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Machine Learning Course

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PricingMachine Learning CourseApplied Machine Learning Online Course
  • How to utilise Appliedaicourse 0/1

    • Lecture1.1
      How to Learn from Appliedaicourse 35 min
    • Lecture1.2
      How the Job Guarantee program works 16 min
  • Python for Data Science Introduction 0/1

    • Lecture2.1
      Python, Anaconda and relevant packages installations 23 min
    • Lecture2.2
      Why learn Python? 04 min
    • Lecture2.3
      Keywords and identifiers 06 min
    • Lecture2.4
      comments, indentation and statements 09 min
    • Lecture2.5
      Variables and data types in Python 32 min
    • Lecture2.6
      Standard Input and Output 07 min
    • Lecture2.7
      Operators 14 min
    • Lecture2.8
      Control flow: if else 10 min
    • Lecture2.9
      Control flow: while loop 16 min
    • Lecture2.10
      Control flow: for loop 15 min
    • Lecture2.11
      Control flow: break and continue 10 min
  • Python for Data Science: Data Structures 0/6

    • Lecture3.1
      Lists 38 min
    • Lecture3.2
      Tuples part 1 10 min
    • Lecture3.3
      Tuples part-2 04 min
    • Lecture3.4
      Sets 16 min
    • Lecture3.5
      Dictionary 21 min
    • Lecture3.6
      Strings 16 min
  • Python for Data Science: Functions 0/10

    • Lecture4.1
      Introduction 13 min
    • Lecture4.2
      Types of functions 25 min
    • Lecture4.3
      Function arguments 10 min
    • Lecture4.4
      Recursive functions 16 min
    • Lecture4.5
      Lambda functions 08 min
    • Lecture4.6
      Modules 07 min
    • Lecture4.7
      Packages 06 min
    • Lecture4.8
      File Handling 23 min
    • Lecture4.9
      Exception Handling 15 min
    • Lecture4.10
      Debugging Python 15 min
  • Python for Data Science: Numpy 0/2

    • Lecture5.1
      Numpy Introduction 41 min
    • Lecture5.2
      Numerical operations on Numpy 41 min
  • Python for Data Science: Matplotlib 0/1

    • Lecture6.1
      Getting started with Matplotlib 20 min
  • Python for Data Science: Pandas 0/3

    • Lecture7.1
      Getting started with pandas 08 min
    • Lecture7.2
      Data Frame Basics 09 min
    • Lecture7.3
      Key Operations on Data Frames 31 min
  • Python for Data Science: Computational Complexity 0/4

    • Lecture8.1
      Space and Time Complexity: Find largest number in a list 20 min
    • Lecture8.2
      Binary search 17 min
    • Lecture8.3
      Find elements common in two lists 06 min
    • Lecture8.4
      Find elements common in two lists using a Hashtable/Dict 12 min
  • Plotting for exploratory data analysis (EDA) 0/0

    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.

    • Lecture9.1
      Introduction to IRIS dataset and 2D scatter plot 26 min
    • Lecture9.2
      3D scatter plot 06 min
    • Lecture9.3
      Pair plots 14 min
    • Lecture9.4
      Limitations of Pair Plots 02 min
    • Lecture9.5
      Histogram and Introduction to PDF(Probability Density Function) 17 min
    • Lecture9.6
      Univariate Analysis using PDF 06 min
    • Lecture9.7
      CDF(Cumulative Distribution Function) 15 min
    • Lecture9.8
      Mean, Variance and Standard Deviation 17 min
    • Lecture9.9
      Median 10 min
    • Lecture9.10
      Percentiles and Quantiles 09 min
    • Lecture9.11
      IQR(Inter Quartile Range) and MAD(Median Absolute Deviation) 06 min
    • Lecture9.12
      Box-plot with Whiskers 09 min
    • Lecture9.13
      Violin Plots 04 min
    • Lecture9.14
      Summarizing Plots, Univariate, Bivariate and Multivariate analysis 06 min
    • Lecture9.15
      Multivariate Probability Density, Contour Plot 09 min
    • Lecture9.16
      Exercise: Perform EDA on Haberman dataset 04 min
  • 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.

    • Lecture10.1
      Why learn it ? 04 min
    • Lecture10.2
      Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector 14 min
    • Lecture10.3
      Dot Product and Angle between 2 Vectors 14 min
    • Lecture10.4
      Projection and Unit Vector 05 min
    • Lecture10.5
      Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane 23 min
    • Lecture10.6
      Distance of a point from a Plane/Hyperplane, Half-Spaces 10 min
    • Lecture10.7
      Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D) 07 min
    • Lecture10.8
      Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D) 06 min
    • Lecture10.9
      Square ,Rectangle 06 min
    • Lecture10.10
      Hyper Cube,Hyper Cuboid 03 min
    • Lecture10.11
      Revision Questions 30 min
  • Probability and Statistics 0/32

    • Lecture11.1
      Introduction to Probability and Statistics 17 min
    • Lecture11.2
      Population and Sample 07 min
    • Lecture11.3
      Gaussian/Normal Distribution and its PDF(Probability Density Function) 27 min
    • Lecture11.4
      CDF(Cumulative Distribution function) of Gaussian/Normal distribution 11 min
    • Lecture11.5
      Symmetric distribution, Skewness and Kurtosis 24 min
    • Lecture11.6
      Standard normal variate (Z) and standardization 05 min
    • Lecture11.7
      Kernel density estimation 07 min
    • Lecture11.8
      Sampling distribution & Central Limit theorem 19 min
    • Lecture11.9
      Q-Q plot:How to test if a random variable is normally distributed or not? 23 min
    • Lecture11.10
      How distributions are used? 17 min
    • Lecture11.11
      Chebyshev’s inequality 20 min
    • Lecture11.12
      Discrete and Continuous Uniform distributions 13 min
    • Lecture11.13
      How to randomly sample data points (Uniform Distribution) 10 min
    • Lecture11.14
      Bernoulli and Binomial Distribution 11 min
    • Lecture11.15
      Log Normal Distribution 12 min
    • Lecture11.16
      Power law distribution 12 min
    • Lecture11.17
      Box cox transform 12 min
    • Lecture11.18
      Applications of non-gaussian distributions? 26 min
    • Lecture11.19
      Co-variance 14 min
    • Lecture11.20
      Pearson Correlation Coefficient 13 min
    • Lecture11.21
      Spearman Rank Correlation Coefficient 07 min
    • Lecture11.22
      Correlation vs Causation 03 min
    • Lecture11.23
      How to use correlations? 13 min
    • Lecture11.24
      Confidence interval (C.I) Introduction 08 min
    • Lecture11.25
      Computing confidence interval given the underlying distribution 11 min
    • Lecture11.26
      C.I for mean of a normal random variable 14 min
    • Lecture11.27
      Confidence interval using bootstrapping 17 min
    • Lecture11.28
      Hypothesis testing methodology, Null-hypothesis, p-value 16 min
    • Lecture11.29
      Hypothesis Testing Intution with coin toss example 27 min
    • Lecture11.30
      Resampling and permutation test 15 min
    • Lecture11.31
      K-S Test for similarity of two distributions 15 min
    • Lecture11.32
      Code Snippet K-S Test 06 min
    • Lecture11.33
      Hypothesis testing: another example 18 min
    • Lecture11.34
      Resampling and Permutation test: another example 19 min
    • Lecture11.35
      How to use hypothesis testing? 23 min
    • Lecture11.36
      Proportional Sampling 18 min
    • Lecture11.37
      Revision Questions 30 min
  • Interview Questions on Probability and statistics 0/0

    • Lecture12.1
      Questions & Answers 30 min
  • Dimensionality reduction and Visualization: 0/0

    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.

