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[FreeCoursesOnline.Me] PentesterAcademy - Data Science And Machine Learning For Infosec - [FCO]
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!
About Author :
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.
For More Udemy Free Courses >>> http://www.freetutorials.eu For more Lynda and other Courses >>> https://www.freecoursesonline.me/ Our Forum for discussion >>> https://discuss.freetutorials.eu/
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FILE LIST
Filename
Size
1 - Data Science and Machine Learning for Infosec- COURSE INTRODUCTION.MP4
15.7 MB
1 - Lab Setup and Installation.mp4
118.3 MB
1.1 - Module 1- Pandas Part 1.mp4
491.4 MB
1.2 - Module 1- Pandas Part 2 .mp4
543.9 MB
1.3 - Module 1- Pandas Part 3 .mp4
865.5 MB
1.4 - Module 1 Pandas Part 4 .mp4
599.7 MB
10.1 - Module 10- Dimension Reduction Part 1.mp4
353.8 MB
10.2 - Module 10- Dimension Reduction Part 2.mp4
395.3 MB
10.3 - Module 10- Dimension Reduction Part 3.mp4
687.9 MB
11.1 - Module 11- Clustering Part 1.mp4
560.7 MB
11.2 - Module 11- Clustering Part 2.mp4
366.9 MB
11.3 - Module 11- Clustering Part 3.mp4
330.3 MB
12.1 - Module 12- Stochastic Gradient Descent Part 1.mp4
438.7 MB
12.2 - Module 12- Stochastic Gradient Descent Part 2 .mp4
377.6 MB
12.3 - Module 12- Stochastic Gradient Descent Part 3.mp4
655 MB
13.1 - Module 13- Neural Networks - Deep Learning Part 1.mp4
336.3 MB
13.2 - Module 13- Neural Networks - Deep Learning Part 2.mp4
314.4 MB
13.3 - Module 13- Neural Networks - Deep Learning Part 3.mp4
719.3 MB
14.1 - Module 14- Recommendations Engine Part 1.mp4
497.6 MB
14.2 - Module 14- Recommendations Engine Part 2.mp4
386 MB
2.1 - Module 2- K Nearest Neighbors (KNN) Part 1 .mp4
407.2 MB
2.2 - Module 2- K Nearest Neighbors (KNN) Part 2.mp4
398 MB
2.3 - Module 2- K Nearest Neighbors (KNN) Part 3.mp4
519 MB
2.4 - Module 2- K Nearest Neighbors (KNN) Part 4.mp4
308 MB
3.1 - Module 3- Model Evaluation and Linear Regression Part 1.mp4
294.5 MB
3.2 - Module 3- Model Evaluation and Linear Regression Part 2.mp4
424.5 MB
3.3 - Module 3- Model Evaluation and Linear Regression Part 3.mp4
313.5 MB
3.4 - Module 3- Model Evaluation and Linear Regression Part 4.mp4
422.8 MB
4.1 - Module 4- Logistic Regression Part 1 .mp4
283.9 MB
4.2 - Module 4- Logistic Regression Part 2.mp4
199.9 MB
4.3 - Module 4- Logistic Regression Part 3.mp4
289.5 MB
4.4 - Module 4- Logistic Regression Part 4 .mp4
344.1 MB
5.1 - Module 5- Natural Language Processing Part 1.mp4
397.7 MB
5.2 - Module 5- Natural Language Processing Part 2.mp4
305.3 MB
5.3 - Module 5- Natural Language Processing Part 3.mp4
290.9 MB
5.4 - Module 5- Natural Language Processing Part 4.mp4
381 MB
6.1 - Module 6- Naive Bayes Classification Part 1.mp4
212.4 MB
6.2 - Module 6- Naive Bayes Classification Part 2.mp4
293.4 MB
6.3 - Module 6- Naive Bayes Classification Part 3.mp4
250.8 MB
6.4 - Module 6- Naive Bayes Classification Part 4.mp4
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