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Artificial Intelligence #2: Polynomial & Logistic Regression

Regression techniques for students and professionals. Learn Polynomial & Logistic Regression and code them in python

Created bySobhan N.

Last updated 12/2017

English

What you'll learn

Program Polynomial Regression from scratch in python.
Program Logistic Regression from scratch in python.
Predict output of model easily and precisely.
Use Regression model to solve real world problems.
Use Polynomial Regression to Model Non Linear Datasets.
Build Model to Predict CO2 and Global Temperature by Polynomial Regression.
Classify Handwritten Images by Logistic Regression
Classify IRIS Flowers by Logistic Regression

Course content

3 sections • 20 lectures • 2h 8m total length

Introduction3:00
Learn polynomial regression to model nonlinear relationships and logistic regression for classification in Python; estimate nonlinear functions and classify iris flowers, bulbs, and handwritten digits with sample datasets.
Required Softwares and Libraries0:10

Polynomial Regression Theory4:45
Explore polynomial regression theory, modeling the expected value of y with an nth degree polynomial of x, using the design matrix and least squares to estimate linear-in-parameters models.
Polynomial Regression Sine Function Part-112:22
Learn to apply polynomial regression from first to third degree on data x and y, fit with coefficients, visualize with plots, and evaluate using r-squared.
Polynomial Regression Sine Function Part-211:00
Master polynomial regression to fit a sine function by building second- and third-order models and visualizing results with plots. Achieve high accuracy and R-squared improvements through higher-order fits.
Polynomial Regression Sine Function Source Code0:18
Polynomial Regression Built-in Dataset Part-17:20
Demonstrate polynomial regression with a built-in dataset by generating 300 samples with 2 features, adding noise, applying degree-2 polynomial features, and training a linear regression model with a 70/30 split.
Polynomial Regression Built-in Dataset Part-27:01
Learn to build a second-degree polynomial regression model from a built-in dataset, evaluate it with mean squared error and accuracy, and explore higher-order polynomial options.
Polynomial Regression Built-in Dataset Source0:12
Polynomial Regression CO2vsTemp part-111:48
Investigate the relationship between global temperature and CO2 using polynomial regression on a dataset from 1880 to 2012. Create plots and build regression models to visualize and assess the connection.
Polynomial Regression CO2vsTemp part-213:15
Build a third-degree polynomial regression to estimate temperature from CO2, using polynomial features, train-test splits, linear regression, and evaluate with R-squared and mean squared error.
Polynomial Regression CO2vsTemp Source0:21

Logistic Regression Theory8:17
Explore logistic regression theory for binary classification by modeling a binary dependent variable with predictor variables using the logistic function and maximum likelihood estimation.
Logistic Regression for Blobs Datasets part-18:31
Build a logistic regression model to classify blobs datasets, using make_blobs for data, standard scaler, train/test split, and evaluation with confusion matrix, classification report, and accuracy metrics, with seaborn visualizations.
Logistic Regression for Blobs Datasets part-28:21
Standardize input variables to prepare blob datasets, train a logistic regression model, then predict and evaluate results using confusion matrices, accuracy, and classification reports with visual heatmaps.
Logistic Regression for Blobs Datasets Source0:15
Logistic Regression for IRIS Flowers15:25
Train a logistic regression classifier on the iris dataset to distinguish three flower classes, using standardized features, a train-test split, and evaluation metrics like confusion matrix and accuracy.
Logistic Regression for IRIS Flowers Source0:21
Logistic Regression Handwritten Digits15:44
Classify handwritten digits with logistic regression on a 1000-sample 28x28 grayscale dataset, featuring shuffle, a test set, and evaluation via confusion matrices, heatmaps, and accuracy across ten classes.
Logistic Regression Handwritten Digits Source0:22

Requirements

You should know about basic statistics
You must know basic python programming
Install Sublime and required library for python
You should have a great desire to learn programming and do it in a hands-on fashion, without having to watch countless lectures filled with slides and theory.
All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.

Description

In statistics, Logistic Regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical. This article covers the case of a binary dependent variable—that is, where the output can take only two values, "0" and "1", which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. Cases where the dependent variable has more than two outcome categories may be analysed in multinomial logistic regression, or, if the multiple categories are ordered, in ordinal logistic regression. In the terminology of economics, logistic regression is an example of a qualitative response/discrete choice model.

Logistic Regression was developed by statistician David Cox in 1958. The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). It allows one to say that the presence of a risk factor increases the odds of a given outcome by a specific factor.

Polynomial Regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in X. Polynomial regression fits a nonlinear relationship between the value of X and the corresponding conditional mean of Y. denoted E(y |x), and has been used to describe nonlinear phenomena such as the growth rate of tissues, the distribution of carbon isotopes in lake sediments, and the progression of disease epidemics. Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, Polynomial Regression is considered to be a special case of multiple linear regression.

The predictors resulting from the polynomial expansion of the "baseline" predictors are known as interaction features. Such predictors/features are also used in classification settings.

In this Course you learn Polynomial Regression & Logistic Regression You learn how to estimate output of nonlinear system by Polynomial Regressions to find the possible future output Next you go further You will learn how to classify output of model by using Logistic Regression

In the first section you learn how to use python to estimate output of your system. In this section you can estimate output of:

Nonlinear Sine Function

Python Dataset

Temperature and CO2

In the Second section you learn how to use python to classify output of your system with nonlinear structure .In this section you can estimate output of:

Classify Blobs

Classify IRIS Flowers

Classify Handwritten Digits

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Important information before you enroll:

In case you find the course useless for your career, don't forget you are covered by a 30 day money back guarantee, full refund, no questions asked!

Once enrolled, you have unlimited, lifetime access to the course!

You will have instant and free access to any updates I'll add to the course.

You will give you my full support regarding any issues or suggestions related to the course.

Check out the curriculum and FREE PREVIEW lectures for a quick insight.

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Click the "Take This Course" button at the top right now!

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Best Regrads,

Sobhan

Who this course is for:

Anyone who wants to make the right choice when starting to learn Linear & Multi Linear Regression.
Learners who want to work in data science and big data field
students who want to learn machine learning
Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method.
Modelers, Statisticians, Analysts and Analytic Professional.

Artificial Intelligence #2: Polynomial & Logistic Regression

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 3min

Polynomial Regression10 lectures • 1hr 8min

Logistic Regression8 lectures • 57min

Requirements

Description

Who this course is for: