Stack overflow Tag Predictor

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  • Python for Data Science Introduction 0/1

  • Python for Data Science: Data Structures 0/6

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

    • Lecture3.1
      Introduction 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 Data Science: Computational Complexity 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 Hash table/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 0/1

    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 0/27

    • Lecture10.1
      Introduction to Probability and Statistics 17 min
    • Lecture10.2
      Population and Sample 07 min
    • Lecture10.3
      Gaussian/Normal Distribution and its PDF(Probability Density Function) 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
      Kernel density estimation 07 min
    • Lecture10.8
      Sampling distribution & 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 (C.I) Introduction 08 min
    • Lecture10.21
      Computing confidence-interval given distribution 11 min
    • Lecture10.22
      C.I for mean of a normal random variable 14 min
    • Lecture10.23
      Confidence interval using bootstrapping 17 min
    • Lecture10.24
      Hypothesis testing methodology, Null-hypothesis, p-value 16 min
    • Lecture10.25
      Resampling and permutation test 15 min
    • Lecture10.26
      K-S Test 06 min
    • Lecture10.27
      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} 05 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 06 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
  • Principal Component Analysis(PCA) 0/10

    • Lecture12.1
      Why learn it 04 min
    • Lecture12.2
      Geometric intuition 14 min
    • Lecture12.3
      Mathematical objective function 13 min
    • Lecture12.4
      Alternative formulation of PCA: distance minimization 10 min
    • Lecture12.5
      Eigenvalues and eigenvectors 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
      Code example using visualization 19 min
    • Lecture12.10
      PCA for dimensionality reduction (not-visualization) 15 min
  • T-distributed stochastic neighborhood embedding (t-SNE) 0/7

    • Lecture13.1
      What is t-SNE? 07 min
    • Lecture13.2
      Neighborhood of a point, Embedding 07 min
    • Lecture13.3
      Geometric intuition 09 min
    • Lecture13.4
      Crowding problem 08 min
    • Lecture13.5
      How to apply t-SNE and interpret its output (distill.pub) 38 min
    • Lecture13.6
      t-SNE on MNIST 07 min
    • Lecture13.7
      Code example 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 Neighbors 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 . [./knn/knn.ipynb and knn folder] 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 0/20

    • 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 27 min
    • Lecture16.18
      Bias-Variance tradeoff 24 min
    • Lecture16.19
      Intuitive understanding of bias-variance. 06 min
    • Lecture16.20
      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 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
  • Naive Bayes 0/21

    • Lecture18.1
      Conditional probability 13 min
    • Lecture18.2
      Independent vs Mutually exclusive events 06 min
    • Lecture18.3
      Bayes Theorem with examples 18 min
    • Lecture18.4
      Exercise problems on Bayes Theorem
    • Lecture18.5
      Naive Bayes algorithm 26 min
    • Lecture18.6
      Toy example: Train and test stages 26 min
    • Lecture18.7
      Naive Bayes on Text data 16 min
    • Lecture18.8
      Laplace/Additive Smoothing 24 min
    • Lecture18.9
      Log-probabilities for numerical stability 11 min
    • Lecture18.10
      Bias and Variance tradeoff 14 min
    • Lecture18.11
      Feature importance and interpretability 10 min
    • Lecture18.12
      Imbalanced data 14 min
    • Lecture18.13
      Outliers 06 min
    • Lecture18.14
      Missing values 03 min
    • Lecture18.15
      Handling Numerical features (Gaussian NB) 13 min
    • Lecture18.16
      Multiclass classification 02 min
    • Lecture18.17
      Similarity or Distance matrix 03 min
    • Lecture18.18
      Large dimensionality 02 min
    • Lecture18.19
      Best and worst cases 08 min
    • Lecture18.20
      Code example 07 min
    • Lecture18.21
      Exercise: Apply Naive Bayes to Amazon reviews 06 min
  • Logistic Regression 0/18

    • Lecture19.1
      Geometric intuition of Logistic Regression 31 min
    • Lecture19.2
      Sigmoid function: Squashing 37 min
    • Lecture19.3
      Mathematical formulation of Objective function 24 min
    • Lecture19.4
      Weight vector 11 min
    • Lecture19.5
      L2 Regularization: Overfitting and Underfitting 26 min
    • Lecture19.6
      L1 regularization and sparsity 11 min
    • Lecture19.7
      Probabilistic Interpretation: Gaussian Naive Bayes 19 min
    • Lecture19.8
      Loss minimization interpretation 24 min
    • Lecture19.9
      Hyperparameter search: Grid Search and Random Search 16 min
    • Lecture19.10
      Column Standardization 05 min
    • Lecture19.11
      Feature importance and Model interpretability 14 min
    • Lecture19.12
      Collinearity of features 14 min
    • Lecture19.13
      Test/Run time space and time complexity 10 min
    • Lecture19.14
      Real world cases 11 min
    • Lecture19.15
      Non-linearly separable data & feature engineering 28 min
    • Lecture19.16
      Code sample: Logistic regression, GridSearchCV, RandomSearchCV 23 min
    • Lecture19.17
      Exercise: Apply Logistic regression to Amazon reviews dataset. 06 min
    • Lecture19.18
      Extensions to Logistic Regression: Generalized linear models 09 min
  • Linear Regression 0/4

