This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
EG
The whole specialization is extremely useful for people starting in ML. Highly recommended!
AA
the course is very powerful and I have jump to higher level regarding data wrangling and how to deal with data. the assessment have some error which can be fixed easily
BS
Some bugs in the assignment, but overall excellent discussion of how to avoid common pitfalls when using data for ML.
SC
Really good,... one thing you have to change is that your assumption of people knowing Python for Jupyter Notebook really well... the week 3 assignment was a pain for quite sometime
PN
Excellent depth in coverage. Lab, although only one, was instructive to enable learning while also being exhaustive and intensive to drive learnings home.
CC
Good course, if you follow the previous ones and if you know some python (Pandas).
NH
Excellent content with good programming assignments and examples.
KY
The programming assignment was tough, the instructions were a bit misleading. I didn't get all correct though.
PA
Well this course absolutely good,but you need patience when doing programming assignment,and there's a lot error tho,but what we need is that information,anna gave us the easiest insight
Showing: 20 of 24
The instructor is great, but please fix the programming assignment! There are so many typos it's embarassing. Also, the autograder EXPECTS typos in some variable names, so you can't even pass it if your answers are correct.
The experience with the programming assignment was very bad. There was an error that was occurring at frequent intervals which crashed my jupyter notebook, making me to start afresh. I was facing an issue in reopening the notebook where it took a long time and the mathematical notations were also not loaded properly.
It was a great course except for the 3-hour data cleaning assignment using Jupyter Notebook. Other Jupyter assignments worked fine, but this was was so extensive that it completely bogged down my (high end) laptop. After an hour or so every keystroke was slow. I rebooted and restarted multiple times and I was unable to finish (solely due to the technical challenges). It took me literally 2 hours to manage to raise by score from 19 to 20 to pass the darn class, struggling to get one more scored point to process and get graded.
Having said that, the instructor is terrific and I found it very informative (primarily as a refresher after a long career in data management, but I learned a number of things too). Well worth the time invested (not counting the very fustrating technical difficulties mentioned above).
The lab should be broken down into 3 labs.. it was very long..I wish there would be more hands on practice like that lab! good stuff! jupyter notebook is a great tool..I really enjoyed this course, thank you !
You'll learn how to be aware of your data and address different problems that could significantly affect your machine learning model. Plus, the practical assignment was really enjoyable.
Excellent depth in coverage. Lab, although only one, was instructive to enable learning while also being exhaustive and intensive to drive learnings home.
Some bugs in the assignment, but overall excellent discussion of how to avoid common pitfalls when using data for ML.
This is a great course. In fact, the theory was amazing. I´m very glad with you, I can understand the data better.
The whole specialization is extremely useful for people starting in ML. Highly recommended!
Good course, if you follow the previous ones and if you know some python (Pandas).
What is different about this course is its focus of ML applied to the real world.
Excellent content with good programming assignments and examples.
This is the best!!!
Nice course!
This course is very helpful if you want to learn Machine Learning. The primary objective of the course is to ensure you make proper decisions while handling your data. This course walks you through different types of data, problems surrounding it and how to tackle them. It's one of the finest courses on data. I do hope the instructor adds some coding tests on handling data.
It's a really nice course covering all the content related to data in Machine learning. The content is so detailed and the instructor have made the entire learning process very smooth. Thanks a lot for such a great course.
Well this course absolutely good,but you need patience when doing programming assignment,and there's a lot error tho,but what we need is that information,anna gave us the easiest insight
Really good,... one thing you have to change is that your assumption of people knowing Python for Jupyter Notebook really well... the week 3 assignment was a pain for quite sometime
the course is very powerful and I have jump to higher level regarding data wrangling and how to deal with data. the assessment have some error which can be fixed easily
The programming assignment was tough, the instructions were a bit misleading. I didn't get all correct though.