https://github.com/tsg405/applied-machine-learning-in-python
This Repo contains - Starter files, Coursework, Programming Assignments for the course --> Applied Machine Learning in Python, University of Michigan [COURSERA]
https://github.com/tsg405/applied-machine-learning-in-python
applied-machine-learning assignment classification coursera data-science fruit-dataset machine-learning matplotlib-pyplot numpy pandas python quiz regression scikit-learn scipy seaborn supervised-machine-learning university-of-michigan unsupervised-machine-learning
Last synced: 12 days ago
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This Repo contains - Starter files, Coursework, Programming Assignments for the course --> Applied Machine Learning in Python, University of Michigan [COURSERA]
- Host: GitHub
- URL: https://github.com/tsg405/applied-machine-learning-in-python
- Owner: TSG405
- License: bsd-3-clause
- Created: 2021-10-09T09:04:19.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-10-10T13:29:52.000Z (over 3 years ago)
- Last Synced: 2025-03-27T23:51:07.230Z (30 days ago)
- Topics: applied-machine-learning, assignment, classification, coursera, data-science, fruit-dataset, machine-learning, matplotlib-pyplot, numpy, pandas, python, quiz, regression, scikit-learn, scipy, seaborn, supervised-machine-learning, university-of-michigan, unsupervised-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 38.5 MB
- Stars: 10
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Applied Machine Learning in Python --- Coursera
This Repo contains - Starter files, Coursework, Programming Assignments for the course --> Applied Machine Learning in Python, University of Michigan [COURSERA]
## About the Course
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.
This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
## Certificate
#### ALL THE BEST, LEARNERS!
### @ TSG405