DATA SCIENCE AND MACHINE LEARNING IN PYTHON

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Course Synopsis

This course is targeted for beginners who want to learn how to think and write meaningful pieces of code or read codes written by someone else in Python. This course teaches how to map literary description of a problem (requirement) to an application/library coded in Python. This is a core basic level course that is essential for anyone who has no prior programming experience but wishes to be a professional Python engineer in future.

Required Textbooks 

1. Chris Albon, “Machine Learning with Python Cookbook”, O’Reilly Media Inc. US.
2. LazyProgrammer, “Deep Learning in Python”, LazyProgrammer, US.
3. Oliver Theobald, “Machine Learning for Absolute Beginners”, Scatterplot Press.

Completion Criteria

After fulfilling all of the following criteria, the student will be deemed to have finished the Module: 

1. Has attended 90% of all classes held.
2. Has received an average grade of 80% on all assignment. 
3. Has received an average of 60% in assessments.
4. The tutor believes the student has grasped all of the concepts and is ready to go on to the next module.

Prerequisites

1. Basic knowledge about programming, bits/bytes, procedures, classes, computer architecture, etc. If you just have theoretical knowledge that is perfectly okay but you should have strong convictions on what programming is, and what you hope to achieve from this class.
2. Willing and eager to spend at least 10-20 hours (Varying from student-to-student) per week outside of the training class to self-study and practice.
3. There is no prior educational level requirement for this course. Anyone from 10+2 student to someone who is doing their PHD in Genetic Engineering is welcome to take this course.
4. If you are only interested in theory and have no interest/patience in spending at least 10 hours every week throughout the duration of the course, then this course might not be for you.