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Applied Machine Learning Online Course

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How to utilise Appliedaicourse 0/2
- Lecture1.1
  
  How to Learn from Appliedaicourse 35 min
- Lecture1.2
  
  How the Job Guarantee program works 16 min
Python for Data Science Introduction 1/11
- 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) 0/16

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 0/11

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 0/37
- 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 05 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 0/1
- Lecture12.1
  
  Questions & Answers 30 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.
- 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) 0/10
- 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 0/8
- 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 0/1
- 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) 0/1
- 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 0/1
- 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 0/13
- 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 0/1
- 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 0/1
- 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 0/1
- 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 Tree :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 0/12
- 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. 0/1
- Lecture39.1
  
  Questions & Answers 30 min
Case study 2: Stackoverflow tag predictor 0/18
- 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 0/17
- 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 0/28
- 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 0/21
- 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 0/28
- 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 0/22
- 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 0/29
- 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 0/14
- 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 1/1
- Lecture58.1
  
  AD-Click Predicition
Interview Questions 0/3
- Lecture59.1
  
  Revision Questions 30 min
- Lecture59.2
  
  Questions 30 min
- Lecture59.3
  
  External resources for Interview Questions 30 min

Machine Learning Course

AD-Click Predicition - www.FreeCoursesOnline.Me [FCO]

How to utilise Appliedaicourse 0/2

Python for Data Science Introduction 1/11

Python for Data Science: Data Structures 0/6

Python for Data Science: Functions 0/10

Python for Data Science: Numpy 0/2

Python for Data Science: Matplotlib 0/1

Python for Data Science: Pandas 0/3

Python for Data Science: Computational Complexity 0/4

Plotting for exploratory data analysis (EDA) 0/16

Linear Algebra 0/11

Probability and Statistics 0/37

Interview Questions on Probability and statistics 0/1

Dimensionality reduction and Visualization: 0/10

PCA(principal component analysis) 0/10

(t-SNE)T-distributed Stochastic Neighbourhood Embedding 0/8

Interview Questions on Dimensionality Reduction 0/1

Real world problem: Predict rating given product reviews on Amazon 0/17

Classification And Regression Models: K-Nearest Neighbors 0/32

Interview Questions on K-NN(K Nearest Neighbour) 0/1

Classification algorithms in various situations 0/21

Performance measurement of models 0/10

Interview Questions on Performance Measurement Models 0/1

Naive Bayes 0/22

Logistic Regression 0/18

Linear Regression 0/4

Solving Optimization Problems 0/13

Interview Questions on Logistic Regression and Linear Regression 0/1

Support Vector Machines (SVM) 0/16

Interview Questions on Support Vector Machine 0/1

Decision Trees 0/16

Interview Questions on decision Trees 0/1

Ensemble Models 0/20

Featurization and Feature engineering. 0/18

Miscellaneous Topics 0/12

Unsupervised learning/Clustering 0/14

Hierarchical clustering Technique 0/7

DBSCAN (Density based clustering) Technique 0/11

Recommender Systems and Matrix Factorization 0/16

Interview Questions on Recommender Systems and Matrix Factorization. 0/1

Case study 2: Stackoverflow tag predictor 0/18

Case Study 3: Quora question Pair Similarity Problem 0/17

Case Study 4: Amazon fashion discovery engine 0/28

Case Study 5: Microsoft Malware Detection 0/21

Case study 6:Netflix Movie Recommendation System 0/28

Case Study 7: Personalized Cancer Diagnosis 0/22

Case study 8:Taxi demand prediction in New York City 0/29

Deep Learning:Neural Networks. 0/14

Deep Learning: Deep Multi-layer perceptrons 0/21

Deep Learning: Tensorflow and Keras. 0/14

Deep Learning: Convolutional Neural Nets. 0/19

Deep Learning: Long Short-term memory (LSTMs) 0/11

Interview Questions on Deep Learning 0/1

Case Study 9: Self Driving Car 0/14

Case Study 10: Music Generation using Deep-Learning 0/11

Case Study 11: Human Activity Recognition 0/8

Facebook Friend Recommendation using Graph Mining 0/19

SQL 0/27

Case Studies 1/1

Interview Questions 0/3