    • Lecture13.1
      What is Dimensionality reduction? 03 min
    • Lecture13.2
      Row Vector and Column Vector 05 min
    • Lecture13.3
      How to represent a data set? 04 min
    • Lecture13.4
      How to represent a dataset as a Matrix. 07 min
    • Lecture13.5
      Data Preprocessing: Feature Normalisation 20 min
    • Lecture13.6
      Mean of a data matrix 06 min
    • Lecture13.7
      Data Preprocessing: Column Standardization 16 min
    • Lecture13.8
      Co-variance of a Data Matrix 24 min
    • Lecture13.9
      MNIST dataset (784 dimensional) 20 min
    • Lecture13.10
      Code to Load MNIST Data Set 12 min
  • PCA(principal component analysis) 0/0

    • Lecture14.1
      Why learn PCA? 04 min
    • Lecture14.2
      Geometric intuition of PCA 14 min
    • Lecture14.3
      Mathematical objective function of PCA 13 min
    • Lecture14.4
      Alternative formulation of PCA: Distance minimization 10 min
    • Lecture14.5
      Eigen values and Eigen vectors (PCA): Dimensionality reduction 23 min
    • Lecture14.6
      PCA for Dimensionality Reduction and Visualization 10 min
    • Lecture14.7
      Visualize MNIST dataset 05 min
    • Lecture14.8
      Limitations of PCA 05 min
    • Lecture14.9
      PCA Code example 19 min
    • Lecture14.10
      PCA for dimensionality reduction (not-visualization) 15 min
  • (t-SNE)T-distributed Stochastic Neighbourhood Embedding 0/1

    • Lecture15.1
      What is t-SNE? 07 min
    • Lecture15.2
      Neighborhood of a point, Embedding 07 min
    • Lecture15.3
      Geometric intuition of t-SNE 09 min
    • Lecture15.4
      Crowding Problem 08 min
    • Lecture15.5
      How to apply t-SNE and interpret its output 38 min
    • Lecture15.6
      t-SNE on MNIST 07 min
    • Lecture15.7
      Code example of t-SNE 09 min
    • Lecture15.8
      Revision Questions 30 min
  • Interview Questions on Dimensionality Reduction 0/0

    • Lecture16.1
      Questions & Answers 30 min
  • Real world problem: Predict rating given product reviews on Amazon 0/17

    • Lecture17.1
      Dataset overview: Amazon Fine Food reviews(EDA) 23 min
    • Lecture17.2
      Data Cleaning: Deduplication 15 min
    • Lecture17.3
      Why convert text to a vector? 14 min
    • Lecture17.4
      Bag of Words (BoW) 18 min
    • Lecture17.5
      Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization. 15 min
    • Lecture17.6
      uni-gram, bi-gram, n-grams. 09 min
    • Lecture17.7
      tf-idf (term frequency- inverse document frequency) 22 min
    • Lecture17.8
      Why use log in IDF? 14 min
    • Lecture17.9
      Word2Vec. 16 min
    • Lecture17.10
      Avg-Word2Vec, tf-idf weighted Word2Vec 09 min
    • Lecture17.11
      Bag of Words( Code Sample) 19 min
    • Lecture17.12
      Text Preprocessing( Code Sample) 11 min
    • Lecture17.13
      Bi-Grams and n-grams (Code Sample) 05 min
    • Lecture17.14
      TF-IDF (Code Sample) 06 min
    • Lecture17.15
      Word2Vec (Code Sample) 12 min
    • Lecture17.16
      Avg-Word2Vec and TFIDF-Word2Vec (Code Sample) 02 min
    • Lecture17.17
      Assignment-2: Apply t-SNE 30 min
  • Classification And Regression Models: K-Nearest Neighbors 0/32

    • Lecture18.1
      How “Classification” works? 10 min
    • Lecture18.2
      Data matrix notation 07 min
    • Lecture18.3
      Classification vs Regression (examples) 06 min
    • Lecture18.4
      K-Nearest Neighbours Geometric intuition with a toy example 11 min
    • Lecture18.5
      Failure cases of KNN 07 min
    • Lecture18.6
      Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming 20 min
    • Lecture18.7
      Cosine Distance & Cosine Similarity 19 min
    • Lecture18.8
      How to measure the effectiveness of k-NN? 16 min
    • Lecture18.9
      Test/Evaluation time and space complexity 12 min
    • Lecture18.10
      KNN Limitations 09 min
    • Lecture18.11
      Decision surface for K-NN as K changes 23 min
    • Lecture18.12
      Overfitting and Underfitting 12 min
    • Lecture18.13
      Need for Cross validation 22 min
    • Lecture18.14
      K-fold cross validation 17 min
    • Lecture18.15
      Visualizing train, validation and test datasets 13 min
    • Lecture18.16
      How to determine overfitting and underfitting? 19 min
    • Lecture18.17
      Time based splitting 19 min
    • Lecture18.18
      k-NN for regression 05 min
    • Lecture18.19
      Weighted k-NN 08 min
    • Lecture18.20
      Voronoi diagram 04 min
    • Lecture18.21
      Binary search tree 16 min
    • Lecture18.22
      How to build a kd-tree 17 min
    • Lecture18.23
      Find nearest neighbours using kd-tree 13 min
    • Lecture18.24
      Limitations of Kd tree 09 min
    • Lecture18.25
      Extensions 03 min
    • Lecture18.26
      Hashing vs LSH 10 min
    • Lecture18.27
      LSH for cosine similarity 40 min
    • Lecture18.28
      LSH for euclidean distance 13 min
    • Lecture18.29
      Probabilistic class label 08 min
    • Lecture18.30
      Code Sample:Decision boundary . 23 min
    • Lecture18.31
      Code Sample:Cross Validation 13 min
    • Lecture18.32
      Revision Questions 30 min
  • Interview Questions on K-NN(K Nearest Neighbour) 0/0

