How to utilise Appliedaicourse 0/0
- How to Learn from Appliedaicourse 35 minLecture1.1
Python for Data Science Introduction 0/1
- Python, Anaconda and relevant packages installations 23 minLecture2.1
- Why learn Python? 04 minLecture2.2
- Keywords and identifiers 06 minLecture2.3
- comments, indentation and statements 09 minLecture2.4
- Variables and data types in Python 32 minLecture2.5
- Standard Input and Output 07 minLecture2.6
- Operators 14 minLecture2.7
- Control flow: if else 10 minLecture2.8
- Control flow: while loop 16 minLecture2.9
- Control flow: for loop 15 minLecture2.10
- Control flow: break and continue 10 minLecture2.11
Python for Data Science: Data Structures 0/6
- Lists 38 minLecture3.1
- Tuples part 1 10 minLecture3.2
- Tuples part-2 04 minLecture3.3
- Sets 16 minLecture3.4
- Dictionary 21 minLecture3.5
- Strings 16 minLecture3.6
Python for Data Science: Functions 0/10
- Introduction 13 minLecture4.1
- Types of functions 25 minLecture4.2
- Function arguments 10 minLecture4.3
- Recursive functions 16 minLecture4.4
- Lambda functions 08 minLecture4.5
- Modules 07 minLecture4.6
- Packages 06 minLecture4.7
- File Handling 23 minLecture4.8
- Exception Handling 15 minLecture4.9
- Debugging Python 15 minLecture4.10
Python for Data Science: Numpy 0/2
- Numpy Introduction 41 minLecture5.1
- Numerical operations on Numpy 41 minLecture5.2
Python for Data Science: Matplotlib 0/1
- Getting started with Matplotlib 20 minLecture6.1
Python for Data Science: Pandas 0/3
- Getting started with pandas 08 minLecture7.1
- Data Frame Basics 09 minLecture7.2
- Key Operations on Data Frames 31 minLecture7.3
Python for Data Science: Computational Complexity 0/4
- Space and Time Complexity: Find largest number in a list 20 minLecture8.1
- Binary search 17 minLecture8.2
- Find elements common in two lists 06 minLecture8.3
- Find elements common in two lists using a Hashtable/Dict 12 minLecture8.4
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.
- Introduction to IRIS dataset and 2D scatter plot 26 minLecture9.1
- 3D scatter plot 06 minLecture9.2
- Pair plots 14 minLecture9.3
- Limitations of Pair Plots 02 minLecture9.4
- Histogram and Introduction to PDF(Probability Density Function) 17 minLecture9.5
- Univariate Analysis using PDF 06 minLecture9.6
- CDF(Cumulative Distribution Function) 15 minLecture9.7
- Mean, Variance and Standard Deviation 17 minLecture9.8
- Median 10 minLecture9.9
- Percentiles and Quantiles 09 minLecture9.10
- IQR(Inter Quartile Range) and MAD(Median Absolute Deviation) 06 minLecture9.11
- Box-plot with Whiskers 09 minLecture9.12
- Violin Plots 04 minLecture9.13
- Summarizing Plots, Univariate, Bivariate and Multivariate analysis 06 minLecture9.14
- Multivariate Probability Density, Contour Plot 09 minLecture9.15
- Exercise: Perform EDA on Haberman dataset 04 minLecture9.16
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.
