Microsoft Malware Detection

₹15,000.00
  • Python for Data Science Introduction 0/0

  • Python for Data Science: Data Structures 0/5

    • Lecture2.1
      Lists 38 min
    • Lecture2.2
      Tuples Part1 10 min
    • Lecture2.3
      Tuples Part2 04 min
    • Lecture2.4
      Sets 16 min
    • Lecture2.5
      Dictionaries 21 min
    • Lecture2.6
      Strings 16 min
  • Python for Data Science: Functions 0/10

    • Lecture3.1
      Introduction to functions 13 min
    • Lecture3.2
      Types of functions 25 min
    • Lecture3.3
      Function arguments 10 min
    • Lecture3.4
      Recursive functions 16 min
    • Lecture3.5
      Lambda functions 08 min
    • Lecture3.6
      Modules 07 min
    • Lecture3.7
      Packages 06 min
    • Lecture3.8
      File Handling 23 min
    • Lecture3.9
      Exception Handling 15 min
    • Lecture3.10
      Debugging Python 15 min
  • Python for Data Science: Numpy 0/2

    • Lecture4.1
      Numpy Introduction 41 min
    • Lecture4.2
      Numerical operations on Numpy 41 min
  • Python for Data Science: Matplotlib 0/1

    • Lecture5.1
      Getting started with Matplotlib 20 min
  • Python for Data Science: Pandas 0/3

    • Lecture6.1
      Getting started with pandas 08 min
    • Lecture6.2
      Data Frame Basics 09 min
    • Lecture6.3
      Key Operations on Data Frames 31 min
  • Python for Datascience:Computational Complexity: an Introduction 0/4

    • Lecture7.1
      Space and Time Complexity: Find largest number in a list 20 min
    • Lecture7.2
      Binary search 17 min
    • Lecture7.3
      Find elements common in two lists 06 min
    • Lecture7.4
      Find elements common in two lists using a Hashtable/Dict 12 min
  • Plotting for exploratory data analysis (EDA) 0/1

    exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

  • Linear Algebra 0/2

    It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms.

  • Probability and Statistics 0/25

    • Lecture10.1
      Introduction to Probability and Statistics 17 min
    • Lecture10.2
      Population and Sample 07 min
    • Lecture10.3
      Gaussian/Normal Distribution 27 min
    • Lecture10.4
      CDF(Cumulative Distribution function) of Gaussian/Normal distribution 11 min
    • Lecture10.5
      Symmetric distribution, Skewness and Kurtosis 05 min
    • Lecture10.6
      Standard normal variate (z) and standardization 15 min
    • Lecture10.7
      Kernal density estimation 07 min
    • Lecture10.8
      Sampling distribution and Central limit theorem 19 min
    • Lecture10.9
      Q-Q plot:How to test if a random variable is normally distributed or not? 23 min
    • Lecture10.10
      Discrete and Continuous Uniform distributions 13 min
    • Lecture10.11
      How to randomly sample data points (Uniform Distribution) 10 min
    • Lecture10.12
      Bernoulli and Binomial Distribution 11 min
    • Lecture10.13
      Log Normal Distribution 12 min
    • Lecture10.14
      Power law distribution 12 min
    • Lecture10.15
      Box cox transform 12 min
    • Lecture10.16
      Co-variance 14 min
    • Lecture10.17
      Pearson Correlation Coefficient 13 min
    • Lecture10.18
      Spearman Rank Correlation Coefficient 07 min
    • Lecture10.19
      Correlation vs Causation 03 min
    • Lecture10.20
      Confidence Interval Introduction 08 min
    • Lecture10.21
      Hypothesis testing methodology, Null-hypothesis, p-value 16 min
    • Lecture10.22
      Testing methodology, Null-hypothesis, test-statistic, p-value 16 min
    • Lecture10.23
      Resampling and permutation test 15 min
    • Lecture10.24
      K-S Test 06 min
    • Lecture10.25
      K-S Test for similarity of two distributions 15 min
  • Dimensionality reduction and Visualization: 0/10

    In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, via obtaining a set of principal variables. It can be divided into feature selection and feature extraction.

