About the Course
The first part of this course deals with the basics of python programming language. The second part begins with an introduction of machine learning followed by a comparison of supervised and unsupervised machine leaning concepts. Cluster analysis is discussed as a category of unsupervised machine learning. Subsequently, it offers an in-depth theoretical knowledge and practical implementation in python code of two most popular clustering algorithms – k-means and Hierarchical clustering.
This second part tries to maintain a fine balance between necessary theoretical knowledge needed by a data scientist and practical implementation details using python programming language. The third part of the course consists of practice problem sets where students can put into practice their understanding of python and clustering.
- No prior knowledge of python is required for this course as the first part of the course deals with the basics of python in enough details
What will you learn?
Part I- Python Basics:
- Installation and Environments
- Variable, Identifier, keywords, and Operators
- Control Statements- conditionals, loops
- Data Structure-List, Tuple, Set, Dictionary
- Data Manipulation using Pandas library
- Arrays, Linear Algebra, Summary statistics using Numpy library
- Data visualization using Matplotlib and Seaborn
Part II – Clustering:
- Understanding Machine Learning, supervised and unsupervised learning
- Clustering definition and concept
- K-means clustering
- Hierarchical clustering
Part III – Practice Problem sets
To enroll in this course, click the link below.
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