    • Lecture19.1
      Questions & Answers 30 min
  • Classification algorithms in various situations 0/21

    • Lecture20.1
      Introduction 05 min
    • Lecture20.2
      Imbalanced vs balanced dataset 23 min
    • Lecture20.3
      Multi-class classification 12 min
    • Lecture20.4
      k-NN, given a distance or similarity matrix 09 min
    • Lecture20.5
      Train and test set differences 22 min
    • Lecture20.6
      Impact of outliers 07 min
    • Lecture20.7
      Local outlier Factor (Simple solution :Mean distance to Knn) 13 min
    • Lecture20.8
      k distance 04 min
    • Lecture20.9
      Reachability-Distance(A,B) 08 min
    • Lecture20.10
      Local reachability-density(A) 09 min
    • Lecture20.11
      Local outlier Factor(A) 21 min
    • Lecture20.12
      Impact of Scale & Column standardization 12 min
    • Lecture20.13
      Interpretability 12 min
    • Lecture20.14
      Feature Importance and Forward Feature selection 22 min
    • Lecture20.15
      Handling categorical and numerical features 24 min
    • Lecture20.16
      Handling missing values by imputation 21 min
    • Lecture20.17
      curse of dimensionality 27 min
    • Lecture20.18
      Bias-Variance tradeoff 24 min
    • Lecture20.19
      Intuitive understanding of bias-variance. 06 min
    • Lecture20.20
      Revision Questions 30 min
    • Lecture20.21
      best and wrost case of algorithm 06 min
  • Performance measurement of models 0/10

    • Lecture21.1
      Accuracy 15 min
    • Lecture21.2
      Confusion matrix, TPR, FPR, FNR, TNR 25 min
    • Lecture21.3
      Precision and recall, F1-score 10 min
    • Lecture21.4
      Receiver Operating Characteristic Curve (ROC) curve and AUC 19 min
    • Lecture21.5
      Log-loss 12 min
    • Lecture21.6
      R-Squared/Coefficient of determination 14 min
    • Lecture21.7
      Median absolute deviation (MAD) 05 min
    • Lecture21.8
      Distribution of errors 07 min
    • Lecture21.9
      Assignment-3: Apply k-Nearest Neighbor 05 min
    • Lecture21.10
      Revision Questions 30 min
  • Interview Questions on Performance Measurement Models 0/0

    • Lecture22.1
      Questions & Answers 30 min
  • Naive Bayes 0/22

    • Lecture23.1
      Conditional probability 13 min
    • Lecture23.2
      Independent vs Mutually exclusive events 06 min
    • Lecture23.3
      Bayes Theorem with examples 18 min
    • Lecture23.4
      Exercise problems on Bayes Theorem 30 min
    • Lecture23.5
      Naive Bayes algorithm 26 min
    • Lecture23.6
      Toy example: Train and test stages 26 min
    • Lecture23.7
      Naive Bayes on Text data 16 min
    • Lecture23.8
      Laplace/Additive Smoothing 24 min
    • Lecture23.9
      Log-probabilities for numerical stability 11 min
    • Lecture23.10
      Bias and Variance tradeoff 14 min
    • Lecture23.11
      Feature importance and interpretability 10 min
    • Lecture23.12
      Imbalanced data 14 min
    • Lecture23.13
      Outliers 06 min
    • Lecture23.14
      Missing values 03 min
    • Lecture23.15
      Handling Numerical features (Gaussian NB) 13 min
    • Lecture23.16
      Multiclass classification 02 min
    • Lecture23.17
      Similarity or Distance matrix 03 min
    • Lecture23.18
      Large dimensionality 02 min
    • Lecture23.19
      Best and worst cases 08 min
    • Lecture23.20
      Code example 07 min
    • Lecture23.21
      Assignment-4: Apply Naive Bayes 06 min
    • Lecture23.22
      Revision Questions 30 min
  • Logistic Regression 0/18

    • Lecture24.1
      Geometric intuition of Logistic Regression 31 min
    • Lecture24.2
      Sigmoid function: Squashing 37 min
    • Lecture24.3
      Mathematical formulation of Objective function 24 min
    • Lecture24.4
      Weight vector 11 min
    • Lecture24.5
      L2 Regularization: Overfitting and Underfitting 26 min
    • Lecture24.6
      L1 regularization and sparsity 11 min
    • Lecture24.7
      Probabilistic Interpretation: Gaussian Naive Bayes 19 min
    • Lecture24.8
      Loss minimization interpretation 24 min
    • Lecture24.9
      hyperparameters and random search 16 min
    • Lecture24.10
      Column Standardization 05 min
    • Lecture24.11
      Feature importance and Model interpretability 14 min
    • Lecture24.12
      Collinearity of features 14 min
    • Lecture24.13
      Test/Run time space and time complexity 10 min
    • Lecture24.14
      Real world cases 11 min
    • Lecture24.15
      Non-linearly separable data & feature engineering 28 min
    • Lecture24.16
      Code sample: Logistic regression, GridSearchCV, RandomSearchCV 23 min
    • Lecture24.17
      Assignment-5: Apply Logistic Regression 06 min
    • Lecture24.18
      Extensions to Generalized linear models 09 min
  • Linear Regression 0/4

    • Lecture25.1
      Geometric intuition of Linear Regression 13 min
    • Lecture25.2
      Mathematical formulation 14 min
    • Lecture25.3
      Real world Cases 08 min
    • Lecture25.4
      Code sample for Linear Regression 13 min
  • Solving Optimization Problems 0/12

    • Lecture26.1
      Differentiation 29 min
    • Lecture26.2
      Online differentiation tools 08 min
    • Lecture26.3
      Maxima and Minima 12 min
    • Lecture26.4
      Vector calculus: Grad 10 min
    • Lecture26.5
      Gradient descent: geometric intuition 19 min
    • Lecture26.6
      Learning rate 08 min
    • Lecture26.7
      Gradient descent for linear regression 08 min
    • Lecture26.8
      SGD algorithm 09 min
    • Lecture26.9
      Constrained Optimization & PCA 14 min
    • Lecture26.10
      Logistic regression formulation revisited 06 min
    • Lecture26.11
      Why L1 regularization creates sparsity? 17 min
    • Lecture26.12
      Assignment 6: Implement SGD for linear regression ​ 06 min
    • Lecture26.13
      Revision questions 30 min
  • Interview Questions on Logistic Regression and Linear Regression 0/0

    • Lecture27.1
      Questions & Answers 30 min
  • Support Vector Machines (SVM) 0/16