    • Lecture11.1
      what is dimensionality reduction? 03 min
    • Lecture11.2
      Row vector, Column vector: Iris dataset example 05 min
    • Lecture11.3
      Represent a dataset: D= {x_i, y_i} 04 min
    • Lecture11.4
      Represent a dataset as a Matrix 07 min
    • Lecture11.5
      Data preprocessing: Column Normalization 20 min
    • Lecture11.6
      Mean of a data matrix 06 min
    • Lecture11.7
      Data preprocessing: Column Standardization 16 min
    • Lecture11.8
      Co-variance of a Data Matrix 24 min
    • Lecture11.9
      MNIST dataset (784 dimensional) 20 min
    • Lecture11.10
      Code to load MNIST Dataset 12 min
  • PCA(principal component analysis) 0/10

    • Lecture12.1
      Why learn PCA? 04 min
    • Lecture12.2
      Geometric intuition of PCA 14 min
    • Lecture12.3
      Mathematical objective function of PCA 13 min
    • Lecture12.4
      Alternative formulation of PCA: Distance minimization 10 min
    • Lecture12.5
      Eigen values and Eigen vectors 23 min
    • Lecture12.6
      PCA for Dimensionality Reduction and Visualization 10 min
    • Lecture12.7
      Visualize MNIST dataset 05 min
    • Lecture12.8
      Limitations of PCA 05 min
    • Lecture12.9
      PCA Code example using Visualization 19 min
    • Lecture12.10
      PCA Code example using non-Visualization 15 min
  • (t-SNE)T-distributed Stochastic Neighbourhood Embedding 0/7

    • Lecture13.1
      What is t-SNE? 07 min
    • Lecture13.2
      Neighborhood of a point, Embedding 07 min
    • Lecture13.3
      Geometric intuition of t-SNE 09 min
    • Lecture13.4
      Crowding Problem 08 min
    • Lecture13.5
      How to apply t-SNE and interpret its output 38 min
    • Lecture13.6
      t-SNE on MNIST 07 min
    • Lecture13.7
      Code example of t-SNE 09 min
  • Real world problem: Predict rating given product reviews on Amazon 0/17

    • Lecture14.1
      Dataset overview: Amazon Fine Food reviews(EDA) 23 min
    • Lecture14.2
      Data Cleaning: Deduplication 15 min
    • Lecture14.3
      Why convert text to a vector? 14 min
    • Lecture14.4
      Bag of Words (BoW) 18 min
    • Lecture14.5
      Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization. 15 min
    • Lecture14.6
      uni-gram, bi-gram, n-grams. 09 min
    • Lecture14.7
      tf-idf (term frequency- inverse document frequency) 22 min
    • Lecture14.8
      Why use log in IDF? 14 min
    • Lecture14.9
      Word2Vec. 16 min
    • Lecture14.10
      Avg-Word2Vec, tf-idf weighted Word2Vec 09 min
    • Lecture14.11
      Bag of Words( Code Sample) 19 min
    • Lecture14.12
      Text Preprocessing( Code Sample) 11 min
    • Lecture14.13
      Bi-Grams and n-grams (Code Sample) 05 min
    • Lecture14.14
      TF-IDF (Code Sample) 06 min
    • Lecture14.15
      Word2Vec (Code Sample) 12 min
    • Lecture14.16
      Avg-Word2Vec and TFIDF-Word2Vec (Code Sample) 02 min
    • Lecture14.17
      Exercise: t-SNE visualization of Amazon reviews with polarity based color-coding 06 min
  • Classification And Regression Models: K-Nearest Neighbours 0/32

