Airbnb First Travel Destination

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  • Python for Data Science Introduction 0/0

  • 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 0/3

    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 0/23

    • Lecture4.1
      Introduction to Probability and Statistics 17 min
    • Lecture4.2
      Population and Sample 17 min
    • Lecture4.3
      Gaussian/Normal Distribution and its PDF(Probability Density Function) 27 min
    • Lecture4.4
      CDF(Cumulative Distribution function) of Gaussian/Normal distribution 11 min
    • Lecture4.5
      Symmetric distribution, Skewness and Kurtosis 05 min
    • Lecture4.6
      Standard normal variate (z) and standardization 15 min
    • Lecture4.7
      Kernel density estimation 07 min
    • Lecture4.8
      Sampling distribution & Central Limit theorem 19 min
    • Lecture4.9
      Q-Q plot:How to test if a random variable is normally distributed or not? 23 min
    • Lecture4.10
      Discrete and Continuous Uniform distributions 13 min
    • Lecture4.11
      How to randomly sample data points (Uniform Distribution) 10 min
    • Lecture4.12
      Bernoulli and Binomial Distribution 11 min
    • Lecture4.13
      Log Normal Distribution 12 min
    • Lecture4.14
      Power law distribution 12 min
    • Lecture4.15
      Box cox transform 12 min
    • Lecture4.16
      Co-variance 14 min
    • Lecture4.17
      Pearson Correlation Coefficient 13 min
    • Lecture4.18
      Spearman Rank Correlation Coefficient 07 min
    • Lecture4.19
      Correlation vs Causation 03 min
    • Lecture4.20
      Confidence interval (C.I) Introduction 30 min
    • Lecture4.21
      Computing confidence-interval given distribution 11 min
    • Lecture4.22
      C.I for mean of a normal random variable 14 min
    • Lecture4.23
      Confidence interval using bootstrapping 17 min
    • Lecture4.24
      Hypothesis testing methodology, Null-hypothesis, p-value 16 min
    • Lecture4.25
      Resampling and permutation test 15 min
    • Lecture4.26
      K-S Test 06 min
    • Lecture4.27
      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.

    • Lecture5.1
      what is dimensionality reduction? 03 min
    • Lecture5.2
      Row vector, Column vector: Iris dataset example. 05 min
    • Lecture5.3
      Represent a dataset: D= {x_i, y_i} 04 min
    • Lecture5.4
      Represent a dataset as a Matrix. 07 min
    • Lecture5.5
      Data preprocessing: Column Normalization 20 min
    • Lecture5.6
      Mean of a data matrix 06 min
    • Lecture5.7
      Data preprocessing: Column Standardization 16 min
    • Lecture5.8
      Co-variance of a Data Matrix. 24 min
    • Lecture5.9
      MNIST dataset (784 dimensional) 20 min
    • Lecture5.10
      Code to load MNIST Dataset 12 min
  • PCA(principal component analysis) 0/10

    • Lecture6.1
      Why learn PCA? 04 min
    • Lecture6.2
      Geometric intuition of PCA 14 min
    • Lecture6.3
      Mathematical objective function of PCA 14 min
    • Lecture6.4
      Alternative formulation of PCA: Distance minimization 10 min
    • Lecture6.5
      Eigen values and Eigen vectors 23 min
    • Lecture6.6
      PCA for Dimensionality Reduction and Visualization 10 min
    • Lecture6.7
      Visualize MNIST dataset 05 min
    • Lecture6.8
      Limitations of PCA 05 min
    • Lecture6.9
      PCA Code example using Visualization 19 min
    • Lecture6.10
      PCA Code example using non-Visualization 15 min
  • (t-SNE)T-distributed Stochastic Neighbourhood Embedding 0/7

    • Lecture7.1
      What is t-SNE? 07 min
    • Lecture7.2
      Neighborhood of a point, Embedding 07 min
    • Lecture7.3
      Geometric intuition of t-SNE 09 min
    • Lecture7.4
      Crowding Problem 08 min
    • Lecture7.5
      How to apply t-SNE and interpret its output 38 min
    • Lecture7.6
      t-SNE on MNIST 04 min
    • Lecture7.7
      Code example of t-SNE 09 min
  • Real world problem: Predict rating given product reviews on Amazon 0/17