    • Lecture28.1
      Geometric Intution 20 min
    • Lecture28.2
      Mathematical derivation 32 min
    • Lecture28.3
      Why we take values +1 and and -1 for Support vector planes 09 min
    • Lecture28.4
      Loss function (Hinge Loss) based interpretation 18 min
    • Lecture28.5
      Dual form of SVM formulation 16 min
    • Lecture28.6
      kernel trick 10 min
    • Lecture28.7
      Polynomial Kernel 11 min
    • Lecture28.8
      RBF-Kernel 21 min
    • Lecture28.9
      Domain specific Kernels 06 min
    • Lecture28.10
      Train and run time complexities 08 min
    • Lecture28.11
      nu-SVM: control errors and support vectors 06 min
    • Lecture28.12
      SVM Regression 08 min
    • Lecture28.13
      Cases 09 min
    • Lecture28.14
      Code Sample 14 min
    • Lecture28.15
      Assignment-7: Apply SVM ​ 04 min
    • Lecture28.16
      Revision Questions 30 min
  • Interview Questions on Support Vector Machine 0/0

    • Lecture29.1
      Questions & Answers 30 min
  • Decision Trees 0/16

    • Lecture30.1
      Geometric Intuition of decision tree: Axis parallel hyperplanes 17 min
    • Lecture30.2
      Sample Decision tree 08 min
    • Lecture30.3
      Building a decision Tree:Entropy 19 min
    • Lecture30.4
      Building a decision Tree:Information Gain 10 min
    • Lecture30.5
      Building a decision Tree: Gini Impurity 07 min
    • Lecture30.6
      Building a decision Tree: Constructing a DT 21 min
    • Lecture30.7
      Building a decision Tree: Splitting numerical features 08 min
    • Lecture30.8
      Feature standardization 04 min
    • Lecture30.9
      Building a decision Tree:Categorical features with many possible values 07 min
    • Lecture30.10
      Overfitting and Underfitting 08 min
    • Lecture30.11
      Train and Run time complexity 07 min
    • Lecture30.12
      Regression using Decision Trees 09 min
    • Lecture30.13
      Cases 12 min
    • Lecture30.14
      Code Samples 09 min
    • Lecture30.15
      Assignment-8: Apply Decision Trees ​ 03 min
    • Lecture30.16
      Revision Questions 30 min
  • Interview Questions on decision Trees 0/0

    • Lecture31.1
      Questions & Answers 30 min
  • Ensemble Models 0/20

    • Lecture32.1
      What are ensembles? 06 min
    • Lecture32.2
      Bootstrapped Aggregation (Bagging) Intuition 17 min
    • Lecture32.3
      Random Forest and their construction 15 min
    • Lecture32.4
      Bias-Variance tradeoff 07 min
    • Lecture32.5
      Train and run time complexity 09 min
    • Lecture32.6
      Bagging:Code Sample 04 min
    • Lecture32.7
      Extremely randomized trees 08 min
    • Lecture32.8
      Random Forest :Cases 06 min
    • Lecture32.9
      Boosting Intuition 17 min
    • Lecture32.10
      Residuals, Loss functions and gradients 13 min
    • Lecture32.11
      Gradient Boosting 10 min
    • Lecture32.12
      Regularization by Shrinkage 08 min
    • Lecture32.13
      Train and Run time complexity 06 min
    • Lecture32.14
      XGBoost: Boosting + Randomization 14 min
    • Lecture32.15
      AdaBoost: geometric intuition 07 min
    • Lecture32.16
      Stacking models 22 min
    • Lecture32.17
      Cascading classifiers 15 min
    • Lecture32.18
      Kaggle competitions vs Real world 09 min
    • Lecture32.19
      Assignment-9: Apply Random Forests & GBDT ​ 04 min
    • Lecture32.20
      Revision Questions 30 min
  • Featurization and Feature engineering. 0/18

    • Lecture33.1
      Introduction 17 min
    • Lecture33.2
      Moving window for Time Series Data 25 min
    • Lecture33.3
      Fourier decomposition 22 min
    • Lecture33.4
      Deep learning features: LSTM 08 min
    • Lecture33.5
      Image histogram 23 min
    • Lecture33.6
      Keypoints: SIFT. 10 min
    • Lecture33.7
      Deep learning features: CNN 04 min
    • Lecture33.8
      Relational data 10 min
    • Lecture33.9
      Graph data 12 min
    • Lecture33.10
      Indicator variables 07 min
    • Lecture33.11
      Feature binning 14 min
    • Lecture33.12
      Interaction variables 08 min
    • Lecture33.13
      Mathematical transforms 04 min
    • Lecture33.14
      Model specific featurizations 09 min
    • Lecture33.15
      Feature orthogonality 11 min
    • Lecture33.16
      Domain specific featurizations 04 min
    • Lecture33.17
      Feature slicing 10 min
    • Lecture33.18
      Kaggle Winners solutions 07 min
  • Miscellaneous Topics 0/10

    • Lecture34.1
      Calibration of Models:Need for calibration 08 min
    • Lecture34.2
      Productionization and deployment of Machine Learning Models 30 min
    • Lecture34.3
      Calibration Plots. 17 min
    • Lecture34.4
      Platt’s Calibration/Scaling. 08 min
    • Lecture34.5
      Isotonic Regression 11 min
    • Lecture34.6
      Code Samples 04 min
    • Lecture34.7
      Modeling in the presence of outliers: RANSAC 13 min
    • Lecture34.8
      Productionizing models 17 min
    • Lecture34.9
      Retraining models periodically. 08 min
    • Lecture34.10
      A/B testing. 22 min
    • Lecture34.11
      Data Science Life cycle 17 min
    • Lecture34.12
      VC dimension 22 min
  • Unsupervised learning/Clustering 0/14

    • Lecture35.1
      What is Clustering? 10 min
    • Lecture35.2
      Unsupervised learning 04 min
    • Lecture35.3
      Applications 16 min
    • Lecture35.4
      Metrics for Clustering 13 min
    • Lecture35.5
      K-Means: Geometric intuition, Centroids 08 min
    • Lecture35.6
      K-Means: Mathematical formulation: Objective function 11 min
    • Lecture35.7
      K-Means Algorithm. 11 min
    • Lecture35.8
      How to initialize: K-Means++ 24 min
    • Lecture35.9
      Failure cases/Limitations 11 min
    • Lecture35.10
      K-Medoids 19 min
    • Lecture35.11
      Determining the right K 05 min
    • Lecture35.12
      Code Samples 07 min
    • Lecture35.13
      Time and space complexity 04 min
    • Lecture35.14
      Assignment-10: Apply K-means, Agglomerative, DBSCAN clustering algorithms ​ 05 min
  • Hierarchical clustering Technique 0/7

