## Naive Bayes classification

The basis It’s based on Bayes’ theorem (check the wikipedia link, and see how complex the decision trees could be). Assumes predictors contribute independently to the classification. Works well in supervised learning problems. Works with continuous and discrete data. Can work with discrete data sets. It is not sensitive to non-correlating “predictors”. Naives Bayes plot Example: … Read more

## Understanding Logistics regression

The basis Logistics or Logit regression. It’s a regression model where the dependent variable (DV) is categorical. Outcome follows a Bernoulli distribution. Success = 1 , failure = 0. Natural logarithm of odds ratio ln (p/1-p)… logit(p). Inverse log curve gives a nice “s” curve to work with. Equate logarithm of odds ratio with regression line equation. Solve for probability … Read more

## Linear regression example

I was looking for a simple example of a regression and how to calculate it by hand. I found this one: least squares example. the main formula to calculate the linear regression is   y = Ḇo + Ḇ1xcontinue learning the basis !

## Overfitting

What is overfitting? when you are preparing a machine learning solution, you work basically with data sets that contains: Data: relevant and/or important data. Noise: inrelevant and/or non important data. With this data you want to identify a trigger, a signal that responds to your target pattern you want that your code identifies. So you … Read more

## My First Machine Learning Project in Python Step-By-Step

This post is very interesting to me to understand the steps to build a Machine Learning code: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/

## Understanding machine learning

I have watched this video from @TessFerrandez: Machine Learning for Developers. The video explains how the process of building a machine learning solution is. She explains it in plain English and with very nice examples easy to remind the concepts. The video helped me to link a lot of technical ideas explained in the courses with … Read more

## Machine Learning, sources of information

My problem I want to learn Machine Learning concepts, understand how to apply on real cases. There are so many sources of information, some of them with good/bad quality, some others very complex. The questions are simple: when is useful to use machine learning solution? what is a neuronal net? what are the steps to … Read more

## AWS Machine Learning for enterprises

Today I attended this webinar to introduce the main components that AWS offers for working on Machine Learning. I like this picture that explains in plain english what is Deep Learning, machine learning and Artificial Inteligence. The main component of the overall solution is SageMaker, which is an integrated deep learning development and deployment platform, launched … Read more

## Type errors

When you face the classic decision making table, as the one below. You can have to types of errors. They are known as: Type I error: false positives. Type II error: false negatives. The best way to remember it is with this picture below: