Supervised Machine Learning In Python Regression Analysis

What you'll learn
Describe the input and output of a regression model
Prepare data with feature engineering techniques
Implement Linear & Polynomial Regression, RANSAC Regression, Decision Tree & Random Forest Regression, Support Vector Regression, Neural Networks models
Use a variety of error metrics to select a regression model that best suits your data
Requirements
Basic knowledge of Python Programming
Description
Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There's an endless supply of industries and applications that machine learning can make more efficient and intelligent. Supervised machine learning is the underlying method behind a large part of this. Supervised learning involves using some algorithm to analyze and learn from past observations, enabling you to predict future events. This course introduces you to one of the prominent modelling families of supervised Machine Learning called Regression. This course will teach you to implement supervised classification machine learning models in Python using the Scikit learn (sklearn) library. You will become familiar with the most successful and widely used classification techniques, such as:Linear RegressionPolynomial RegressionRANSAC RegressionDecision Tree RegressionRandom Forest RegressionSupport Vector RegressionNeural NetworksYou will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. The complete course is built on several examples where you will learn to code with real datasets. By the end of this course, you will be able to build machine learning models to make predictions using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!Happy Learning.Career Growth:Employment website Indeed has listed machine learning engineers as #1 among The Best Jobs in the U.S., citing a 344% growth rate and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.
Overview
Section 1: Fundamentals
Lecture 1 Introduction
Lecture 2 Artificial Intelligence
Lecture 3 Machine Learning
Lecture 4 Supervised Learning
Lecture 5 Supervised Learning: Classifications
Lecture 6 Supervised Learning: Regressions
Lecture 7 Installation of Python Platform
Section 2: Building and Evaluating Regression ML Models
Lecture 8 Important Terminologies
Lecture 9 Simple Linear Regression
Lecture 10 Multiple Linear Regression
Lecture 11 Splitting Data
Lecture 12 Residuals
Lecture 13 Mean Absolute Error (MAE)
Lecture 14 Mean Squared Error (MSE)
Lecture 15 Root Mean Squared Error (RMSE)
Lecture 16 Max Error
Lecture 17 R² score, the coefficient of determination
Lecture 18 Polynomial Regression
Lecture 19 RANSAC Regression
Lecture 20 Decision Tree Regression
Lecture 21 Random Forest Regression
Lecture 22 Support Vector Regression
Lecture 23 Neural Networks
Research scholars and college students,Industry professionals and aspiring data scientists,Beginners starting out to the field of Machine Learning

Homepage

https://www.udemy.com/course/regressionanalysis/