    • Lecture36.1
      Agglomerative & Divisive, Dendrograms 13 min
    • Lecture36.2
      Agglomerative Clustering 09 min
    • Lecture36.3
      Proximity methods: Advantages and Limitations. 24 min
    • Lecture36.4
      Time and Space Complexity 04 min
    • Lecture36.5
      Limitations of Hierarchical Clustering 05 min
    • Lecture36.6
      Code sample 03 min
    • Lecture36.7
      Assignment-10: Apply K-means, Agglomerative, DBSCAN clustering algorithms 03 min
  • DBSCAN (Density based clustering) Technique 0/11

    • Lecture37.1
      Density based clustering 05 min
    • Lecture37.2
      MinPts and Eps: Density 06 min
    • Lecture37.3
      Core, Border and Noise points 07 min
    • Lecture37.4
      Density edge and Density connected points. 06 min
    • Lecture37.5
      DBSCAN Algorithm 11 min
    • Lecture37.6
      Hyper Parameters: MinPts and Eps 10 min
    • Lecture37.7
      Advantages and Limitations of DBSCAN 10 min
    • Lecture37.8
      Time and Space Complexity 03 min
    • Lecture37.9
      Code samples. 03 min
    • Lecture37.10
      Assignment-10: Apply K-means, Agglomerative, DBSCAN clustering algorithms 03 min
    • Lecture37.11
      Revision Questions 30 min
  • Recommender Systems and Matrix Factorization 0/16

    • Lecture38.1
      Problem formulation: Movie reviews 23 min
    • Lecture38.2
      Content based vs Collaborative Filtering 11 min
    • Lecture38.3
      Similarity based Algorithms 16 min
    • Lecture38.4
      Matrix Factorization: PCA, SVD 23 min
    • Lecture38.5
      Matrix Factorization: NMF 03 min
    • Lecture38.6
      Matrix Factorization for Collaborative filtering 23 min
    • Lecture38.7
      Matrix Factorization for feature engineering 09 min
    • Lecture38.8
      Clustering as MF 21 min
    • Lecture38.9
      Hyperparameter tuning 10 min
    • Lecture38.10
      Matrix Factorization for recommender systems: Netflix Prize Solution 30 min
    • Lecture38.11
      Cold Start problem 06 min
    • Lecture38.12
      Word vectors as MF 20 min
    • Lecture38.13
      Eigen-Faces 15 min
    • Lecture38.14
      Code example. 11 min
    • Lecture38.15
      Assignment-11: Apply Truncated SVD ​ 07 min
    • Lecture38.16
      Revision Questions 30 min
  • Interview Questions on Recommender Systems and Matrix Factorization. 0/0

    • Lecture39.1
      Questions & Answers 30 min
  • Case study 2: Stackoverflow tag predictor 0/17

    • Lecture40.1
      Business/Real world problem 10 min
    • Lecture40.2
      Business objectives and constraints 05 min
    • Lecture40.3
      Mapping to an ML problem: Data overview 04 min
    • Lecture40.4
      Mapping to an ML problem:ML problem formulation. 05 min
    • Lecture40.5
      Mapping to an ML problem:Performance metrics. 21 min
    • Lecture40.6
      Hamming loss 07 min
    • Lecture40.7
      EDA:Data Loading 13 min
    • Lecture40.8
      EDA:Analysis of tags 11 min
    • Lecture40.9
      EDA:Data Preprocessing 11 min
    • Lecture40.10
      Data Modeling : Multi label Classification 18 min
    • Lecture40.11
      Data preparation. 08 min
    • Lecture40.12
      Train-Test Split 02 min
    • Lecture40.13
      Featurization 06 min
    • Lecture40.14
      Logistic regression: One VS Rest 07 min
    • Lecture40.15
      Sampling data and tags+Weighted models. 04 min
    • Lecture40.16
      Logistic regression revisited 04 min
    • Lecture40.17
      Why not use advanced techniques 03 min
    • Lecture40.18
      Assignments. 05 min
  • Case Study 3: Quora question Pair Similarity Problem 0/16

    • Lecture41.1
      Business/Real world problem : Problem definition 06 min
    • Lecture41.2
      Business objectives and constraints. 05 min
    • Lecture41.3
      Mapping to an ML problem : Data overview 05 min
    • Lecture41.4
      Mapping to an ML problem : ML problem and performance metric. 04 min
    • Lecture41.5
      Mapping to an ML problem : Train-test split 05 min
    • Lecture41.6
      EDA: Basic Statistics. 07 min
    • Lecture41.7
      EDA: Basic Feature Extraction 06 min
    • Lecture41.8
      EDA: Text Preprocessing 10 min
    • Lecture41.9
      EDA: Advanced Feature Extraction 31 min
    • Lecture41.10
      EDA: Feature analysis. 09 min
    • Lecture41.11
      EDA: Data Visualization: T-SNE. 03 min
    • Lecture41.12
      EDA: TF-IDF weighted Word2Vec featurization. 06 min
    • Lecture41.13
      ML Models :Loading Data 06 min
    • Lecture41.14
      ML Models: Random Model 07 min
    • Lecture41.15
      ML Models : Logistic Regression and Linear SVM 11 min
    • Lecture41.16
      ML Models : XGBoost 06 min
    • Lecture41.17
      Assignments 04 min
  • Case Study 4: Amazon fashion discovery engine 0/27

    • Lecture42.1
      Problem Statement: Recommend similar apparel products in e-commerce using product descriptions and Images 12 min
    • Lecture42.2
      Plan of action 07 min
    • Lecture42.3
      Amazon product advertising API 10 min
    • Lecture42.4
      Data folders and paths 06 min
    • Lecture42.5
      Overview of the data and Terminology 12 min
    • Lecture42.6
      Data cleaning and understanding:Missing data in various features 22 min
    • Lecture42.7
      Understand duplicate rows 09 min
    • Lecture42.8
      Remove duplicates : Part 1 12 min
    • Lecture42.9
      Remove duplicates: Part 2 15 min
    • Lecture42.10
      Text Pre-Processing: Tokenization and Stop-word removal 10 min
    • Lecture42.11
      Stemming 04 min
    • Lecture42.12
      Text based product similarity :Converting text to an n-D vector: bag of words 14 min
    • Lecture42.13
      Code for bag of words based product similarity 26 min
    • Lecture42.14
      TF-IDF: featurizing text based on word-importance 17 min
    • Lecture42.15
      Code for TF-IDF based product similarity 10 min
    • Lecture42.16
      Code for IDF based product similarity 09 min
    • Lecture42.17
      Text Semantics based product similarity: Word2Vec(featurizing text based on semantic similarity) 19 min
    • Lecture42.18
      Code for Average Word2Vec product similarity 15 min
    • Lecture42.19
      TF-IDF weighted Word2Vec 09 min
    • Lecture42.20
      Code for IDF weighted Word2Vec product similarity 06 min
    • Lecture42.21
      Weighted similarity using brand and color 09 min
    • Lecture42.22
      Code for weighted similarity 07 min
    • Lecture42.23
      Building a real world solution 05 min
    • Lecture42.24
      Deep learning based visual product similarity:ConvNets: How to featurize an image: edges, shapes, parts 11 min
    • Lecture42.25
      Using Keras + Tensorflow to extract features 08 min
    • Lecture42.26
      Visual similarity based product similarity 06 min
    • Lecture42.27
      Measuring goodness of our solution :A/B testing 07 min
    • Lecture42.28
      Exercise :Build a weighted Nearest neighbor model using Visual, Text, Brand and Color 09 min
  • Case Study 5: Microsoft Malware Detection 0/20

