Machine Learning Regression Masterclass in Python_1

Machine Learning Regression Masterclass in Python, available at $89.99, has an average rating of 4.41, with 83 lectures, based on 756 reviews, and has 6859 subscribers.

You will learn about Master Python programming and Scikit learn as applied to machine learning regression Understand the underlying theory behind simple and multiple linear regression techniques Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy Apply multiple linear regression to predict stock prices and Universities acceptance rate Cover the basics and underlying theory of polynomial regression Apply polynomial regression to predict employees鈥?salary and commodity prices Understand the theory behind logistic regression Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features Understand the underlying theory and mathematics behind Artificial Neural Networks Learn how to train network weights and biases and select the proper transfer functions Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance Apply ANNs to predict house prices given parameters such as area, number of rooms..etc Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test Understand the underlying theory and intuition behind Lasso and Ridge regression techniques Sample real-world, practical projects This course is ideal for individuals who are Data Scientists who want to apply their knowledge on Real World Case Studies or Machine Learning Enthusiasts who look to add more projects to their Portfolio It is particularly useful for Data Scientists who want to apply their knowledge on Real World Case Studies or Machine Learning Enthusiasts who look to add more projects to their Portfolio.

Enroll now: Machine Learning Regression Masterclass in Python

Summary

Title: Machine Learning Regression Masterclass in Python

Price: $89.99

Average Rating: 4.41

Number of Lectures: 83

Number of Published Lectures: 77

Number of Curriculum Items: 83

Number of Published Curriculum Objects: 77

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Master Python programming and Scikit learn as applied to machine learning regression

Understand the underlying theory behind simple and multiple linear regression techniques

Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy

Apply multiple linear regression to predict stock prices and Universities acceptance rate

Cover the basics and underlying theory of polynomial regression

Apply polynomial regression to predict employees鈥?salary and commodity prices

Understand the theory behind logistic regression

Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features

Understand the underlying theory and mathematics behind Artificial Neural Networks

Learn how to train network weights and biases and select the proper transfer functions

Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods

Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance

Apply ANNs to predict house prices given parameters such as area, number of rooms..etc

Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test

Understand the underlying theory and intuition behind Lasso and Ridge regression techniques

Sample real-world, practical projects

Who Should Attend

Data Scientists who want to apply their knowledge on Real World Case Studies

Machine Learning Enthusiasts who look to add more projects to their Portfolio

Target Audiences

Data Scientists who want to apply their knowledge on Real World Case Studies

Machine Learning Enthusiasts who look to add more projects to their Portfolio

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.

The purpose of this course is to provide students with knowledge of key aspects of machine learning regression techniques in a practical, easy and fun way. Regression is an important machine learning technique that works by predicting a continuous (dependant) variable based on multiple other independent variables. Regression strategies are widely used for stock market predictions, real estate trend analysis, and targeted marketing campaigns.

The course provides students with practical hands-on experience in training machine learning regression models using real-world dataset. This course covers several technique in a practical manner, including:

路 Simple Linear Regression

路 Multiple Linear Regression

路 Polynomial Regression

路 Logistic Regression

路 Decision trees regression

路 Ridge Regression

路 Lasso Regression

路 Artificial Neural Networks for Regression analysis

路 Regression Key performance indicators

The course is targeted towards students wanting to gain a fundamental understanding of machine learning regression models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master machine learning regression models and can directly apply these skills to solve real world challenging problems.

Course Curriculum

Chapter 1: INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]

