"The road to machine learning starts with Regression. Running a regression model is a no-brainer. Once you are finished reading this article, you'll able to build, improve, and optimize regression models on your own. Note: This article is best suited for people new to machine learning with requisite knowledge of statistics.

Udemy Coupon - Machine Learning Regression Masterclass in Python, Build 8 Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard English [Auto-generated] Students also bought Deep Learning Prerequisites: Linear Regression in Python Learn Regression Analysis for Business Regression Analysis / Data Analytics in Regression Regression Analysis for Statistics & Machine Learning in R Machine Learning for Beginners: Linear Regression model in R Preview this Course GET COUPON CODE Description Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020.

Learn to build a Polynomial Regression model to predict the values for a non-linear dataset. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.

In previous stories, I have given a brief of Linear Regression and showed how to perform Simple and Multiple Linear Regression. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.

I'm almost certain that now you might want to learn about these branches in greater detail. Worry not, I'll surely open the gates to these subsets in the posts to come. If you missed my post, you can find it at the following link: Branches of Artificial Intelligence. Previously, we discussed Machine Learning. We also discussed its subsets -- Supervised Learning, Unsupervised Learning, and Reinforcement Learning.