    • Lecture15.1
      How “Classification” works? 10 min
    • Lecture15.2
      Data matrix notation 07 min
    • Lecture15.3
      Classification vs Regression (examples) 06 min
    • Lecture15.4
      K-Nearest Neighbours Geometric intuition with a toy example 11 min
    • Lecture15.5
      Failure cases of KNN 07 min
    • Lecture15.6
      Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming 20 min
    • Lecture15.7
      Cosine Distance & Cosine Similarity 19 min
    • Lecture15.8
      How to measure the effectiveness of k-NN? 16 min
    • Lecture15.9
      Test/Evaluation time and space complexity 12 min
    • Lecture15.10
      KNN Limitations 09 min
    • Lecture15.11
      Decision surface for K-NN as K changes 23 min
    • Lecture15.12
      Overfitting and Underfitting 12 min
    • Lecture15.13
      Need for Cross validation 22 min
    • Lecture15.14
      K-fold cross validation 17 min
    • Lecture15.15
      Visualizing train, validation and test datasets 13 min
    • Lecture15.16
      How to determine overfitting and underfitting? 19 min
    • Lecture15.17
      Time based splitting 19 min
    • Lecture15.18
      k-NN for regression 05 min
    • Lecture15.19
      Weighted k-NN 08 min
    • Lecture15.20
      Voronoi diagram 04 min
    • Lecture15.21
      Binary search tree 16 min
    • Lecture15.22
      How to build a kd-tree 17 min
    • Lecture15.23
      Find nearest neighbours using kd-tree 13 min
    • Lecture15.24
      Limitations of Kd tree 09 min
    • Lecture15.25
      Extensions 03 min
    • Lecture15.26
      Hashing vs LSH 10 min
    • Lecture15.27
      LSH for cosine similarity 40 min
    • Lecture15.28
      LSH for euclidean distance 13 min
    • Lecture15.29
      Probabilistic class label 08 min
    • Lecture15.30
      Code Sample:Decision boundary . 23 min
    • Lecture15.31
      Code Sample:Cross Validation 13 min
    • Lecture15.32
      Exercise: Apply k-NN on Amazon reviews dataset 05 min
  • Classification algorithms in various situations 0/19

    • Lecture16.1
      Introduction 05 min
    • Lecture16.2
      Imbalanced vs balanced dataset 23 min
    • Lecture16.3
      Multi-class classification 12 min
    • Lecture16.4
      k-NN, given a distance or similarity matrix 09 min
    • Lecture16.5
      Train and test set differences 22 min
    • Lecture16.6
      Impact of outliers 07 min
    • Lecture16.7
      Local outlier Factor (Simple solution :Mean distance to Knn) 13 min
    • Lecture16.8
      K-Distance(A),N(A) 04 min
    • Lecture16.9
      Reachability-Distance(A,B) 08 min
    • Lecture16.10
      Local reachability-density(A) 09 min
    • Lecture16.11
      Local outlier Factor(A) 21 min
    • Lecture16.12
      Impact of Scale & Column standardization 12 min
    • Lecture16.13
      Interpretability 12 min
    • Lecture16.14
      Feature Importance and Forward Feature selection 22 min
    • Lecture16.15
      Handling categorical and numerical features 24 min
    • Lecture16.16
      Handling missing values by imputation 21 min
    • Lecture16.17
      Curse of dimensionality 21 min
    • Lecture16.18
      Bias-Variance tradeoff 24 min
    • Lecture16.19
      Best and worst cases for an algorithm 06 min
  • Performance measurement of models 0/8

    • Lecture17.1
      Accuracy 15 min
    • Lecture17.2
      Confusion matrix, TPR, FPR, FNR, TNR 25 min
    • Lecture17.3
      Precision and recall,F1 score 10 min
    • Lecture17.4
      Receiver Operating Characteristic Curve (ROC) curve and AUC 19 min
    • Lecture17.5
      Log-loss 12 min
    • Lecture17.6
      R-Squared/Coefficient of determination 14 min
    • Lecture17.7
      Median absolute deviation (MAD) 05 min
    • Lecture17.8
      Distribution of errors 07 min
  • Logistic Regression 0/18

