Python

Practical Anomaly Detection using Python and scikit-learn Introduction Anomaly detection is a critical task in various domains, including finance, healthcare, and cybersecurity. It involves identifying data points, events, or patterns that deviate from the norm within a given dataset. In this article, we will explore how to build an anomaly detection system using Python and scikit-learn. Prerequisites To follow this article, you should have: Familiarity with Python and basic data structures (e.g., lists, dictionaries) Understanding of basic machine learning concepts (e.g., supervised vs. unsupervised learning) Installations: Python, scikit-learn, and relevant libraries (e.g., NumPy, Pandas) Main Sections 1. Data Preparation and Preprocessing Data preparation is a crucial step in anomaly detection. It involves cleaning, transforming, and normalizing the data to make it suitable for analysis. ...

Detecting Anomalies with Machine Learning and Python Introduction Anomaly detection is a critical task in data analysis, enabling the identification of suspicious transactions, credit card inconsistencies, and irregularities in medical records. In this post, we will delve into the practical implementation of anomaly detection using machine learning in Python, focusing on real-world security applications and challenges. Prerequisites To follow along with this tutorial, you will need: A basic understanding of Python and machine learning concepts (e.g., supervised and unsupervised learning) Familiarity with popular Python libraries for machine learning (e.g., scikit-learn, TensorFlow) Access to a Python environment for code execution Preparing the Data Before training a machine learning model, we need to prepare our dataset. This includes selecting relevant data, handling missing values, and scaling numerical features. ...

Understanding and Addressing AI Bias in Machine Learning Models Introduction Artificial intelligence (AI) has revolutionized various industries, from healthcare to finance, with its ability to analyze vast amounts of data and make informed decisions. However, AI systems can perpetuate existing biases present in the data, leading to unfair outcomes and discrimination. In this comprehensive guide, we will explore the concept of AI bias, its impact on machine learning models, and strategies for identifying and mitigating bias in AI systems. ...

As I continue this series on CI/CD pipeline security, it is time to now work on securely building and deploying our application. This post picks up where my Build Secure Python Pipelines: Adding Tests and Hooks in Action post left off. In this post, we’ll continue our pipeline development by adding a container build and deployment to Kubernetes. In addition to this, we’ll add some security components to the build and deployment process. ...

As we continue this series started in my Getting Started with Secure CI/CD: Essential Practices for Beginners post, I’ll be securing my Python code with automated testing and hooks. While some of this information builds on some previous posts I’ve created in the past, Adding pre-commit Hooks to Python Repo Writing Tests For Your Python Project I still wanted to incorporate these together in a meaningful way. My goal is to help anyone that is trying to figure out how to piece together their own pipeline. ...