- Why learn it ? 04 minLecture10.1
- Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector 14 minLecture10.2
- Dot Product and Angle between 2 Vectors 14 minLecture10.3
- Projection and Unit Vector 05 minLecture10.4
- Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane 23 minLecture10.5
- Distance of a point from a Plane/Hyperplane, Half-Spaces 10 minLecture10.6
- Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D) 07 minLecture10.7
- Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D) 06 minLecture10.8
- Square ,Rectangle 06 minLecture10.9
- Hyper Cube,Hyper Cuboid 03 minLecture10.10
- Revision Questions 30 minLecture10.11
Probability and Statistics 0/32
- Introduction to Probability and Statistics 17 minLecture11.1
- Population and Sample 07 minLecture11.2
- Gaussian/Normal Distribution and its PDF(Probability Density Function) 27 minLecture11.3
- CDF(Cumulative Distribution function) of Gaussian/Normal distribution 11 minLecture11.4
- Symmetric distribution, Skewness and Kurtosis 05 minLecture11.5
- Standard normal variate (Z) and standardization 05 minLecture11.6
- Kernel density estimation 07 minLecture11.7
- Sampling distribution & Central Limit theorem 19 minLecture11.8
- Q-Q plot:How to test if a random variable is normally distributed or not? 23 minLecture11.9
- How distributions are used? 17 minLecture11.10
- Chebyshev’s inequality 20 minLecture11.11
- Discrete and Continuous Uniform distributions 13 minLecture11.12
- How to randomly sample data points (Uniform Distribution) 10 minLecture11.13
- Bernoulli and Binomial Distribution 11 minLecture11.14
- Log Normal Distribution 12 minLecture11.15
- Power law distribution 12 minLecture11.16
- Box cox transform 12 minLecture11.17
- Applications of non-gaussian distributions? 26 minLecture11.18
- Co-variance 14 minLecture11.19
- Pearson Correlation Coefficient 13 minLecture11.20
- Spearman Rank Correlation Coefficient 07 minLecture11.21
- Correlation vs Causation 03 minLecture11.22
- How to use correlations? 13 minLecture11.23
- Confidence interval (C.I) Introduction 08 minLecture11.24
- Computing confidence interval given the underlying distribution 11 minLecture11.25
- C.I for mean of a normal random variable 14 minLecture11.26
- Confidence interval using bootstrapping 17 minLecture11.27
- Hypothesis testing methodology, Null-hypothesis, p-value 16 minLecture11.28
- Hypothesis Testing Intution with coin toss example 27 minLecture11.29
- Resampling and permutation test 15 minLecture11.30
- K-S Test for similarity of two distributions 15 minLecture11.31
- Code Snippet K-S Test 06 minLecture11.32
- Hypothesis testing: another example 18 minLecture11.33
- Resampling and Permutation test: another example 19 minLecture11.34
- How to use hypothesis testing? 23 minLecture11.35
- Proportional Sampling 18 minLecture11.36
- Revision Questions 30 minLecture11.37
Interview Questions on Probability and statistics 0/0
- Questions & Answers 30 minLecture12.1
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.
- What is Dimensionality reduction? 03 minLecture13.1
- Row Vector and Column Vector 05 minLecture13.2
- How to represent a data set? 04 minLecture13.3
- How to represent a dataset as a Matrix. 07 minLecture13.4
- Data Preprocessing: Feature Normalisation 20 minLecture13.5
- Mean of a data matrix 06 minLecture13.6
- Data Preprocessing: Column Standardization 16 minLecture13.7
- Co-variance of a Data Matrix 24 minLecture13.8
- MNIST dataset (784 dimensional) 20 minLecture13.9
- Code to Load MNIST Data Set 12 minLecture13.10
PCA(principal component analysis) 0/0
- Why learn PCA? 04 minLecture14.1
- Geometric intuition of PCA 14 minLecture14.2
- Mathematical objective function of PCA 13 minLecture14.3
- Alternative formulation of PCA: Distance minimization 10 minLecture14.4
- Eigen values and Eigen vectors (PCA): Dimensionality reduction 23 minLecture14.5
- PCA for Dimensionality Reduction and Visualization 10 minLecture14.6
- Visualize MNIST dataset 05 minLecture14.7
- Limitations of PCA 05 minLecture14.8
- PCA Code example 19 minLecture14.9
- PCA for dimensionality reduction (not-visualization) 15 minLecture14.10
(t-SNE)T-distributed Stochastic Neighbourhood Embedding 0/1
- What is t-SNE? 07 minLecture15.1
- Neighborhood of a point, Embedding 07 minLecture15.2
- Geometric intuition of t-SNE 09 minLecture15.3
- Crowding Problem 08 minLecture15.4
- How to apply t-SNE and interpret its output 38 minLecture15.5
- t-SNE on MNIST 07 minLecture15.6
- Code example of t-SNE 09 minLecture15.7
- Revision Questions 30 minLecture15.8
Interview Questions on Dimensionality Reduction 0/0
- Questions & Answers 30 minLecture16.1