    • Lecture43.1
      Business/real world problem :Problem definition 06 min
    • Lecture43.2
      Business/real world problem :Objectives and constraints 07 min
    • Lecture43.3
      Machine Learning problem mapping :Data overview. 13 min
    • Lecture43.4
      Machine Learning problem mapping :ML problem 12 min
    • Lecture43.5
      Machine Learning problem mapping :Train and test splitting 04 min
    • Lecture43.6
      Exploratory Data Analysis :Class distribution. 03 min
    • Lecture43.7
      Exploratory Data Analysis :Feature extraction from byte files 08 min
    • Lecture43.8
      Exploratory Data Analysis :Multivariate analysis of features from byte files 03 min
    • Lecture43.9
      Exploratory Data Analysis :Train-Test class distribution 03 min
    • Lecture43.10
      ML models – using byte files only :Random Model 11 min
    • Lecture43.11
      k-NN 07 min
    • Lecture43.12
      Logistic regression 05 min
    • Lecture43.13
      Random Forest and Xgboost 07 min
    • Lecture43.14
      ASM Files :Feature extraction & Multiprocessing. 11 min
    • Lecture43.15
      File-size feature 02 min
    • Lecture43.16
      Univariate analysis 03 min
    • Lecture43.17
      t-SNE analysis. 02 min
    • Lecture43.18
      ML models on ASM file features 07 min
    • Lecture43.19
      Models on all features :t-SNE 02 min
    • Lecture43.20
      Models on all features :RandomForest and Xgboost 04 min
    • Lecture43.21
      Assignments. 04 min
  • Case study 6:Netflix Movie Recommendation System 0/27

    • Lecture44.1
      Business/Real world problem:Problem definition 06 min
    • Lecture44.2
      Objectives and constraints 07 min
    • Lecture44.3
      Mapping to an ML problem:Data overview. 04 min
    • Lecture44.4
      Mapping to an ML problem:ML problem formulation 05 min
    • Lecture44.5
      Exploratory Data Analysis:Data preprocessing 07 min
    • Lecture44.6
      Exploratory Data Analysis:Temporal Train-Test split. 06 min
    • Lecture44.7
      Exploratory Data Analysis:Preliminary data analysis. 15 min
    • Lecture44.8
      Exploratory Data Analysis:Sparse matrix representation 08 min
    • Lecture44.9
      Exploratory Data Analysis:Average ratings for various slices 08 min
    • Lecture44.10
      Exploratory Data Analysis:Cold start problem 05 min
    • Lecture44.11
      Computing Similarity matrices:User-User similarity matrix 20 min
    • Lecture44.12
      Computing Similarity matrices:Movie-Movie similarity 06 min
    • Lecture44.13
      Computing Similarity matrices:Does movie-movie similarity work? 06 min
    • Lecture44.14
      ML Models:Surprise library 06 min
    • Lecture44.15
      Overview of the modelling strategy. 08 min
    • Lecture44.16
      Data Sampling. 05 min
    • Lecture44.17
      Google drive with intermediate files 02 min
    • Lecture44.18
      Featurizations for regression. 11 min
    • Lecture44.19
      Data transformation for Surprise. 02 min
    • Lecture44.20
      Xgboost with 13 features 06 min
    • Lecture44.21
      Surprise Baseline model. 09 min
    • Lecture44.22
      Xgboost + 13 features +Surprise baseline model 04 min
    • Lecture44.23
      Surprise KNN predictors 15 min
    • Lecture44.24
      Matrix Factorization models using Surprise 05 min
    • Lecture44.25
      SVD ++ with implicit feedback 11 min
    • Lecture44.26
      Final models with all features and predictors. 04 min
    • Lecture44.27
      Comparison between various models. 04 min
    • Lecture44.28
      Assignments. 04 min
  • Case Study 7: Personalized Cancer Diagnosis 0/21

    • Lecture45.1
      Business/Real world problem : Overview 13 min
    • Lecture45.2
      Business objectives and constraints. 11 min
    • Lecture45.3
      ML problem formulation :Data 05 min
    • Lecture45.4
      ML problem formulation: Mapping real world to ML problem. 19 min
    • Lecture45.5
      ML problem formulation :Train, CV and Test data construction 04 min
    • Lecture45.6
      Exploratory Data Analysis:Reading data & preprocessing 07 min
    • Lecture45.7
      Exploratory Data Analysis:Distribution of Class-labels 07 min
    • Lecture45.8
      Exploratory Data Analysis: “Random” Model 19 min
    • Lecture45.9
      Univariate Analysis:Gene feature 34 min
    • Lecture45.10
      Univariate Analysis:Variation Feature 19 min
    • Lecture45.11
      Univariate Analysis:Text feature 15 min
    • Lecture45.12
      Machine Learning Models:Data preparation 08 min
    • Lecture45.13
      Baseline Model: Naive Bayes 23 min
    • Lecture45.14
      K-Nearest Neighbors Classification 09 min
    • Lecture45.15
      Logistic Regression with class balancing 10 min
    • Lecture45.16
      Logistic Regression without class balancing 04 min
    • Lecture45.17
      Linear-SVM. 06 min
    • Lecture45.18
      Random-Forest with one-hot encoded features 07 min
    • Lecture45.19
      Random-Forest with response-coded features 06 min
    • Lecture45.20
      Stacking Classifier 08 min
    • Lecture45.21
      Majority Voting classifier 05 min
    • Lecture45.22
      Assignments. 05 min
  • Case study 8:Taxi demand prediction in New York City 0/28

