Course details
This Learning Path takes you from zero experience to a complete understanding of key concepts, edge cases, and using Python for real-world application development. After a brief history of Python and key differences between Python 2 and Python 3, you'll understand how Python has been used in applications such as YouTube and Google App Engine. As you work with the language, you'll learn about control statements, delve into controlling program flow and gradually work on more structured programs via functions.
You'll learn about data structures and study ways to correctly store and represent information. By working through specific examples, you'll learn how Python implements object-oriented programming (OOP) concepts of abstraction, encapsulation of data, inheritance, and polymorphism. You'll be given an overview of how imports, modules, and packages work in Python, how you can handle errors to prevent apps from crashing, as well as file manipulation.
Next, youll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithm over 1990 US Census dataset, to discover patterns and profiles, and explore the process to solve a supervised machine learning problem. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package.
By the end of this Learning Path, you'll have built up an impressive portfolio of projects and armed yourself with the skills you need to tackle Python projects in the real world.
About the Authors
Sanjin Dedic is a robotics engineer. He has worked for 5 years as a product development engineer and for the past 7 years, he has been teaching digital technologies and systems engineering. He has extensive classroom experience in teaching computational thinking and the foundational skills in platforms such as Scratch, Arduino, Python, Raspberry Pi, and Lego Mindstorms.
Samik Sen is currently working with R on machine learning. He has done his PhD in Theoretical Physics. He has tutored classes for high performance computing postgraduates and lecturer at international conferences. He has experience of using Perl on data, producing plots with gnuplot for visualization and latex to produce reports. He, then, moved to finance/football and online education with videos.
Updated on 11 March, 2020- USD 272
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