    • Lecture18.1
      Geometric intuition of Logistic Regression 31 min
    • Lecture18.2
      Sigmoid function: Squashing 37 min
    • Lecture18.3
      Mathematical formulation of Objective function 24 min
    • Lecture18.4
      Weight vector 11 min
    • Lecture18.5
      L2 Regularization: Overfitting and Underfitting 26 min
    • Lecture18.6
      L1 regularization and sparsity 11 min
    • Lecture18.7
      Probabilistic Interpretation: Gaussian Naive Bayes 19 min
    • Lecture18.8
      Loss minimization interpretation 24 min
    • Lecture18.9
      Hyperparameter search: Grid Search and Random Search 16 min
    • Lecture18.10
      Column Standardization 05 min
    • Lecture18.11
      Feature importance and Model interpretability 14 min
    • Lecture18.12
      Collinearity of features 14 min
    • Lecture18.13
      Test/Run time space and time complexity 10 min
    • Lecture18.14
      Real world cases 11 min
    • Lecture18.15
      Non-linearly separable data & feature engineering 28 min
    • Lecture18.16
      Code sample: Logistic regression, GridSearchCV, RandomSearchCV 23 min
    • Lecture18.17
      Exercise: Apply Logistic regression to Amazon reviews dataset. 06 min
    • Lecture18.18
      Extensions to Logistic Regression: Generalized linear models 09 min
  • Solving Optimization Problems 0/12

    • Lecture19.1
      Differentiation 29 min
    • Lecture19.2
      Online differentiation tools 08 min
    • Lecture19.3
      Maxima and Minima 12 min
    • Lecture19.4
      Vector calculus: Grad 10 min
    • Lecture19.5
      Gradient descent: geometric intuition 19 min
    • Lecture19.6
      Learning rate 08 min
    • Lecture19.7
      Gradient descent for linear regression 08 min
    • Lecture19.8
      SGD algorithm 09 min
    • Lecture19.9
      Constrained Optimization & PCA 06 min
    • Lecture19.10
      Logistic regression formulation revisited 06 min
    • Lecture19.11
      Why L1 regularization creates sparsity? 17 min
    • Lecture19.12
      Exercise: Implement SGD for linear regression 06 min
  • Decision Trees 0/15

    • Lecture20.1
      Geometric Intuition: Axis parallel hyperplanes 17 min
    • Lecture20.2
      Sample Decision tree 08 min
    • Lecture20.3
      Building a decision Tree:Entropy 19 min
    • Lecture20.4
      Building a decision Tree:Information Gain 10 min
    • Lecture20.5
      Building a decision Tree: Gini Impurity 07 min
    • Lecture20.6
      Building a decision Tree: Constructing a DT 21 min
    • Lecture20.7
      Building a decision Tree: Splitting numerical features 08 min
    • Lecture20.8
      Feature standardization 04 min
    • Lecture20.9
      Building a decision Tree:Categorical features with many possible values 07 min
    • Lecture20.10
      Overfitting and Underfitting 08 min
    • Lecture20.11
      Train and Run time complexity 07 min
    • Lecture20.12
      Regression using Decision Trees 09 min
    • Lecture20.13
      Cases 12 min
    • Lecture20.14
      Code Samples 09 min
    • Lecture20.15
      Exercise: Decision Trees on Amazon reviews dataset 03 min
  • Ensemble Models 0/19

    • Lecture21.1
      What are ensembles? 06 min
    • Lecture21.2
      Bootstrapped Aggregation (Bagging) Intuition 17 min
    • Lecture21.3
      Random Forest and their construction 15 min
    • Lecture21.4
      Bias-Variance tradeoff 14 min
    • Lecture21.5
      Bagging :Train and Run-time Complexity. 09 min
    • Lecture21.6
      Bagging:Code Sample 04 min
    • Lecture21.7
      Extremely randomized trees 08 min
    • Lecture21.8
      Random Tree :Cases 06 min
    • Lecture21.9
      Boosting Intuition 17 min
    • Lecture21.10
      Residuals, Loss functions and gradients 13 min
    • Lecture21.11
      Gradient Boosting 10 min
    • Lecture21.12
      Regularization by Shrinkage 08 min
    • Lecture21.13
      Train and Run time complexity 06 min
    • Lecture21.14
      XGBoost: Boosting + Randomization 14 min
    • Lecture21.15
      AdaBoost: geometric intuition 07 min
    • Lecture21.16
      Stacking models 22 min
    • Lecture21.17
      Cascading classifiers 15 min
    • Lecture21.18
      Kaggle competitions vs Real world 15 min
    • Lecture21.19
      Exercise: Apply GBDT and RF to Amazon reviews dataset. 04 min
  • Case Study : Microsoft Malware Detection 0/21

