# Handson ML

## Soft Clustering with Gaussian Mixture Models (GMM)

GMM Theory¶ The Gaussian Mixture Model is a generative model that assumes that data are generated from multiple Gaussion distributions each with own Mean and variance. the Gaussian Mixture Models or Mixture of Gaussians models a convex combination of the various distributions. Unlike K-Means, with Gaussian Mixture Models we want to define a probability distribution on the data. In order to do that, we need to convert our clustering problem

## Hard clustering with K-means

kmeans Theory¶ K-Means is the simplest and most fundemental clustering algorithm. Given: $x_1,x_2,…,x_n$, Where $x \in I\!R^d$ Output: Clusters $C_1,C_2,…,C_n$, Where $C_i \in \{1,2,..K\}$ Goal: Partition data into K clusters(groups) where each cluster has similar data. The goal is pretty clear. you have a bunch of data from which you may or may not know the generative distrubition. you want to learn the structure of the data in a such

## Pills of ML & AI: Feature Selection

Did you know that there are basically three ways of selecting the most important features before feeding your database into an ML model?

## Predict house prices with dense neural networks and tensorflow

In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture).

## Multi Label Classification Of Texts With NLTK

MultiLabelClassification-with-nltk MULTI LABEL CLASSIFICATION WITH NLTK¶ In this tutorial, I will show you how to predict tags for a text. In this post, we will build a multi-label model that’s capable of detecting different types of toxicity in a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. The types of toxicity are: toxic severe_toxic obscene threat insult identity_hate The data set used can

## Discrete Probability Distributions

8 Discrete Probability Distributions¶ 8.2 Binomial Distribution¶ The following code plots the probability mass function (PMF) of $B_{p,n}$, the binomial distribution with parameters $p$ and $n$. It contains interactive sliders that you can use to vary $n$ over the interval $[0,30]$ and $p$ over the interval $[0, 1]$. In [1]: %matplotlib inline Let us now load the required code and analyze it part by part. In [2]: # %load plot_pmf.py import numpy