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.

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Table of Contents (9 chapters)
Preface
About the Book
1
1. Fundamentals
1. Fundamentals
Introduction
Data Quality Considerations
Summary
Free Chapter
2
2. Exploratory Data Analysis and Visualization
2. Exploratory Data Analysis and Visualization
Introduction
Exploratory Data Analysis (EDA)
Summary Statistics and Central Values
Missing Values
Distribution of Values
Relationships within the Data
Summary
3
3. Linear Regression
3. Linear Regression
Introduction
Regression and Classification Problems
Linear Regression
Multiple Linear Regression
Summary
4
4. Autoregression
4. Autoregression
Introduction
Autoregression Models
Summary
5
5. Classification Techniques
5. Classification Techniques
Introduction
Ordinary Least Squares as a Classifier
Logistic Regression
Classification Using K-Nearest Neighbors
Classification Using Decision Trees
Artificial Neural Networks
Summary
6
6. Ensemble Modeling
6. Ensemble Modeling
Introduction
One-Hot Encoding
Overfitting and Underfitting
Bagging
Bootstrapping
Boosting
Stacking
Summary
7
7. Model Evaluation
7. Model Evaluation
Introduction
Importing the Modules and Preparing Our Dataset
Evaluation Metrics
Splitting a Dataset
Performance Improvement Tactics
Summary
Appendix
1. Fundamentals
2. Exploratory Data Analysis and Visualization
3. Linear Regression
4. Autoregression
5. Classification Techniques
6. Ensemble Modeling
7. Model Evaluation

Summary

In this chapter, we introduced the concept of supervised machine learning, along with a number of use cases, including the automation of manual tasks such as identifying hairstyles from the 1960s and 1980s. In this introduction, we encountered the concept of labeled datasets and the process of mapping one information set (the input data or features) to the corresponding labels. We took a practical approach to the process of loading and cleaning data using Jupyter notebooks and the extremely powerful pandas library. Note that this chapter has only covered a small fraction of the functionality within pandas, and that an entire book could be dedicated to the library itself. It is recommended that you become familiar with reading the pandas documentation and continue to develop your pandas skills through practice. The final section of this chapter covered a number of data quality issues that need to be considered to develop a high-performing supervised learning model, including missing data, class imbalance, and low sample sizes. We discussed a number of options for managing such issues and emphasized the importance of checking these mitigations against the performance of the model. In the next chapter, we will extend the data cleaning process that we covered and investigate the data exploration and visualization process. Data exploration is a critical aspect of any machine learning solution since without a comprehensive knowledge of the dataset, it would be almost impossible to model the information provided.

Previous Section
End of Chapter 1
Next Chapter