Data Science and Machine Learning for Infosec

The age of intelligent machines is here! We are now seeing Machine Learning disrupting every technological field including computer security. As more and more security products use Machine Learning, it is important as Pentesters and Security Researchers to understand how to make and break this technology!

Data Science and Machine Learning for Infosec Course Videos



1
Course Introduction

Course Introduction  



2
Lab Setup and Installation

Lab Setup and Installation  



3
Module 1: Pandas Part 1

Module 1: Pandas Part 1  



4
Module 1: Pandas Part 2

Module 1: Pandas Part 2  



5
Module 1: Pandas Part 3

Module 1: Pandas Part 3  



6
Module 1: Pandas Part 4

Module 1: Pandas Part 4  



7
Module 2: K Nearest Neighbors (KNN) Part 1

Module 2: K Nearest Neighbors (KNN) Part 1  



8
Module 2: K Nearest Neighbors (KNN) Part 2

Module 2: K Nearest Neighbors (KNN) Part 2  



9
Module 2: K Nearest Neighbors (KNN) Part 3

Module 2: K Nearest Neighbors (KNN) Part 3  



10
Module 2: K Nearest Neighbors (KNN) Part 4

Module 2: K Nearest Neighbors (KNN) Part 4  



11
Module 3: Model Evaluation and Linear Regression Part 1

Module 3: Model Evaluation and Linear Regression Part 1  



12
Module 3: Model Evaluation and Linear Regression Part 2

Module 3: Model Evaluation and Linear Regression Part 2  



13
Module 3: Model Evaluation and Linear Regression Part 3

Module 3: Model Evaluation and Linear Regression Part 3  



14
Module 3: Model Evaluation and Linear Regression Part 4

Module 3: Model Evaluation and Linear Regression Part 4  



15
Module 4: Logistic Regression Part 1

Module 4: Logistic Regression Part 1  



16
Module 4: Logistic Regression Part 2

Module 4: Logistic Regression Part 2  



17
Module 4: Logistic Regression Part 3

Module 4: Logistic Regression Part 3  



18
Module 4: Logistic Regression Part 4

Module 4: Logistic Regression Part 4  



19
Module 5: Natural Language Processing Part 1

Module 5: Natural Language Processing Part 1  



20
Module 5: Natural Language Processing Part 2

Module 5: Natural Language Processing Part 2  



21
Module 5: Natural Language Processing Part 3

Module 5: Natural Language Processing Part 3  



22
Module 5: Natural Language Processing Part 4

Module 5: Natural Language Processing Part 4  



23
Module 6: Naive Bayes Classification Part 1

Module 6: Naive Bayes Classification Part 1  



24
Module 6: Naive Bayes Classification Part 2

Module 6: Naive Bayes Classification Part 2  



25
Module 6: Naive Bayes Classification Part 3

Module 6: Naive Bayes Classification Part 3  



26
Module 6: Naive Bayes Classification Part 4

Module 6: Naive Bayes Classification Part 4  



27
Module 7: Advanced Scikit Learn Part 1

Module 7: Advanced Scikit Learn Part 1  



28
Module 7: Advanced Scikit Learn Part 2

Module 7: Advanced Scikit Learn Part 2  



29
Module 8: Decision Trees Part 1

Module 8: Decision Trees Part 1  



30
Module 8: Decision Trees Part 2

Module 8: Decision Trees Part 2  



31
Module 8: Decision Trees Part 3

Module 8: Decision Trees Part 3  



32
Module 9: Ensembling Techniques Part 1

Module 9: Ensembling Techniques Part 1  



33
Module 9: Ensembling Techniques Part 2

Module 9: Ensembling Techniques Part 2  



34
Module 9: Ensembling Techniques Part 3

Module 9: Ensembling Techniques Part 3  



35
Module 9: Ensembling Techniques Part 4

Module 9: Ensembling Techniques Part 4  



36
Module 10: Dimension Reduction Part 1

Module 10: Dimension Reduction Part 1  



37
Module 10: Dimension Reduction Part 2

Module 10: Dimension Reduction Part 2  



38
Module 10: Dimension Reduction Part 3

Module 10: Dimension Reduction Part 3  



39
Module 11: Clustering Part 1

Module 11: Clustering Part 1  



40
Module 11: Clustering Part 2

Module 11: Clustering Part 2  



41
Module 11: Clustering Part 3

Module 11: Clustering Part 3  



42
Module 12: Stochastic Gradient Descent Part 1

Module 12: Stochastic Gradient Descent Part 1  



43
Module 12: Stochastic Gradient Descent Part 2

Module 12: Stochastic Gradient Descent Part 2  



44
Module 12: Stochastic Gradient Descent Part 3

Module 12: Stochastic Gradient Descent Part 3  



45
Module 13: Neural Networks - Deep Learning Part 1

Module 13: Neural Networks - Deep Learning Part 1  



46
Module 13: Neural Networks - Deep Learning Part 2

Module 13: Neural Networks - Deep Learning Part 2  



47
Module 13: Neural Networks - Deep Learning Part 3

Module 13: Neural Networks - Deep Learning Part 3  



48
Module 14: Recommendations Engine Part 1

Module 14: Recommendations Engine Part 1  



49
Module 14: Recommendations Engine Part 2

Module 14: Recommendations Engine Part 2  



50
Case Study: Detecting Malicious URLs

Case Study: Detecting Malicious URLs  

Sinan Ozdemir

Sinan Ozdemir is a Data Scientist and Machine Learning expert from San Francisco with a Masters in Theoretical Mathematics from Johns Hopkins University where he served as a lecturer of Mathematics, Statistics and Computer Science for sometime. He is author of the book "Principles of Data Science" and the course "Data Science for Infosec Professionals" for Pentester Academy (2017 release). He is also currently the co-Founder/CTO of Legion Analytics, a machine learning startup. Sinan's passion for data and education has followed him all over the country where he has taught in several prison systems, conferences and universities.