Outlier Detection in Python

In the world of data science, true insight often comes not just from understanding patterns, but from identifying the exceptions—the outliers. These unusual data points can be the most informative, revealing critical issues, novel opportunities, hidden fraud, or valuable insights. This exclusive bundle brings together Brett Kennedy’s acclaimed book and comprehensive video course, “Outlier Detection in Python,” to provide you with a complete and practical toolkit to master this essential skill.

What You Get in This Comprehensive Bundle:

1. “Outlier Detection in Python” – The Book: A practical, in-depth guide to spotting the parts of your data that deviate from the norm. You’ll explore a comprehensive set of statistical methods and machine learning approaches. (Purchase includes a free eBook in PDF and ePub formats from Manning Publications).
2. “Outlier Detection in Python, Video Edition” – The Course: Over 19 hours and 35 minutes of expert-led video instruction by Brett Kennedy. This O’Reilly course brings concepts to life, demonstrating how to apply techniques in real-world scenarios.

Why This Bundle is Your Pathway to Mastery:

This unique combination offers an unparalleled learning experience. Dive deep into the principles and practices with the expertly written book, and then see those concepts visually demonstrated and explained in the engaging video course. This dual approach caters to different learning styles, helps reinforce complex topics, and ensures you can move from theory to practical application with confidence. Whether you’re a beginner or looking to advance your skills, this bundle covers the spectrum.

What You Will Learn and Achieve:

• Comprehensive Understanding: Grasp the core concepts, from definitions and types of outliers (local, global, collective) to their critical role in machine learning and data analysis.
• Practical Toolkit Mastery: Become proficient with standard Python libraries (like pandas and NumPy) and specialized outlier detection tools such as scikit-learn (Isolation Forest, Local Outlier Factor, One-Class SVM), PyOD (Histogram-based Outlier Score, ECOD, COPOD, Angle-based Outlier Detection), alibi-detect, and PyCaret.
• Versatile Data Handling: Confidently identify and interpret anomalies across a wide array of data types, including numeric, categorical, tabular, text, time series, and even image data.
• End-to-End Outlier Detection Process: Master the complete lifecycle: from defining the types of outliers you’re interested in and collecting/cleaning data, to feature selection/engineering, choosing and fitting models (including various machine learning and deep learning approaches), evaluating model performance, and setting up ongoing outlier detection systems.
• Improved Accuracy and Results: Learn techniques to combine multiple outlier detection methods, build powerful ensembles, and effectively scale and combine scores for superior accuracy.
• Effective Interpretation and Action: Understand how to interpret your results, delve into explainable outlier detection to understand why a point is an outlier, and learn best practices for working with outlier detection predictions.
• Real-World Application & Problem Solving: Apply your knowledge to diverse domains such as finance (fraud detection), social media (spotting bot activity), network security (identifying intrusions), and general data quality assessment.
• Advanced Insights & Techniques: Explore advanced topics including handling very large or very small datasets, generating synthetic data for robust testing, identifying collective outliers, and leveraging cutting-edge deep learning-based detection methods.

Who Is This Bundle For?

• Python Programmers: Especially those familiar with data manipulation tools like pandas and NumPy.
• Data Scientists & Analysts: Seeking to deepen their expertise in anomaly and outlier detection techniques.
• Aspiring Data Professionals: Individuals with a basic understanding of statistics who are ready to learn a high-demand, valuable skill.
• Anyone Interested in Data Anomalies: Professionals and enthusiasts eager to understand how to find, interpret, and act upon unusual data.

About the Author & Instructor:

Learn from the best with Brett Kennedy, a seasoned data scientist with over thirty years of experience in software development and data science. Brett brings his wealth of practical knowledge and clear teaching style to both the book and the video course, ensuring you gain skills that are directly applicable to real-world challenges.

Elevate your data analysis capabilities and turn anomalies into actionable intelligence. This comprehensive bundle is your definitive resource for mastering outlier detection in Python, empowering you to uncover the hidden stories within your data.