Home Courses Microsoft Malware Detection Models on all features :RandomForest and Xgboost

Models on all features :RandomForest and Xgboost

Instructor: Applied AI Course Duration: 4 mins

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Models on all features :t-SNE

Assignments.

Case Study : Microsoft Malware Detection

1.1

Business/real world problem :Problem definition

6 min

1.2

Business/real world problem :Objectives and constraints

7 min

1.3

Machine Learning problem mapping :Data overview.

13 min

1.4

Machine Learning problem mapping :ML problem

12 min

1.5

Machine Learning problem mapping :Train-Test splitting

4 min

1.6

Exploratory Data Analysis :Class distribution.

3 min

1.7

Exploratory Data Analysis :Feature extraction from byte files

8 min

1.8

Exploratory Data Analysis :Multivariate analysis of features from byte files

3 min

1.9

Exploratory Data Analysis :Train-Test class distributions

3 min

1.10

ML models - using byte files only :Random Model

11 min

1.11

7 min

1.12

Logistic regression

5 min

1.13

Random Forest And Xg Boost

7 min

1.14

Feature extraction & Multi-threading.

11 min

1.15

File-size feature

2 min

1.16

Univariate analysis

3 min

1.17

t-SNE analysis.

2 min

1.18

ML models on ASM file features

7 min

1.19

Models on all features :t-SNE

2 min

1.20

Models on all features :RandomForest and Xgboost

4 min

1.21

4 min