Supervised Machine Learning In Python : Regression Analysis

[LeeAndro]

Published 7/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 214.60 MB | Duration: 0h 58m

Learn to Implement Regression Models in Scikit-learn ( sklearn ) : A Python Artificial Intelligence Library

What you'll learn
Describe the input and output of a regression model
Prepare data with feature eeering 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 . This course is designed most straightforwardly to utilize your wisely. Get ready to do more learning than your machine!Happy Learning.Career Growth:Employment website Indeed has listed machine learning eeers 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 Teologies

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 deteation

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,Bners starting out to the field of Machine Learning

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