    • Lecture20.1
      Geometric intuition 13 min
    • Lecture20.2
      Mathematical formulation 14 min
    • Lecture20.3
      Cases 08 min
    • Lecture20.4
      Code Sample 13 min
  • Solving optimization problems : Stochastic Gradient Descent 0/12

    • Lecture21.1
      Differentiation 29 min
    • Lecture21.2
      Online differentiation tools 08 min
    • Lecture21.3
      Maxima and Minima 12 min
    • Lecture21.4
      Vector calculus: Grad 10 min
    • Lecture21.5
      Gradient descent: geometric intuition 19 min
    • Lecture21.6
      Learning rate 08 min
    • Lecture21.7
      Gradient descent for linear regression 08 min
    • Lecture21.8
      SGD algorithm 09 min
    • Lecture21.9
      Constrained Optimization & PCA 14 min
    • Lecture21.10
      Logistic regression formulation revisited 06 min
    • Lecture21.11
      Why L1 regularization creates sparsity? 17 min
    • Lecture21.12
      Exercise: Implement SGD for linear regression 06 min
  • Stack Overflow Tag Prediction 0/18

    • Lecture22.1
      Business/Real world problem 10 min
    • Lecture22.2
      Business objectives and constraints 05 min
    • Lecture22.3
      Mapping to an ML problem: Data overview 04 min
    • Lecture22.4
      Mapping to an ML problem:ML problem formulation. 05 min
    • Lecture22.5
      Mapping to an ML problem:Performance metrics. 21 min
    • Lecture22.6
      Hamming loss 07 min
    • Lecture22.7
      EDA:Data Loading 13 min
    • Lecture22.8
      EDA:Analysis of tags 11 min
    • Lecture22.9
      EDA:Data Preprocessing 11 min
    • Lecture22.10
      Data Modeling : Multi label Classification 18 min
    • Lecture22.11
      Data preparation. 08 min
    • Lecture22.12
      Train-Test Split 02 min
    • Lecture22.13
      Featurization 06 min
    • Lecture22.14
      Logistic regression: One VS Rest 07 min
    • Lecture22.15
      Sampling data and tags+Weighted models. 04 min
    • Lecture22.16
      Logistic regression revisited 04 min
    • Lecture22.17
      Why not use advanced techniques 03 min
    • Lecture22.18
      Assignments. 05 min

Description:

Stack Overflow is the largest, most trusted online community for developers to learn, share their programming knowledge, and build their careers.

Stack Overflow is something which every programmer use one way or another. Each month, over 50 million developers come to Stack Overflow to learn, share their knowledge, and build their careers. It features questions and answers on a wide range of topics in computer programming.

The website serves as a platform for users to ask and answer questions, and, through membership and active participation, to vote questions and answers up or down and edit questions and answers in a fashion similar to a wiki or Digg. As of April 2014 Stack Overflow has over 4,000,000 registered users, and it exceeded 10,000,000 questions in late August 2015. Based on the type of tags assigned to questions, the top eight most discussed topics on the site are: Java, JavaScript, C#, PHP, Android, jQuery, Python and HTML.

Statement: (Multilabel Classification) A tag is a word or phrase that describes the topic of the question. Every question should have at least one tag, and can have up to five tags. Tags can be newly created by the user (if the user has reputation above 1500), or can be chosen from the list of tags available in the site. Tags help experts in finding the relevant questions that they can answer. Tags can also be used to find questions that are relevant or interesting to a user. Given this huge number of tags, it may be difficult for users to manually search appropriate tags while posting questions. Also, only users with good reputation can add new tags which in a way limit normal users from suggesting new tags

Since there are a huge number of tags, it is often a cumbersome process to search the correct tags. It may be useful to have an auto-tagging system that suggests tags to users depending on the content of the question.

Data:

Data Type:CSV files

train.csv (Id , title, body, tags)

Test.csv (id, title, body)

Data Size: 10GB

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 269
  • Quizzes 0
  • Duration 70+ hours
  • Skill level All levels
  • Language English
  • Students 2
  • Assessments Yes
QUALIFICATION: Masters from IISC Bangalore PROFESSIONAL EXPIERENCE: 9+ years of Experience( Yahoo Labs, Matherix Labs Co-founder and Amazon)
₹15,000.00

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