    • Lecture8.1
      Dataset overview: Amazon Fine Food reviews(EDA) 23 min
    • Lecture8.2
      Data Cleaning: Deduplication 15 min
    • Lecture8.3
      Why convert text to a vector? 14 min
    • Lecture8.4
      Bag of Words (BoW) 14 min
    • Lecture8.5
      Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization 15 min
    • Lecture8.6
      uni-gram, bi-gram, n-grams 09 min
    • Lecture8.7
      tf-idf (term frequency- inverse document frequency) 22 min
    • Lecture8.8
      Why use log in IDF? 14 min
    • Lecture8.9
      Word2Vec 16 min
    • Lecture8.10
      Avg-Word2Vec, tf-idf weighted Word2Vec 09 min
    • Lecture8.11
      Bag of Words( Code Sample) 09 min
    • Lecture8.12
      Text Preprocessing( Code Sample) 11 min
    • Lecture8.13
      Bi-Grams and n-grams (Code Sample) 05 min
    • Lecture8.14
      TF-IDF (Code Sample) 06 min
    • Lecture8.15
      Word2Vec (Code Sample) 12 min
    • Lecture8.16
      Avg-Word2Vec and TFIDF-Word2Vec (Code Sample) 02 min
    • Lecture8.17
      Exercise: t-SNE visualization of Amazon reviews with polarity based color-coding 06 min
  • Classification And Regression Models: K-Nearest Neighbors 0/32

    • Lecture9.1
      How “Classification” works? 10 min
    • Lecture9.2
      Data matrix notation 07 min
    • Lecture9.3
      Classification vs Regression (examples) 06 min
    • Lecture9.4
      K-Nearest Neighbors Geometric intuition with a toy example 11 min
    • Lecture9.5
      Failure cases of KNN 07 min
    • Lecture9.6
      Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming 20 min
    • Lecture9.7
      Cosine Distance & Cosine Similarity 19 min
    • Lecture9.8
      How to measure the effectiveness of k-NN? 16 min
    • Lecture9.9
      Test/Evaluation time and space complexity 12 min
    • Lecture9.10
      KNN Limitations 09 min
    • Lecture9.11
      Decision surface for K-NN as K changes 23 min
    • Lecture9.12
      Overfitting and Underfitting 12 min
    • Lecture9.13
      Need for Cross validation 22 min
    • Lecture9.14
      K-fold cross validation 17 min
    • Lecture9.15
      Visualizing train, validation and test datasets 13 min
    • Lecture9.16
      How to determine overfitting and underfitting? 19 min
    • Lecture9.17
      Time based splitting 19 min
    • Lecture9.18
      k-NN for regression 05 min
    • Lecture9.19
      Weighted k-NN 08 min
    • Lecture9.20
      Voronoi diagram 04 min
    • Lecture9.21
      Binary search tree 16 min
    • Lecture9.22
      How to build a kd-tree 17 min
    • Lecture9.23
      Find nearest neighbours using kd-tree 13 min
    • Lecture9.24
      Limitations of Kd tree 09 min
    • Lecture9.25
      Extensions 03 min
    • Lecture9.26
      Hashing vs LSH 10 min
    • Lecture9.27
      LSH for cosine similarity 40 min
    • Lecture9.28
      LSH for euclidean distance 13 min
    • Lecture9.29
      Probabilistic class label 08 min
    • Lecture9.30
      Code Sample:Decision boundary 23 min
    • Lecture9.31
      Code Sample:Cross Validation 13 min
    • Lecture9.32
      Exercise: Apply k-NN on Amazon reviews dataset 05 min
  • Performance measurement of models 0/8

    • Lecture10.1
      Accuracy 15 min
    • Lecture10.2
      Confusion matrix, TPR, FPR, FNR, TNR 25 min
    • Lecture10.3
      Precision and recall 10 min
    • Lecture10.4
      Receiver Operating Characteristic Curve (ROC) curve and AUC 19 min
    • Lecture10.5
      Log-loss 12 min
    • Lecture10.6
      R-Squared/Coefficient of determination 14 min
    • Lecture10.7
      Median absolute deviation (MAD) 05 min
    • Lecture10.8
      Distribution of errors 07 min
  • Naive Bayes 0/10

    • Lecture11.1
      Conditional probability
    • Lecture11.2
      Conditional independence
    • Lecture11.3
      Bayes rule and examples
    • Lecture11.4
      Naive Bayes algorithm
    • Lecture11.5
      Toy example
    • Lecture11.6
      Space and Time complexity: train and test time 30 min
    • Lecture11.7
      Laplace/Additive Smoothing 30 min
    • Lecture11.8
      Underfitting and Overfitting 30 min
    • Lecture11.9
      Feature importance and interpretability 30 min
    • Lecture11.10
      Exercise: Apply Naive Bayes to Amazon reviews
  • Logistic Regression 0/19

