An Introduction to Machine Learning with Cluster Analysis using Python
- Self-paced with Life Time Access
- Certificate on Completion
- Access on Android and iOS App
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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
Part I- Python Basics:
- Installation and Environments
- Variable, Identifier, keywords, and Operators
- Control Statements- conditionals, loops
- Functions
- Modules
- 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
How to install python for different operating systems and brief explanation of different developments environments to be used in the course.
Conceptual understanding of variable in python. Acceptable rules of Identifier. Keyword list. Operators and Operator precedence.
concepts and application examples of Variable, Identifier, Keyword and operators
Brief explanation of different control statements in python. Application examples of Conditionals.
Brief explanation and examples of concepts of machine learning, supervised and unsupervised learning
These problem sets are given so that participants can put into practice their understanding of python programming language and knowledge of clustering in practical problem situations.
Explanation of Clustering concept and K-Means clustering Algorithm
Both theoretical explanation and practical session of hierarchical clustering
This section contains practice problem sets, data sets and solution sets. The aim is to provide an opportunity to the participants to put into practice their understanding of python programming language and knowledge of cluster analysis into practical scenarios.
I have a fascination with the data science field. I started learning about it from books. But, I needed some direct and authentic teaching. I reviewed some YouTube videos, but it was difficult for me to choose something worthwhile out of plethora of materials available on the internet. Joining this course has given me focus and I can feel that I am learning from somebody who has more experience than me as far as the data science field is concerned. I had some apprehension about the course before joining it, but now I feel happy that I have taken the right decision. Only, the difficulty I faced that I wanted to make payment in Euro but ultimately I had to convert Euro to US dollar to make payment. I will request the site to accept the payment in the country’s own currency.
I am Sukanta Bala a Microsoft Employee (Hyderabad, India) looking for Cluster Analysis courses for career enhancement and I've found the perfect one. I had prior knowledge of python. So, I enjoyed solving quizzes and problem sets at the beginning. The presentation of basics of python programming language is very lucid and is likely to be useful for newbies. I particularly enjoyed the hierarchical clustering of movies. As this course covers two most used clustering algorithms, I feel the course is worth my money. I highly recommend people to go for it.
