Learning Path: Python And Machine Learning Foundation

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, you’ll 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.

Sanjin Dedic

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

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.

This Learning Path is great for anyone who wants to start using Python to build anything from simple command-line programs to web applications. It is also designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. Prior knowledge of Python isn't required

Learn to use control statements

Understand how to use loops to iterate over objects or data for accurate results

Write encapsulated and succinct Python functions

Build Python classes using object-oriented programming

Manipulate files on the file system (open, read, write, and delete)

Gain insight into the difference between supervised and unsupervised models

Study popular algorithms, such as K-means, Gaussian Mixture, Birch, Naïve-Bayes, Decision Tree, and SVM

Visualize errors in various models using Matplotlib