Learn Python in 3 Hours[Video], published by Packt
This is the code repository for Learn Python in 3 Hours [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Learn Python in 3 Hours is a fast-paced, action-packed course that maximizes your time. It's designed from the ground up to bring you from zero to hero in the shortest time. The course is based on many years of Python development experience in both large enterprises and nimble startups.
- Set up your own development environment on Windows to create Python applications
- Use special Python features to speed up list comprehensions and small functions
- Leverage the wide range of pre-made packages on PyPI
- Manage different projects with a myriad of dependencies
- Use classes and create objects with OOP using Python
- Use special Python techniques such as decorators and context managers
- Perform data science using scikit-learn, pandas, and matplotlib
To fully benefit from the coverage included in this course, you will need:
This course is for programmers at all experience levels who would like to transition into developing using Python. Prior programming experience is assumed
This course has the following software requirements:
Minimum Hardware Requirements
For successful completion of this course, students will require the computer systems with at least the following:
OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
Processor: Intel Core i5 or equivalent
Memory: 8 GB RAM
Storage: 35 GB available space
Recommended Hardware Requirements For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
Processor: Intel Core i7 or equivalent
Memory: 48 GB RAM
Storage: 35 GB available space
Software Requirements
OS: Windows 7 or Windows 10
Browser: Google Chrome, Latest Version
Code Editor: Atom IDE, Latest Version
Others: Python3 installed using the Anaconda package or equivalent, Tensorflow r1.4
Exercise Files
Exercise files should have a start and an end state for each video that contains a demonstration of code.