[2020] MACHINE LEARNING REGRESSION MASTERCLASS IN PYTHON (Udemy.com)

Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras

Created by: Dr. Ryan Ahmed

Produced in 2020

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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' 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

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Quality Score

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Overall Score : 90 / 100

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Course 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.
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.Who this course is 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

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Instructor Details

Dr. Ryan Ahmed

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 50,000+ students globally. He has over 15 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 con

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Reviews

4.5

20 total reviews

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Very Good to explain linear regression ,and Thank you and cheer up for your good content in the future

Good & clean explanations to learn and refresh knowledge for all level Data scientists and who interested in AI & ML professionals.

It was a good introduction to understanding of concepts and then applying them with help of Projects .

Mostra conceitos bsicos de regresso alm de mtodos bastante interessantes e teis para visualizar os dados.

I took this course after completing the ML Basics course from SuperDataScience team. Most of the regression techniques were already covered in ML Basics course but this course has very good exercises and I like working on Jupyter notebooks compared to the previous one that was done entirely on Spyder

I have already seen other courses by SuperDataScience, but this one needs a lots of improvements. It seems that "SuperDataScience" has been put here only to sell the package ! It's not up to the level of other SuperDataScience's courses like Kirill's or Hadelin...First, it works on an hold version of python. Moreover, the explanation are very basics. Private and public questions are not answered.Explanations on results and choices are not sufficient...In the overview, it is mentioned that we are going to see decision tree regression, but there is nothing about it.I would NOT recommend this courses. I'll rather recommend other courses from SuperDataScience Team...

One of the best courses I have ever Taken. Very High regards and respect to you Sir.

Poor quality: distracting sounds, parasite words, greeting in the beginning are always too loud, even distorted.Exercises are repetitive and too easy - just replacing 1 parameter.

Dr Ryan you are one of my Fav after this course

Wonderful course.... No useless information ( At least minimum with compare to other courses) ..... Perfect for who wants to learn Machine Learning for analysis.

It is good course but add the right subtitle will be very good.

so far so good. I don't like the concept of bonus lectures. Education should not be based on bonus lecture concept.Few suggestions: 1. Include Grid Search technique to find alpha in Lasso & Ridge2. Include elastic net regression