    • Lecture22.1
      Business/real world problem :Problem definition 06 min
    • Lecture22.2
      Business/real world problem :Objectives and constraints 07 min
    • Lecture22.3
      Machine Learning problem mapping :Data overview. 13 min
    • Lecture22.4
      Machine Learning problem mapping :ML problem 12 min
    • Lecture22.5
      Machine Learning problem mapping :Train-Test splitting 04 min
    • Lecture22.6
      Exploratory Data Analysis :Class distribution. 03 min
    • Lecture22.7
      Exploratory Data Analysis :Feature extraction from byte files 08 min
    • Lecture22.8
      Exploratory Data Analysis :Multivariate analysis of features from byte files 03 min
    • Lecture22.9
      Exploratory Data Analysis :Train-Test class distributions 03 min
    • Lecture22.10
      ML models – using byte files only :Random Model 11 min
    • Lecture22.11
      k-NN 07 min
    • Lecture22.12
      Logistic regression 05 min
    • Lecture22.13
      Random Forest And Xg Boost 07 min
    • Lecture22.14
      Feature extraction & Multi-threading. 11 min
    • Lecture22.15
      File-size feature 02 min
    • Lecture22.16
      Univariate analysis 03 min
    • Lecture22.17
      t-SNE analysis. 02 min
    • Lecture22.18
      ML models on ASM file features 07 min
    • Lecture22.19
      Models on all features :t-SNE 02 min
    • Lecture22.20
      Models on all features :RandomForest and Xgboost 04 min
    • Lecture22.21
      Assignments. 04 min

Statement:

The malicious files belonging to the same malware “family”, with the same forms of malicious behavior, are constantly modified and/or obfuscated using various tactics, such that they look like many different files.
Microsoft is providing the data science community with an unprecedented malware dataset and encouraging open-source progress on effective techniques for grouping variants of malware files into their respective families.

  • Data Type:
    .asm files (Each file name is a unique 10 digit hash key, file contains hexadecimal representation of binary file and will be given an integer representing the one of the 9 families to which malware belongs to.)
    Text data
  • Data Size: 17GB

Key Points:

  1. Validity of this course is 240 days( i.e Starts from the date of your registration to this course)
  2. Expert Guidance, we will try to answer your queries in atmost 24hours
  3. 10+ machine learning algorithms will be taught in this course.
  4. No prerequisites– we will teach every thing from basics ( we just expect you to know basic programming)
  5.  Python for Data science is part of the course curriculum.

 

Target Audience:

We are building our course content and teaching methodology to cater to the needs to students at various levels of expertise and varying background skills. This course can be taken by anyone with a working knowledge of a modern programming language like C/C++/Java/Python. We expect the average student to spend at least 5 hours a week over a 6 month period amounting to a 145+ hours of effort. More the effort, better the results. Here is a list of customers who would benefit from our course:

    1. Undergrad (BS/BTech/BE) students in engineering and science.
    2. Grad(MS/MTech/ME/MCA) students in engineering and science.
    3. Working professionals: Software engineers, Business analysts, Product managers, Program managers, Managers, Startup teams building ML products/services.

Course Features

  • Lectures 276
  • Quizzes 0
  • Duration 100+ hours
  • Skill level All levels
  • Language English
  • Students 5
  • Assessments Yes
QUALIFICATION: Masters from IISC Bangalore PROFESSIONAL EXPIERENCE: 9+ years of Experience( Yahoo Labs, Matherix Labs Co-founder and Amazon)
₹15,000.00

    4 Comments

  1. March 7, 2018

    Do we have a option for registering only for Case Study instead of Full Course?

  2. November 26, 2017

    It is a classroom course or online

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