is often denoted by the letter "Y" and is the outcome or response variable that is being measured or observed. The dependent variable is also known as the response variable, outcome variable, or output variable.

Determine the independent and dependent variables from the scenario below

scenario: A cow is given a growth hormone and then compared to another cow that was not given a growth hormone.  Both cows were weighed at 2 years.

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Indepndent:

growth hormone

cows

Dependent:

weight of cow