Real world problem: Predict rating given product reviews on Amazon 0/18
- Dataset overview: Amazon Fine Food reviews(EDA) 23 minLecture17.1
- Data Cleaning: Deduplication 15 minLecture17.2
- Why convert text to a vector? 14 minLecture17.3
- Bag of Words (BoW) 18 minLecture17.4
- Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization. 15 minLecture17.5
- uni-gram, bi-gram, n-grams. 09 minLecture17.6
- tf-idf (term frequency- inverse document frequency) 22 minLecture17.7
- Why use log in IDF? 14 minLecture17.8
- Word2Vec. 16 minLecture17.9
- Avg-Word2Vec, tf-idf weighted Word2Vec 09 minLecture17.10
- Bag of Words( Code Sample) 19 minLecture17.11
- Text Preprocessing( Code Sample) 11 minLecture17.12
- Bi-Grams and n-grams (Code Sample) 05 minLecture17.13
- TF-IDF (Code Sample) 06 minLecture17.14
- Word2Vec (Code Sample) 12 minLecture17.15
- Avg-Word2Vec and TFIDF-Word2Vec (Code Sample) 02 minLecture17.16
- Exercise: t-SNE visualization of Amazon reviews with polarity based color-coding 06 minLecture17.17
- Assignment 30 minLecture17.18
Classification And Regression Models: K-Nearest Neighbors 0/33
- How “Classification” works? 10 minLecture18.1
- Data matrix notation 07 minLecture18.2
- Classification vs Regression (examples) 06 minLecture18.3
- K-Nearest Neighbours Geometric intuition with a toy example 11 minLecture18.4
- Failure cases of KNN 07 minLecture18.5
- Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming 20 minLecture18.6
- Cosine Distance & Cosine Similarity 19 minLecture18.7
- How to measure the effectiveness of k-NN? 16 minLecture18.8
- Test/Evaluation time and space complexity 12 minLecture18.9
- KNN Limitations 09 minLecture18.10
- Decision surface for K-NN as K changes 23 minLecture18.11
- Overfitting and Underfitting 12 minLecture18.12
- Need for Cross validation 22 minLecture18.13
- K-fold cross validation 17 minLecture18.14
- Visualizing train, validation and test datasets 13 minLecture18.15
- How to determine overfitting and underfitting? 19 minLecture18.16
- Time based splitting 19 minLecture18.17
- k-NN for regression 05 minLecture18.18
- Weighted k-NN 08 minLecture18.19
- Voronoi diagram 04 minLecture18.20
- Binary search tree 16 minLecture18.21
- How to build a kd-tree 17 minLecture18.22
- Find nearest neighbours using kd-tree 13 minLecture18.23
- Limitations of Kd tree 09 minLecture18.24
- Extensions 03 minLecture18.25
- Hashing vs LSH 10 minLecture18.26
- LSH for cosine similarity 40 minLecture18.27
- LSH for euclidean distance 13 minLecture18.28
- Probabilistic class label 08 minLecture18.29
- Code Sample:Decision boundary . 23 minLecture18.30
- Code Sample:Cross Validation 13 minLecture18.31
- Exercise: Apply k-NN on Amazon reviews dataset 05 minLecture18.32
- Revision Questions 30 minLecture18.33
Interview Questions on K-NN(K Nearest Neighbour) 0/0
- Questions & Answers 30 minLecture19.1
Classification algorithms in various situations 0/21
- Introduction 05 minLecture20.1
- Imbalanced vs balanced dataset 23 minLecture20.2
- Multi-class classification 12 minLecture20.3
- k-NN, given a distance or similarity matrix 09 minLecture20.4
- Train and test set differences 22 minLecture20.5
- Impact of outliers 07 minLecture20.6
- Local outlier Factor (Simple solution :Mean distance to Knn) 13 minLecture20.7
- k distance 04 minLecture20.8
- Reachability-Distance(A,B) 08 minLecture20.9
- Local reachability-density(A) 09 minLecture20.10
- Local outlier Factor(A) 21 minLecture20.11
- Impact of Scale & Column standardization 12 minLecture20.12
- Interpretability 12 minLecture20.13
- Feature Importance and Forward Feature selection 22 minLecture20.14
- Handling categorical and numerical features 24 minLecture20.15
- Handling missing values by imputation 21 minLecture20.16
- curse of dimensionality 27 minLecture20.17
- Bias-Variance tradeoff 24 minLecture20.18
- Intuitive understanding of bias-variance. 06 minLecture20.19
- Revision Questions 30 minLecture20.20
- best and wrost case of algorithm 06 minLecture20.21
Performance measurement of models 0/9
- Accuracy 15 minLecture21.1
- Confusion matrix, TPR, FPR, FNR, TNR 25 minLecture21.2
- Precision and recall, F1-score 10 minLecture21.3
- Receiver Operating Characteristic Curve (ROC) curve and AUC 19 minLecture21.4
- Log-loss 12 minLecture21.5
- R-Squared/Coefficient of determination 14 minLecture21.6
- Median absolute deviation (MAD) 05 minLecture21.7
- Revision Questions 30 minLecture21.8
- Distribution of errors 07 minLecture21.9
Interview Questions on Performance Measurement Models 0/0
- Questions & Answers 30 minLecture22.1
Naive Bayes 0/22
- Conditional probability 13 minLecture23.1
