Book Image

The Supervised Learning Workshop - Second Edition

By : Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur
Book Image

The Supervised Learning Workshop - Second Edition

By: Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Overview of this book

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.
Table of Contents (9 chapters)

Autoregression Models

Autoregression models are classical or "standard" modeling methods used on time series data (that is, any dataset that changes with time) and can complement the linear regression techniques covered previously. Autoregression models are often used for forecasting in the economics and finance industry as they are useful with univariate time series (where there are no x variables other than time) and with very large datasets (such as streaming data or high-frequency sensor data) where the linear algebra operations might run into memory or performance issues on very large datasets. The "auto" part of autoregression refers to the fact that these models leverage correlation of a time series to itself in the past, hence autoregression. In addition, many systems do not have an associated causal model—the time series data is said to be stochastic. An example is stock price data over time. Although many attempts have been made, and continue to...