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Machine Learning Regression Masterclass in Python

Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras
4.7
☆☆☆☆☆ 4.7/5
(540 reviews)
5,014 students
Created by
Dr. Ryan Ahmed, Ph.D., MBA

9.7

Classbaze Grade®

10.0

Freshness

9.0

Popularity

9.6

Material

Detailed Analysis »

Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python
Go to course »
Platform: Udemy
Price: check current price
Video: 10h 21m
Language: English
Next start: On Demand

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Classbaze Rating

Classbaze Grade®

9.7 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness

10.0 / 10
This course was last updated on 6/2022.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Popularity

9.0 / 10
We analyzed factors such as the rating (4.7/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Material

9.6 / 10
Video Score: 9.2 / 10
The course includes 10h 21m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 5 hours 48 minutes of 749 Machine Learning courses on Udemy.
Detail Score: 9.7 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

3 articles.
1 resources.
0 exercise.
0 test.

In this page

About this course

  The course will teach you this
  What you need to START the course
  Who can start this course?
General questions
  Coupons & Discounts
  Guarantees & money back
  Financial aid
Teacher
Classbaze hand picked recommendations

About the course

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:
· Simple Linear Regression
· Multiple Linear Regression
· Polynomial Regression
· Logistic Regression
· Decision trees regression
· Ridge Regression
· Lasso Regression
· Artificial Neural Networks for Regression analysis
· 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.

What can you learn from this course?

✓ 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’ 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

What you need to start the course?

• Machine Learning basics
• PC with Internet connetion

Who is this course is made for?

• 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

Are there coupons or discounts for Machine Learning Regression Masterclass in Python ? What is the current price?

The course costs $19.99. And currently there is a 83% discount on the original price of the course, which was $99.99. So you save $80 if you enroll the course now.
The average price is $13.6 of 749 Machine Learning courses. So this course is 47% more expensive than the average Machine Learning course on Udemy.

Will I be refunded if I'm not satisfied with the Machine Learning Regression Masterclass in Python course?

YES, Machine Learning Regression Masterclass in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any financial aid for this course?

Currently we could not find a scholarship for the Machine Learning Regression Masterclass in Python course, but there is a $80 discount from the original price ($99.99). So the current price is just $19.99.

Who will teach this course? Can I trust Dr. Ryan Ahmed, Ph.D., MBA?

Dr. Ryan Ahmed, Ph.D., MBA has created 44 courses that got 27,789 reviews which are generally positive. Dr. Ryan Ahmed, Ph.D., MBA has taught 295,730 students and received a 4.6 average review out of 27,789 reviews. Depending on the information available, we think that Dr. Ryan Ahmed, Ph.D., MBA is an instructor that you can trust.

Learn everything about

Dr. Ryan Ahmed, Ph.D., MBA

Professor & Best-selling Instructor, 250K+ students
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan’s mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. 
Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 280,000+ students globally. He has over 25 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. 
Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.


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Dr. Ryan Ahmed, Ph.D., MBA's full profile and courses »
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9.7

Classbaze Grade®

10.0

Freshness

9.0

Popularity

9.6

Material

Detailed Analysis »

Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python
Go to course »
Platform: Udemy
Price: check current price
Video: 10h 21m
Language: English
Next start: On Demand

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