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Supervised Learning

• Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
• It infers a function from labeled training data consisting of a set of training examples

Unsupervised Learning

• Unsupervised learning is a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels. It is also known as self-organization and allows modeling probability densities of given inputs.
• Goal - Uncover structure in Data - structure underline data
• Exa: Topic Modeling - Many Document with text from newspaper , no labeling Out job us to group there

Probabilistic Model - Set of probability distribution

What is the difference between supervised and unsupervised learning?

• Supervised learning and Unsupervised learning are machine learning tasks. ...
• Unsupervised learning is where you only have input data and no corresponding output variables.
• Training dataset: A set of examples used for learning, where the target value is known.

Data Modeling

• Block 1. Data
• Block 2. Build Model - We define model to get to goal
• Block 3. Infer Hidden Variable - Algorithm - model have unknown params called block -3- Define objective function
• Block 4. predict and Explore -Goal - What we want to do

Gaussian Distribution Multivariate

Block 2 - Probabilistic Model

• is set of probability distribution p(x|@) on data
• We Pick up distributions family

Block-3 - Maximum Likelihood Estimation

• How do we learn parameters of model

• Linear Regression Example Education , Seniority and Income

Logistic regresssion

https://towardsdatascience.com/logistic-regression-using-python-sklearn-numpy-mnist-handwriting-recognition-matplotlib-a6b31e2b166a

**Panda and numpy ** https://jakevdp.github.io/PythonDataScienceHandbook/