    • Lecture12.1
      Geometric intuition 31 min
    • Lecture12.2
      Sigmoid function: Squashing 37 min
    • Lecture12.3
      Mathematical formulation of Objective function 24 min
    • Lecture12.4
      Weight vector 11 min
    • Lecture12.5
      Regularization: Overfitting and Underfitting 26 min
    • Lecture12.6
      L2 regularization, L1 regularization and sparsity 11 min
    • Lecture12.7
      L1 regularization and sparsity 11 min
    • Lecture12.8
      Probabilistic Interpretation: Gaussian Naive Bayes 19 min
    • Lecture12.9
      Loss function interpretation 24 min
    • Lecture12.10
      Hyperparameter search: Grid Search and Random Search 16 min
    • Lecture12.11
      Column Standardization 05 min
    • Lecture12.12
      Feature importance and Model interpretability 14 min
    • Lecture12.13
      Collinearity of features 14 min
    • Lecture12.14
      Test/Run time space and time complexity 10 min
    • Lecture12.15
      Real world cases 11 min
    • Lecture12.16
      Non-linearly separable data & feature engineering 28 min
    • Lecture12.17
      Code sample: Logistic regression, GridSearchCV, RandomSearchCV 23 min
    • Lecture12.18
      Exercise: Apply Logistic regression to Amazon reviews dataset. 06 min
    • Lecture12.19
      Extensions to Logistic Regression: Generalized linear models 09 min
  • Linear Regression 0/4

    • Lecture13.1
      Geometric intuition 13 min
    • Lecture13.2
      Mathematical formulation 16 min
    • Lecture13.3
      Real world Cases 08 min
    • Lecture13.4
      Code sample for Linear Regression 13 min
  • Solving optimization problems : Stochastic Gradient Descent 0/12

    • Lecture14.1
      Differentiation 29 min
    • Lecture14.2
      Online differentiation tools 08 min
    • Lecture14.3
      Maxima and Minima 12 min
    • Lecture14.4
      Vector calculus: Grad 10 min
    • Lecture14.5
      Gradient descent: geometric intuition 19 min
    • Lecture14.6
      Learning rate 10 min
    • Lecture14.7
      Gradient descent for linear regression 08 min
    • Lecture14.8
      SGD algorithm 09 min
    • Lecture14.9
      Constrained Optimization & PCA 14 min
    • Lecture14.10
      Logistic regression formulation revisited 06 min
    • Lecture14.11
      Why L1 regularization creates sparsity? 17 min
    • Lecture14.12
      Exercise: Implement SGD for linear regression 06 min
  • Bias-Variance tradeoff 0/3

    • Lecture15.1
      Intuition: Underfit and Overfit 30 min
    • Lecture15.2
      Derivation for linear regression 30 min
    • Lecture15.3
      Bias Variance tradeoff for k-NN, NaiveBayes, Logistic Regression, Linear regression
  • Support Vector Machines (SVM) 0/16

    • Lecture16.1
      Geometric Intution
    • Lecture16.2
      Mathematical derivation 30 min
    • Lecture16.3
      Loss function (Hinge Loss) based interpretation 30 min
    • Lecture16.4
      Support vectors 30 min
    • Lecture16.5
      Linear SVM 30 min
    • Lecture16.6
      Primal and Dual
    • Lecture16.7
      Kernelization
    • Lecture16.8
      RBF-Kernel 30 min
    • Lecture16.9
      Polynomial kernel 30 min
    • Lecture16.10
      Domain specific Kernels 30 min
    • Lecture16.11
      Train and run time complexities 30 min
    • Lecture16.12
      Bias-variance tradeoff: Underfitting and Overfitting
    • Lecture16.13
      nu-SVM: control errors and support vectors 30 min
    • Lecture16.14
      SVM Regression 30 min
    • Lecture16.15
      Code Samples 30 min
    • Lecture16.16
      Exercise: Apply SVM to Amazon reviews dataset 30 min
  • Decision Trees 0/15

    • Lecture17.1
      Geometric Intuition: Axis parallel hyperplanes 11 min
    • Lecture17.2
      Sample Decision tree 08 min
    • Lecture17.3
      Building a decision Tree:Entropy 19 min
    • Lecture17.4
      Building a decision Tree:Information Gain 10 min
    • Lecture17.5
      Building a decision Tree: Gini Impurity 07 min
    • Lecture17.6
      Building a decision Tree: Constructing a DT 21 min
    • Lecture17.7
      Building a decision Tree: Splitting numerical features 08 min
    • Lecture17.8
      Feature standardization 04 min
    • Lecture17.9
      Building a decision Tree:Categorical features with many possible values 07 min
    • Lecture17.10
      Overfitting and Underfitting 08 min
    • Lecture17.11
      Train and Run time complexity 07 min
    • Lecture17.12
      Regression using Decision Trees 09 min
    • Lecture17.13
      Cases 12 min
    • Lecture17.14
      Code Samples 09 min
    • Lecture17.15
      Exercise: Decision Trees on Amazon reviews dataset 03 min
  • Ensemble Models 0/12

