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.
Advanced JavaScript by Rohan Chopra, Aaron England and Mohamed Noordeen
- 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
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
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/)
You can download the dataset for the following lessons from the respective URL:
Lesson 06, Lesson 07 and Lesson 08: https://github.com/PacktPublishing/Datasets-of-Master-Data-Science-with-Python Lesson 06 and Lesson 08 use the same dataset