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Home » Technology » Big Data & Data Science » Machine Learning to Detect Phishing Websites

Course Skill Level:

Foundational

Course Duration:

1 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    MLDPWSL21E09

Who should attend & recommended skills:

Developers & beginning data scientists with basic Python, pandas, NumPy, scikit-learn, & data science skills

Who should attend & recommended skills

  • This course is geared for developers interested in data science and for beginner data scientists who want to develop a machine learning model for predicting whether or not an email that contains a link to a website is a phishing website or not.
  • Skill-level: Foundation-level Use Machine Learning to Detect Phishing Websites skills for Intermediate skilled team members. This is not a basic class.
  • Python and its utility functions: Basic (1-2 years’ experience)
  • pandas: Basic (1-2 years’ experience)
  • NumPy: Basic (1-2 years’ experience)
  • scikit-learn: Basic (1-2 years’ experience)
  • Data Science: Basic (1-2 years’ experience)

About this course

In this course, you will be filling in the role of a data scientist employed by an organizations cybersecurity manager. Lately, the employees of the organization are receiving a lot of emails containing links to phishing websites. Your task will be to develop a machine learning model for predicting whether or not an email that contains a link to a website is a phishing website or not. Phishing attacks are considered to be one of the most common types of online security threats, and are capable of breaking into an organizations online security so as to extract confidential information like user passwords, financial information, and so on. The Internet Crime Report 2018 presents the effects of phishing websites.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:
  • Loading and understanding a tabular dataset. As a data scientist, you should be comfortable working with tabular data.
  • Querying the dataset for deriving interesting reports.
  • Cleaning the dataset accordingly so that it is well-suited for a machine learning model.
  • Building and training machine learning models, like Logistic Regression and Neural Networks.
  • Performing hyperparameter tuning techniques, like random search.
  • Providing a summary of the performance of the machine learning models.
  • Loading and understanding the Phishing Websites dataset
  • Asking the right questions of the data to understand it better and preparing reports
  • Creating a Logistic Regression classifier as a baseline model
  • Analyzing the results and using random searches to find the optimal hyperparameters of the baseline model
  • Summarizing the results of your findings

Course breakdown / modules

  • Knowing the Dataset
  • A Quick Tour of Pandas
  • Submit Your Work

  • Getting Useful Information from the Dataset
  • Submit Your Work

  • Cleaning the Class Labels and Inspecting for Missing Values
  • Submit Your Work

  • Training a Logistic Regression Model
  • A Quick Primer on Logistic Regression
  • A Brief Take on Scikit-Learn
  • A Continuous Approach to Splitting Points: Logistic Regression
  • Submit Your Work

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