    • Lecture46.1
      Business/Real world problem Overview 09 min
    • Lecture46.2
      Objectives and Constraints 11 min
    • Lecture46.3
      Mapping to ML problem :Data 08 min
    • Lecture46.4
      Mapping to ML problem :dask dataframes 11 min
    • Lecture46.5
      Mapping to ML problem :Fields/Features. 06 min
    • Lecture46.6
      Mapping to ML problem :Time series forecasting/Regression 08 min
    • Lecture46.7
      Mapping to ML problem :Performance metrics 06 min
    • Lecture46.8
      Data Cleaning :Latitude and Longitude data 04 min
    • Lecture46.9
      Data Cleaning :Trip Duration. 07 min
    • Lecture46.10
      Data Cleaning :Speed. 05 min
    • Lecture46.11
      Data Cleaning :Distance. 02 min
    • Lecture46.12
      Data Cleaning :Fare 06 min
    • Lecture46.13
      Data Cleaning :Remove all outliers/erroneous points 03 min
    • Lecture46.14
      Data Preparation:Clustering/Segmentation 19 min
    • Lecture46.15
      Data Preparation:Time binning 05 min
    • Lecture46.16
      Data Preparation:Smoothing time-series data. 05 min
    • Lecture46.17
      Data Preparation:Smoothing time-series data cont.. 02 min
    • Lecture46.18
      Data Preparation: Time series and Fourier transforms. 13 min
    • Lecture46.19
      Ratios and previous-time-bin values 09 min
    • Lecture46.20
      Simple moving average 08 min
    • Lecture46.21
      Weighted Moving average. 05 min
    • Lecture46.22
      Exponential weighted moving average 06 min
    • Lecture46.23
      Results. 04 min
    • Lecture46.24
      Regression models :Train-Test split & Features 08 min
    • Lecture46.25
      Linear regression. 03 min
    • Lecture46.26
      Random Forest regression 04 min
    • Lecture46.27
      Xgboost Regression 02 min
    • Lecture46.28
      Model comparison 06 min
    • Lecture46.29
      Assignment. 06 min
  • Deep Learning:Neural Networks. 0/14

    • Lecture47.1
      History of Neural networks and Deep Learning. 25 min
    • Lecture47.2
      How Biological Neurons work? 10 min
    • Lecture47.3
      Growth of biological neural networks 16 min
    • Lecture47.4
      Diagrammatic representation: Logistic Regression and Perceptron 17 min
    • Lecture47.5
      Multi-Layered Perceptron (MLP). 23 min
    • Lecture47.6
      Notation 18 min
    • Lecture47.7
      Training a single-neuron model. 28 min
    • Lecture47.8
      Training an MLP: Chain Rule 40 min
    • Lecture47.9
      Training an MLP:Memoization 14 min
    • Lecture47.10
      Backpropagation. 26 min
    • Lecture47.11
      Activation functions 17 min
    • Lecture47.12
      Vanishing Gradient problem. 23 min
    • Lecture47.13
      Bias-Variance tradeoff. 10 min
    • Lecture47.14
      Decision surfaces: Playground 15 min
  • Deep Learning: Deep Multi-layer perceptrons 0/21

    • Lecture48.1
      Deep Multi-layer perceptrons:1980s to 2010s 16 min
    • Lecture48.2
      Dropout layers & Regularization. 21 min
    • Lecture48.3
      Rectified Linear Units (ReLU). 28 min
    • Lecture48.4
      Weight initialization. 24 min
    • Lecture48.5
      Batch Normalization. 21 min
    • Lecture48.6
      Optimizers:Hill-descent analogy in 2D 19 min
    • Lecture48.7
      Optimizers:Hill descent in 3D and contours. 13 min
    • Lecture48.8
      SGD Recap 18 min
    • Lecture48.9
      Batch SGD with momentum. 25 min
    • Lecture48.10
      Nesterov Accelerated Gradient (NAG) 08 min
    • Lecture48.11
      Optimizers:AdaGrad 15 min
    • Lecture48.12
      Optimizers : Adadelta andRMSProp 10 min
    • Lecture48.13
      Adam 11 min
    • Lecture48.14
      Which algorithm to choose when? 05 min
    • Lecture48.15
      Gradient Checking and clipping 10 min
    • Lecture48.16
      Softmax and Cross-entropy for multi-class classification. 25 min
    • Lecture48.17
      How to train a Deep MLP? 08 min
    • Lecture48.18
      Auto Encoders. 27 min
    • Lecture48.19
      Word2Vec :CBOW 19 min
    • Lecture48.20
      Word2Vec: Skip-gram 14 min
    • Lecture48.21
      Word2Vec :Algorithmic Optimizations. 12 min
  • Deep Learning: Tensorflow and Keras. 0/14

    • Lecture49.1
      Tensorflow and Keras overview 23 min
    • Lecture49.2
      GPU vs CPU for Deep Learning. 23 min
    • Lecture49.3
      Google Colaboratory. 05 min
    • Lecture49.4
      Install TensorFlow 06 min
    • Lecture49.5
      Online documentation and tutorials 06 min
    • Lecture49.6
      Softmax Classifier on MNIST dataset. 32 min
    • Lecture49.7
      MLP: Initialization 11 min
    • Lecture49.8
      Model 1: Sigmoid activation 22 min
    • Lecture49.9
      Model 2: ReLU activation. 06 min
    • Lecture49.10
      Model 3: Batch Normalization. 08 min
    • Lecture49.11
      Model 4 : Dropout. 05 min
    • Lecture49.12
      MNIST classification in Keras. 18 min
    • Lecture49.13
      Hyperparameter tuning in Keras. 11 min
    • Lecture49.14
      Exercise: Try different MLP architectures on MNIST dataset. 05 min
  • Deep Learning: Convolutional Neural Nets. 0/19

    • Lecture50.1
      Biological inspiration: Visual Cortex 17 min
    • Lecture50.2
      Convolution:Edge Detection on images. 28 min
    • Lecture50.3
      Convolution:Padding and strides 19 min
    • Lecture50.4
      Convolution over RGB images. 11 min
    • Lecture50.5
      Convolutional layer. 23 min
    • Lecture50.6
      Max-pooling. 12 min
    • Lecture50.7
      CNN Training: Optimization 09 min
    • Lecture50.8
      Example CNN: LeNet [1998] 11 min
    • Lecture50.9
      ImageNet dataset. 06 min
    • Lecture50.10
      Data Augmentation. 07 min
    • Lecture50.11
      Convolution Layers in Keras 17 min
    • Lecture50.12
      AlexNet 13 min
    • Lecture50.13
      VGGNet 11 min
    • Lecture50.14
      Residual Network. 22 min
    • Lecture50.15
      Inception Network. 19 min
    • Lecture50.16
      What is Transfer learning. 23 min
    • Lecture50.17
      Code example: Cats vs Dogs. 15 min
    • Lecture50.18
      Code Example: MNIST dataset. 06 min
    • Lecture50.19
      Assignment: Try various CNN networks on MNIST dataset. 04 min
  • Deep Learning: Long Short-term memory (LSTMs) 0/11

