[ ] Udemy - An Introduction to Intrusion Detection Using ML And Python


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Torrent File Content (39 files)


[ DevCourseWeb.com ] Udemy - An Introduction to Intrusion Detection Using ML And Python
     Get Bonus Downloads Here.url -
182 bytes

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          1. The theoretical Concepts/1. Course overview.mp4 -
7.95 MB

          1. The theoretical Concepts/1. Course overview.srt -
2.38 KB

          1. The theoretical Concepts/10. Model Prediction Types.mp4 -
17.08 MB

          1. The theoretical Concepts/10. Model Prediction Types.srt -
5.3 KB

          1. The theoretical Concepts/11. Performance Evaluation Metrics.mp4 -
12.85 MB

          1. The theoretical Concepts/11. Performance Evaluation Metrics.srt -
4.56 KB

          1. The theoretical Concepts/2. Prerequisites of the course.mp4 -
8.88 MB

          1. The theoretical Concepts/2. Prerequisites of the course.srt -
1.78 KB

          1. The theoretical Concepts/3. What is intrusion detection.mp4 -
17.83 MB

          1. The theoretical Concepts/3. What is intrusion detection.srt -
4.04 KB

          1. The theoretical Concepts/4. How applied machine learning can be used for intrusion detection.mp4 -
12.18 MB

          1. The theoretical Concepts/4. How applied machine learning can be used for intrusion detection.srt -
3.45 KB

          1. The theoretical Concepts/5. How to build an intrusion detection Model.mp4 -
27.21 MB

          1. The theoretical Concepts/5. How to build an intrusion detection Model.srt -
5.36 KB

          1. The theoretical Concepts/6. Data Preparation.mp4 -
13.85 MB

          1. The theoretical Concepts/6. Data Preparation.srt -
2.31 KB

          1. The theoretical Concepts/7. Features Encoding.mp4 -
14.55 MB

          1. The theoretical Concepts/7. Features Encoding.srt -
2.12 KB

          1. The theoretical Concepts/8. Features Scaling.mp4 -
12.43 MB

          1. The theoretical Concepts/8. Features Scaling.srt -
1.81 KB

          1. The theoretical Concepts/9. Features Selection.mp4 -
9 MB

          1. The theoretical Concepts/9. Features Selection.srt -
1.93 KB

          2. Practical Section/1. Loading And Exploring The Dataset.mp4 -
34.71 MB

          2. Practical Section/1. Loading And Exploring The Dataset.srt -
4.61 KB

          2. Practical Section/1.1 Course source code.ipynb -
86.03 KB

          2. Practical Section/1.2 IDS Dataset.csv -
2.66 MB

          2. Practical Section/2. Encoding Dataset Using Label Encoder.mp4 -
34.78 MB

          2. Practical Section/2. Encoding Dataset Using Label Encoder.srt -
5.94 KB

          2. Practical Section/3. Checking The Dataset Balancing Status.mp4 -
37.65 MB

          2. Practical Section/3. Checking The Dataset Balancing Status.srt -
7.38 KB

          2. Practical Section/4. Scaling Dataset The Wrong Way.mp4 -
38.8 MB

          2. Practical Section/4. Scaling Dataset The Wrong Way.srt -
8.02 KB

          2. Practical Section/5. Scaling Dataset Best Practice.mp4 -
15.3 MB

          2. Practical Section/5. Scaling Dataset Best Practice.srt -
2.58 KB

          2. Practical Section/6. Features Selection Using Gini Index.mp4 -
46.64 MB

          2. Practical Section/6. Features Selection Using Gini Index.srt -
8.47 KB

          2. Practical Section/7. Build IDS Model Using The UNSW_NB15 Dataset.html -
213 bytes

          Bonus Resources.txt -
386 bytes



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