Lecture 1: Course Welcome Message

Lecture 2: Updates on Udemy Reviews

Lecture 3: Course Overview

Lecture 4: EXTRA: Learning Path

Lecture 5: ML vs. DL vs. AI

Lecture 6: Get the materials

Chapter 2: ANACONDA AND JUPYTER INSTALLATION

Lecture 1: Download and Set up Anaconda

Lecture 2: What is Jupiter Notebook

Chapter 3: SIMPLE LINEAR REGRESSION

Lecture 1: Intro to Simple Linear Regression

Lecture 2: Simple Linear Regression Intuition

Lecture 3: Least Squares

Lecture 4: Project #1 – Overview

Lecture 5: Project #1 – Data Visualization

Lecture 6: Project #1 – Divide Data into Training and Testing

Lecture 7: Project #1 – Train Model

Lecture 8: Project #1 – Test Model

Lecture 9: Project #2 – Overview

Lecture 10: Project #2 – Solution

Lecture 11: Project #2 – Visualization

Lecture 12: Project #2 – Prepare Training and Testing Data

Lecture 13: Project #2 – Test Model

Lecture 14: Project #2 – Model Testing

Chapter 4: REGRESSION KEY PERFORMANCE INDICATORS

Lecture 1: Regression Metrics Intro

Lecture 2: Regression Metric Part 1

Lecture 3: Regression Metric Part 2

Lecture 4: Bias Variance Tradeoff

Chapter 5: POLYNOMIAL REGRESSION

Lecture 1: Polynomial Regression Intro

Lecture 2: Polynomial Regression – Intuition

Lecture 3: Poly Regression – Salary Load Data

Lecture 4: Poly Regression – Visualize Data

Lecture 5: Poly Regression – Linear Trainingtesting

Lecture 6: Poly Regression – Poly Part 1

Lecture 7: Poly Regression – Poly Part 2

Lecture 8: Poly Regression Project 2 Overview

Lecture 9: Poly Regression – Economies Linear -1

Lecture 10: Poly Regression – Economies Linear -2

Lecture 11: Poly Regression – Economies Poly

Chapter 6: MULTIPLE LINEAR REGRESSION

Lecture 1: Multiple Linear Regression Intro

Lecture 2: Multiple Linear Regression Overview

Lecture 3: Project #1 – Load Data and Libraries

Lecture 4: Project #1 – Data Visualization

Lecture 5: Project #1 – Model Training and Evaluation

Lecture 6: Project #1 – Model Results Evaluation

Lecture 7: Project #2 – Overview

Lecture 8: Project #2 – Load Data

Lecture 9: Project #2 – Data Visualization

Lecture 10: Project #2 – Train the Model

Lecture 11: Project #2 – Model Evaluation

Lecture 12: Project #2 – Retraining Model

Chapter 7: LOGISTIC REGRESSION

Lecture 1: Logistic Regression Intro

Lecture 2: Logistic Regression Intuition

Lecture 3: Confusion Matrix

Lecture 4: Project #2 – Data Import

Lecture 5: Project #2 – Visualization

Lecture 6: Project #2 – Data Cleaning

Lecture 7: Project #2 – Training Testing

Lecture 8: Model Testing Visualization

Chapter 8: APPLY ARTIFICIAL NEURAL NETWORKS TO PERFORM REGRESSION TASKS

Lecture 1: Artificial Neural Networks Intro

Lecture 2: Theory Part 1

Lecture 3: Theory Part 2

Lecture 4: Theory Part 3

Lecture 5: Theory Part 4

Lecture 6: Theory Part 5

Lecture 7: Theory Part 6

Lecture 8: Project – Load Dataset

Lecture 9: Project – Visualize Dataset

Lecture 10: Scale the Data

Lecture 11: Train the Model

Lecture 12: Evaluate the Model

Lecture 13: Multiple Linear regression

Lecture 14: Model Improvement with more features

Chapter 9: LASSO AND RIDGE REGRESSION

Lecture 1: Ridge and Lasso Intro

Lecture 2: Ridge Lasso Part 1

Lecture 3: Ridge Lasso Part 2

Lecture 4: Ridge Lasso Part 3

Lecture 5: Ridge and Lasso in Practice

Chapter 10: Congratulations!! Dont forget your Prize 馃檪

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

Dr. Ryan Ahmed, Ph.D., MBA
Best-Selling Professor, 400K+ students, 250K+ YT Subs

SuperDataScience Team
Helping Data Scientists Succeed

Mitchell Bouchard
B.S, Host @RedCapeLearning 540,000 + Students

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 7 votes

2 stars: 12 votes

3 stars: 58 votes

4 stars: 265 votes

5 stars: 414 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!