This is the code repository for Training Your Systems with Python Statistical Modeling [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Training Your Systems with Python Statistical Learning aims to give viewers a working knowledge of machine learning and statistical analysis using Python and its packages.
The course focuses on using Scikit-Learn, statsmodels, and scipy for statistical learning, while using NumPy and Pandas for storing and managing data and matplotlib for visualization. The course is light on theory and heavy on examples, with concepts being introduced then demonstrated with code and data examples.
At the end of the course, viewers should have a working knowledge of machine learning models and concepts and how to implement these models in Python.
- Find correlations in your data using SciPy
- Train different machine learning models and evaluate their results
- Make predictions using Naïve Bayes Algorithm with the help of Python code
- Employ support vector machines for classification and detection
- Employ ridge and lasso regression models
- Train a neural network
To fully benefit from the coverage included in this course, you will need:
To fully benefit from the coverage included in this course, you will need:
Prior working knowledge of the Python 3.6.x language Working knowledge of Anaconda and Jupyter notebooks Working knowledge of Pandas and NumPy
This course has the following software requirements:
This course has the following software requirements:
A web browser pandas NumPy matplotlib Python statsmodels scikit-learn scipy nltk Anaconda
This course has been tested on the following system configuration:
OS: Windows 10 Processor: Intel Core i7 @ 2.4
GHz, 64-bit Memory: 16GB Hard Disk Space: 1.5TB Python 3.6.x NumPy 1.13.3 pandas 0.21.0 matplotlib 2.0.2 nltk 3.2.2 scikit-learn 0.19.1 statsmodels 0.8.0 conda 4.3.30