Forecasting and Analysis techniques with Python
Linear regression, nonlinear regression, auto-regressive models, time series analysis, regularisation and more.
Next Date: 18 Mar 2017
London and Cambridge
two days Course
What you will learn
You will learn the skills you need to develop and evaluate regression models that allow you to make quantitative predictions.
- Linear regression
- Nonlinear regression
- Auto-regressive models
- Time series analysis
- Regularisation (lasso and ridge)
Languages and libraries
- Python programming language
- numpy and pandas for data manipulation
- scikit-learn for machine learning algorithms
- plotly for interactive visualisations
Learn state-of-the art machine learning techniques at our Machine Learning Techniques using Python bootcamp.
Acquire specialised Natural Language Processing skills at our Text Mining and Natural Language Processing with Python bootcamp.
Prerequisites: Good knowledge of python, some familiarity with matrices, basic understanding of machine learning practice (as taught in Introduction to Data Science)
There are seven modules in the Regression Analysis with Python workshop. Each module focuses on popular languages, libraries and technologies for working with large data sets and applying analysis techniques.
10:00Identify example applications of regression analysis in the real world.
10:30Learn how to pre-process data with pandas and Scikit-Learn, and how to plot graphs using matplotlib.
11:15Develop, train and test linear regression models.
12:00Develop, train and test non-linear and multiple regression models.
14:00Use cross-validation to evaluate and tune models.
15:00Use regularisation to avoid over-fitting, including lasso and ridge methods.
16:00Use auto-regression to predict the future.