This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
- 5 stars71.63%
- 4 stars21.20%
- 3 stars4.82%
- 2 stars1.14%
- 1 star1.18%
来自APPLIED MACHINE LEARNING IN PYTHON的热门评论
The course was really interesting to go through. All the related assignments whether be Quizzes or the Hands-On really test the knowledge. Kudos to the mentor for teaching us in in such a lucid way.
It's a nice course. It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.
EXTREMELY USEFUL AND GOOD COURSE, CONGRATULATIONS TO ALL THE PEOPLE INVOLVE.
Honestly, I never thought I could learn so much in an online course, excited for the rest of the specialization
Great content and good instruction. Need to fix the files in the assignments though. It's hard to keep track in the forums and frustrating go back and forth to find out why it's not working.