Build a Spam Detector AI with Text Classification

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Online

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Description

  • Type

    Course

  • Methodology

    Online

  • Start date

    Different dates available

In this course we’ll use Python 3 to create an Artificial Intelligence (AI) that can determine when an incoming email is spam or not. The technique we’ll use to create this cool project is called Text Classification. The group of algorithms that we’ll cover and use is Naive Bayes. Using term frequency and inverse document frequency we’ll be able to tweak our AI for improved accuracy.


To build our AI we’ll use the publicly available Enron dataset.

Frameworks and tools covered: Python 2.7 (updated for Python 3.6), Anaconda 5.2, Enron Dataset, NumPy 1.13, SciPy 0.19

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Different dates availableEnrolment now open

About this course

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Intermediate knowledge of Python 3
Intermediate knowledge of using NumPy
Intermediate knowledge of machine learning

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This centre's achievements

2021

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 4 years

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Course programme

Introduction 2:49


Introduction 2:49



2:49

Development Environment - Anaconda 8:04


Development Environment - Anaconda 8:04



8:04

M1-1 Text Classification 10:03


M1-1 Text Classification 10:03



10:03

M1-2 Naive Bayes 1 10:18


M1-2 Naive Bayes 1 10:18



10:18

M1-3 Naive Bayes 2 10:59


M1-3 Naive Bayes 2 10:59



10:59

M1-4 Naive Bayes 3 12:18


M1-4 Naive Bayes 3 12:18



12:18

M1-5 Numerical Representations of Text 10:53


M1-5 Numerical Representations of Text 10:53



10:53

M1-6 tf-idf 10:34


M1-6 tf-idf 10:34



10:34

M2-0 Downloading the Dataset


M2-0 Downloading the Dataset



M2-1 Intro to Enron Dataset 9:35


M2-1 Intro to Enron Dataset 9:35



9:35

M2-2 Intro to Provided Code 9:31


M2-2 Intro to Provided Code 9:31



9:31

M2-3 Count Vectorizer 10:00


M2-3 Count Vectorizer 10:00



10:00

M2-4 tf-idf 9:29


M2-4 tf-idf 9:29



9:29

M2-5 Naive Bayes Classifier 12:43


M2-5 Naive Bayes Classifier 12:43



12:43

M2-6 Pipelining 11:40


M2-6 Pipelining 11:40



11:40

Conclusion 2:21


Conclusion 2:21



2:21

Additional information

On-demand, 24/7 access 2.4 hours of video Certificate of completion Source code and PDF notes Closed captions

Build a Spam Detector AI with Text Classification

Price on request