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Combine Python with machine learning principles to discover hidden patterns in raw data

License: MIT License

Jupyter Notebook 98.70% Python 1.30%

data-science-with-python's Introduction

Data Science with Python

Data Science with Python will help you get comfortable with using the Python environment for data science. You will learn all the libraries that a data scientist uses on a daily basis. By the end of this book, you will be able to take a large raw dataset, clean it, manipulate it, and run machine learning algorithms to obtain results that influence business decisions.

Data Science with Python by Rohan Chopra, Aaron England and Mohamed Noordeen

What you will learn

Pre-process data to make it ready to use for machine learning
Create data visualizations with Matplotlib
Use scikit-learn to perform dimension reduction using principal component analysis (PCA)
Solve classification and regression problems
Get predictions using the XGBoost library
Process images and create machine learning models to decode them
Process human language for prediction and classification
Use TensorBoard to monitor training metrics in real time
Find the best hyperparameters for your model with AutoML

Hardware Requirements

For an optimal student experience, we recommend the following hardware configuration:

Processor: Intel Core i5 or equivalent
Memory: 4GB RAM (8 GB Preferred)
Storage: 15 GB available hard disk space
Internet connection

Software Requirements

You'll also need the following software installed in advance:

OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
Browser: Google Chrome/Mozilla Firefox Latest Version
Notepad++/Sublime Text as IDE (optional, as you can practice everything using Jupyter Notebook in your browser)
Python 3.4+ (the latest version is Python 3.7) installed (https://python.org)
Anaconda (https://www.anaconda.com/distribution/)