This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
SP
Well structured course. Concepts are explained clearly with hands on exercises.
AI
The course is extremely good in understanding the concepts of regressions. Great work
MK
I've got great insights from this course!, I would recommend it to anyone looking to bush up ML skills.
MK
It was a great learning experience with in-depth knowledge and practice-based demos helped me to understand the concepts easily.
MM
This course is very helpful. The wonderfull part in this course was the final course project in which I had to create my own linear regression model by adding polynimial features and regularization.
AK
Highly recommended if you want to learn Supervised learning : regression professionally with library. You should have prior knowledge to mathematics.
RR
Interesting course focusing more on the regression for the machine learning
GP
very clear contents and explanations. Regression methods are thoroughly explained. Examples of coding are indeed a very good basis to start coding on the project.
RP
I recommend this course to everyone who wants to excel in Machine Learning. This is a Great Course!
NA
Learned really about supervised learning and more importantly regularization and some available methods.
NV
Very well designed course, great that we could work with our own data and apply the theory. Looking forward to continue the journey.
AJ
It's a nice course it deserve a 5/5 but some common and better regression algorithm like Decision Trees and Random Forest were not taught unlike the Classification part. Thanks
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It is actually disguisting course. Simply reading the powerpoint without any clear explanation. So bad
Really good course but it is whistle-stop through the methods. I strongly recommend getting a book to accompany the course if you are relatively new just so you can cross reference some of the methods and functions.
I found some of the examples a little more difficult to apply to the course work because of how they were demonstrated in the lab. This is NOT a bad thing, all good learning, but when you're trying to unpack things it's good to have another reference source handy.
Very well designed course, great that we could work with our own data and apply the theory. Looking forward to continue the journey.
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
The balance between theory and application is such that both are left quite poorly covered. One does not get an understanding of how algorithms work, explanations focus on 'intuititve' understanding. At the same time, the coding part is not particularly detailed, either. Moreover, there are several mistakes in videos, quizzes and jupyter lab books. I would not recommend this course.
The instructor was very bad. He was only reading the slides without making any further examples or explanations.
Very hard to follow because the instructor is just reading the blurry powerpoint.
Learned really about supervised learning and more importantly regularization and some available methods.
I recommend this course to everyone who wants to excel in Machine Learning. This is a Great Course!
very detailed. However, it is better if the gradient decent has its lesson.
Great course! Covered everything I wished to learn!
Before taking this course, I tested similar courses offered by other institutes or universities. I am glad that I chose IBM because it has a good balance of concepts and applications. I learned a lot from this course. and will be using what I learned in analyzing experimental and survey data.
I gave this course a 4 instead of 5 because there was insufficient explanation on the different evaluation metrics.
Good overview of the different regression models and the theory behind them. Could be a bit more attention to common pittfalls and type and size of problems which are usually addressed by these methods.
This is a perfect course for learning and implementing supervised machine learning for regression tasks in Python. However, it does not have a comprehensive explanation of how linear regression and regularization work behind the scenes. This course should be complemented with DeepLearning.AI and Stanford University's supervised machine learning course for more in-depth knowledge about the algorithm.
Pretty mediocre. Quizzes are jejune and sometimes quite sloppy. Most of what I learned actually came from Googling--a common Coursera complaint, I realize. This course isn't awful, but I definitely would not pay for it.
The course is incomplete on regression analysis. Also, the grading scale was biased after putting in a lot of time and effort(20 pages). The reason was I didn't follow the assignment questions.
Really difficult. Exams are not fully fair. Example: First exam in Week 3 - including videos 1 to 4. There is one question which answer is in video 8. And so many examples like this one.
Slides in videos are not provided.
It is a very bad course. I am sorry, but you are not clear enough with the theme. I have read every notebook and it is missing a lot of information.
I had an excellent experience with the Supervised Machine Learning: Regression course on Coursera. The course is exceptionally well-structured, starting from the fundamentals and gradually building up to more advanced concepts in a clear and intuitive way. The explanations of key topics such as linear regression, cost functions, gradient descent, and model evaluation were thorough and easy to understand. The instructors did a fantastic job breaking down complex mathematical concepts into simple, practical explanations. What I appreciated most was the balance between theory and hands-on practice. The coding exercises and real-world examples helped reinforce my understanding and boosted my confidence in applying regression techniques to actual problems. The quizzes and assignments were thoughtfully designed to test comprehension without being overwhelming. Overall, this course significantly strengthened my foundation in supervised machine learning and gave me practical skills I can apply in real-world projects. I highly recommend this course to anyone looking to build a strong understanding of regression and supervised learning.
I have seen various courses on machine learning and linear regression. This course has been one of the best courses in this field. It provides great detail in the theory and addresses important issues in the field regarding features engineering, regularization, and Ridge, LASSO, and Elastic net models. It also has great practice labs.