    • Lecture51.1
      Why RNNs? 23 min
    • Lecture51.2
      Recurrent Neural Network. 29 min
    • Lecture51.3
      Training RNNs: Backprop. 16 min
    • Lecture51.4
      Types of RNNs. 14 min
    • Lecture51.5
      Need for LSTM/GRU. 10 min
    • Lecture51.6
      LSTM. 34 min
    • Lecture51.7
      GRUs. 07 min
    • Lecture51.8
      Deep RNN. 07 min
    • Lecture51.9
      Bidirectional RNN. 12 min
    • Lecture51.10
      Code example : IMDB Sentiment classification 33 min
    • Lecture51.11
      Exercise: Amazon Fine Food reviews LSTM model. 04 min
  • Interview Questions on Deep Learning 0/1

    • Lecture52.1
      Questions and Answers 30 min
  • Case Study 9: Self Driving Car 0/13

    • Lecture53.1
      Self Driving Car :Problem definition. 14 min
    • Lecture53.2
      Datasets. 09 min
    • Lecture53.3
      Data understanding & Analysis :Files and folders. 04 min
    • Lecture53.4
      Dash-cam images and steering angles. 05 min
    • Lecture53.5
      Split the dataset: Train vs Test 03 min
    • Lecture53.6
      EDA: Steering angles 06 min
    • Lecture53.7
      Mean Baseline model: simple 05 min
    • Lecture53.8
      Deep-learning model:Deep Learning for regression: CNN, CNN+RNN 10 min
    • Lecture53.9
      Batch load the dataset. 06 min
    • Lecture53.10
      NVIDIA’s end to end CNN model. 18 min
    • Lecture53.11
      Train the model. 13 min
    • Lecture53.12
      Test and visualize the output. 11 min
    • Lecture53.13
      Extensions. 05 min
    • Lecture53.14
      Assignment. 03 min
  • Case Study 10: Music Generation using Deep-Learning 0/11

    • Lecture54.1
      Real-world problem 15 min
    • Lecture54.2
      Music representation 17 min
    • Lecture54.3
      Char-RNN with abc-notation :Char-RNN model 23 min
    • Lecture54.4
      Char-RNN with abc-notation :Data preparation. 40 min
    • Lecture54.5
      Char-RNN with abc-notation:Many to Many RNN ,TimeDistributed-Dense layer 18 min
    • Lecture54.6
      Char-RNN with abc-notation : State full RNN 11 min
    • Lecture54.7
      Char-RNN with abc-notation :Model architecture,Model training. 13 min
    • Lecture54.8
      Char-RNN with abc-notation :Music generation. 11 min
    • Lecture54.9
      Char-RNN with abc-notation :Generate tabla music 03 min
    • Lecture54.10
      MIDI music generation. 04 min
    • Lecture54.11
      Survey blog: 05 min
  • Case Study 11: Human Activity Recognition 0/8

    • Lecture55.1
      Human Activity Recognition Problem definition 09 min
    • Lecture55.2
      Dataset understanding 22 min
    • Lecture55.3
      Data cleaning & preprocessing 04 min
    • Lecture55.4
      EDA:Univariate analysis. 05 min
    • Lecture55.5
      EDA:Data visualization using t-SNE 05 min
    • Lecture55.6
      Classical ML models. 13 min
    • Lecture55.7
      Deep-learning Model. 15 min
    • Lecture55.8
      Exercise: Build deeper LSTM models and hyper-param tune them 03 min
  • Facebook Friend Recommendation using Graph Mining 0/19

    • Lecture56.1
      Problem definition. 06 min
    • Lecture56.2
      Overview of Graphs: node/vertex, edge/link, directed-edge, path. 11 min
    • Lecture56.3
      Data format & Limitations. 09 min
    • Lecture56.4
      Mapping to a supervised classification problem. 09 min
    • Lecture56.5
      Business constraints & Metrics. 07 min
    • Lecture56.6
      EDA:Basic Stats 01 hour 01 min
    • Lecture56.7
      EDA:Follower and following stats. 12 min
    • Lecture56.8
      EDA:Binary Classification Task 16 min
    • Lecture56.9
      EDA:Train and test split. 39 min
    • Lecture56.10
      Feature engineering on Graphs:Jaccard & Cosine Similarities 15 min
    • Lecture56.11
      PageRank 14 min
    • Lecture56.12
      Shortest Path 04 min
    • Lecture56.13
      Connected-components 12 min
    • Lecture56.14
      Adar Index 12 min
    • Lecture56.15
      Kartz Centrality 06 min
    • Lecture56.16
      HITS Score 10 min
    • Lecture56.17
      SVD 11 min
    • Lecture56.18
      Weight features 06 min
    • Lecture56.19
      Modeling 10 min
  • SQL 0/27

    • Lecture57.1
      Introduction to Databases 21 min
    • Lecture57.2
      Why SQL? 30 min
    • Lecture57.3
      Execution of an SQL statement. 07 min
    • Lecture57.4
      IMDB dataset 12 min
    • Lecture57.5
      Installing MySQL 11 min
    • Lecture57.6
      Load IMDB data. 04 min
    • Lecture57.7
      USE, DESCRIBE, SHOW TABLES 15 min
    • Lecture57.8
      SELECT 20 min
    • Lecture57.9
      LIMIT, OFFSET 10 min
    • Lecture57.10
      ORDER BY 06 min
    • Lecture57.11
      DISTINCT 10 min
    • Lecture57.12
      WHERE, Comparison operators, NULL 13 min
    • Lecture57.13
      Logical Operators 27 min
    • Lecture57.14
      Aggregate Functions: COUNT, MIN, MAX, AVG, SUM 08 min
    • Lecture57.15
      GROUP BY 13 min
    • Lecture57.16
      HAVING 12 min
    • Lecture57.17
      Order of keywords. 04 min
    • Lecture57.18
      Join and Natural Join 12 min
    • Lecture57.19
      Inner, Left, Right and Outer joins. 23 min
    • Lecture57.20
      Sub Queries/Nested Queries/Inner Queries 24 min
    • Lecture57.21
      DML:INSERT 07 min
    • Lecture57.22
      DML:UPDATE , DELETE 06 min
    • Lecture57.23
      DDL:CREATE TABLE 12 min
    • Lecture57.24
      DDL:ALTER: ADD, MODIFY, DROP 04 min
    • Lecture57.25
      DDL:DROP TABLE, TRUNCATE, DELETE 03 min
    • Lecture57.26
      Data Control Language: GRANT, REVOKE 10 min
    • Lecture57.27
      Learning resources 03 min
  • Case Studies 0/1

    • Lecture58.1
      AD-Click Predicition
  • Interview Questions 0/2

    • Lecture59.1
      Revision Questions 30 min
    • Lecture59.2
      Questions 30 min
    • Lecture59.3
      External resources for Interview Questions 30 min
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