    • Lecture18.1
      Introduction to Bootstrapped Aggregation (Bagging) 30 min
    • Lecture18.2
      Random Forest and their construction 30 min
    • Lecture18.3
      Bias-Variance tradeoff(Random Forest)
    • Lecture18.4
      Applicative details,, Code Samples(Random Forest)
    • Lecture18.5
      Intution to Boosting
    • Lecture18.6
      Gradient Boosting and XGBoost Algorithm 30 min
    • Lecture18.7
      Loss function(Gradient Boosting and XGBoost) 30 min
    • Lecture18.8
      XGBoost Code samples 30 min
    • Lecture18.9
      AdaBoost: geometric intuition 30 min
    • Lecture18.10
      Cascading models, Stacking models 30 min
    • Lecture18.11
      How to win Kaggle competitions using Ensembles 30 min
    • Lecture18.12
      Exercise: Apply GBDT and RF to Amazon reviews dataset 30 min
  • Python for Data Science: Data Structures 0/2

    • Lecture19.1
      Lists and tuples
    • Lecture19.2
      Dictionaries, sets and Strings
  • Python for Data Science: Functions 0/5

    • Lecture20.1
      Introduction and types of functions
    • Lecture20.2
      Function arguments
    • Lecture20.3
      Recursive functions
    • Lecture20.4
      Lambda functions
    • Lecture20.5
      Modules and Packages
  • Python for Data Science: Miscellaneous 0/3

    • Lecture21.1
      File handling
    • Lecture21.2
      exception handling
    • Lecture21.3
      debugging mode
  • Python for Data Science: OOPS(object oriented program) 0/2

    • Lecture22.1
      Principles of Object oriented programming
    • Lecture22.2
      classes and objects
  • Python for Data Science: Pandas 0/7

    • Lecture23.1
      Getting started with pandas
    • Lecture23.2
      Data Frame Basics
    • Lecture23.3
      Loading Data from csv, excel, txt.. etc
    • Lecture23.4
      Handling Missing data
    • Lecture23.5
      Group by, Concat and merging data
    • Lecture23.6
      Pivot Table and Reshaping of Table
    • Lecture23.7
      Time series
  • Python for Data Science: Matplotlib 0/1

    • Lecture24.1
      Getting started with Matplotlib
  • Python for Data Science: Numpy and Scipy 0/1

    • Lecture25.1
      Getting started with Numpy and Scipy
  • Python for Data Science: Seaborn 0/1

    • Lecture26.1
      Getting Started with Seaborn
  • Python for Data Science: Scikit Learn 0/1

    • Lecture27.1
      Getting started with scikit learn

Statement:

(Multiclass Classification)

Airbnb​ is an online marketplace and hospitality service, enabling people to lease or rent short-term lodging including vacation rentals , apartment rentals, homestays , hostels beds, or hotel rooms.
New users on Airbnb can book a place to stay in 34,000+ cities across 190+ countries. By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand. We need to predict the first travel destination of a new user based on his personalized content .

  • Data Type:
    1. CSV files
    2. age_gender_bkts.csv – summary statistics of users’ age group, gender, country of
      Destination
    3. countries.csv – summary statistics of destination countries in this dataset and their locations
    4. sessions.csv – (user_id , action , action_type , action_detail , device_type , secs_elapsed )
    5. test_users.csv – ( id , date_account_created , timestamp_first_active ,
      date_account_created, date_first_booking , gender , age , signup_method , signup_flow,
      language , affiliate_channel , first_affiliate_tracked , signup_app , first_device_type ,
      first_browser , country_destination )
    6. train_users.csv – ( Similar to test data )
  • Data Size: 664.2MB

Key Points:

  1. Validity of this course is 240days( 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 261
  • Quizzes 0
  • Duration 70+ hours
  • Skill level All levels
  • Language English
  • Students 1
  • 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. October 17, 2017

    Hi, I am doing my MS in US and I am interested in taking this course, can I enroll from here? I also wanted to know what is the difference between the two courses 15000 courses and the 25000 course? Because both include the algorithms.

    • October 17, 2017

      Hi, There are clustering Algorithms, Deep-learning Neural Networks and 9+ case studies which wont be covered in this/any case study

  2. October 17, 2017

    Hi, I am currently doing my Master’s in US and I want to enroll in the courses. I wanted to know the difference between these 15000 courses and the 25000 course because both includes ML algorithms?

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