This is the code repository for Building Predictive Models with Machine Learning and Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Learn to build predictive models with Python, the core of a Data Scientist's toolkit. This course will guide you through using the Python ecosystem to get results in a matter of hours, or with practice, in minutes. The best way to learn is with examples. This course will guide you through all the steps needed to train and test your models.
- Make each stage in building a Machine Learning based model easy and fast.
- Write and run your code inside Jupyter Notebooks to make sharing, debugging, and iterating on your code an absolute breeze.
- Read, explore, clean, and prepare your data using Pandas, the most popular library for analyzing data tables.
- Use the Scikit-Learn library to deploy ready-built models, train them, and see results in just a few lines of code.
- Evaluate your models to ensure they can be trusted!
- Cardinal rules you must follow to obtain a valid model you can rely on in the real world.
- Use hyper-parameter optimization to get the best possible version of each model for your specific application.
To fully benefit from the coverage included in this course, you will need:
This course is aimed at developers who want to get started with Machine Learning in Python. Developers who are curious about deploying Machine Learning-based models will find that this course will guide them to understand why some models are better than others at tackling certain challenges. Some knowledge of mathematics and Python is assumed.
This course has the following software requirements:
SETUP AND INSTALLATION
This will vary on a product-by-product basis, but should be a standard PI element for ILT products. This example is relatively basic.
Minimum Hardware Requirements For successful completion of this course, students will require the computer systems with at least the following:
OS: 64-bit operating system, such as Windows 7-10, macOS or Linux.
Processor: Two core cpu - i3 or equivalent.
Memory: 4GB of RAM.
Storage: 10GB.
Recommended Hardware Requirements For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: macOSX recommended.
Processor: 4 core cpu - i5 or better. Makes the code run much faster!
Memory: 8GB of RAM
Storage: 10GB
Software Requirements
Operating system: macOS recommended.
Browser: Chrome is recommended.
Anaconda (Python 3.6+ version) - Download from https://www.anaconda.com/download