- Independent vs Mutually exclusive events 06 minLecture23.2
- Bayes Theorem with examples 18 minLecture23.3
- Exercise problems on Bayes Theorem 30 minLecture23.4
- Naive Bayes algorithm 26 minLecture23.5
- Toy example: Train and test stages 26 minLecture23.6
- Naive Bayes on Text data 16 minLecture23.7
- Laplace/Additive Smoothing 24 minLecture23.8
- Log-probabilities for numerical stability 11 minLecture23.9
- Bias and Variance tradeoff 14 minLecture23.10
- Feature importance and interpretability 10 minLecture23.11
- Imbalanced data 14 minLecture23.12
- Outliers 06 minLecture23.13
- Missing values 03 minLecture23.14
- Handling Numerical features (Gaussian NB) 13 minLecture23.15
- Multiclass classification 02 minLecture23.16
- Similarity or Distance matrix 03 minLecture23.17
- Large dimensionality 02 minLecture23.18
- Best and worst cases 08 minLecture23.19
- Code example 07 minLecture23.20
- Exercise: Apply Naive Bayes to Amazon reviews 06 minLecture23.21
- Revision Questions 30 minLecture23.22
Logistic Regression 0/18
- Geometric intuition of Logistic Regression 31 minLecture24.1
- Sigmoid function: Squashing 37 minLecture24.2
- Mathematical formulation of Objective function 24 minLecture24.3
- Weight vector 11 minLecture24.4
- L2 Regularization: Overfitting and Underfitting 26 minLecture24.5
- L1 regularization and sparsity 11 minLecture24.6
- Probabilistic Interpretation: Gaussian Naive Bayes 19 minLecture24.7
- Loss minimization interpretation 24 minLecture24.8
- hyperparameters and random search 16 minLecture24.9
- Column Standardization 05 minLecture24.10
- Feature importance and Model interpretability 14 minLecture24.11
- Collinearity of features 14 minLecture24.12
- Test/Run time space and time complexity 10 minLecture24.13
- Real world cases 11 minLecture24.14
- Non-linearly separable data & feature engineering 28 minLecture24.15
- Code sample: Logistic regression, GridSearchCV, RandomSearchCV 23 minLecture24.16
- Exercise: Apply Logistic regression to Amazon reviews dataset. 06 minLecture24.17
- Extensions to Generalized linear models 09 minLecture24.18
Linear Regression 0/4
- Geometric intuition of Linear Regression 13 minLecture25.1
- Mathematical formulation 14 minLecture25.2
- Real world Cases 08 minLecture25.3
- Code sample for Linear Regression 13 minLecture25.4
Solving Optimization Problems 0/12
- Differentiation 29 minLecture26.1
- Online differentiation tools 08 minLecture26.2
- Maxima and Minima 12 minLecture26.3
- Vector calculus: Grad 10 minLecture26.4
- Gradient descent: geometric intuition 19 minLecture26.5
- Learning rate 08 minLecture26.6
- Gradient descent for linear regression 08 minLecture26.7
- SGD algorithm 09 minLecture26.8
- Constrained Optimization & PCA 14 minLecture26.9
- Logistic regression formulation revisited 06 minLecture26.10
- Why L1 regularization creates sparsity? 17 minLecture26.11
- Exercise: Implement SGD for linear regression 06 minLecture26.12
- Revision questions 30 minLecture26.13
Interview Questions on Logistic Regression and Linear Regression 0/0
- Questions & Answers 30 minLecture27.1
Support Vector Machines (SVM) 0/16
- Geometric Intution 20 minLecture28.1
- Mathematical derivation 32 minLecture28.2
- Why we take values +1 and and -1 for Support vector planes 09 minLecture28.3
- Loss function (Hinge Loss) based interpretation 18 minLecture28.4
- Dual form of SVM formulation 16 minLecture28.5
- kernel trick 10 minLecture28.6
- Polynomial Kernel 11 minLecture28.7
- RBF-Kernel 21 minLecture28.8
- Domain specific Kernels 06 minLecture28.9
- Train and run time complexities 08 minLecture28.10
- nu-SVM: control errors and support vectors 06 minLecture28.11
- SVM Regression 08 minLecture28.12
- Cases 09 minLecture28.13
- Code Sample 14 minLecture28.14
- Exercise: Apply SVM to Amazon reviews dataset 04 minLecture28.15
- Revision Questions 30 minLecture28.16
Interview Questions on Support Vector Machine 0/0
- Questions & Answers 30 minLecture29.1
Decision Trees 0/16
- Geometric Intuition of decision tree: Axis parallel hyperplanes 17 minLecture30.1
- Sample Decision tree 08 minLecture30.2
- Building a decision Tree:Entropy 19 minLecture30.3
- Building a decision Tree:Information Gain 10 minLecture30.4
- Building a decision Tree: Gini Impurity 07 minLecture30.5
- Building a decision Tree: Constructing a DT 21 minLecture30.6
- Building a decision Tree: Splitting numerical features 08 minLecture30.7
- Feature standardization 04 minLecture30.8
- Building a decision Tree:Categorical features with many possible values 07 minLecture30.9
- Overfitting and Underfitting 08 minLecture30.10
- Train and Run time complexity 07 minLecture30.11
- Regression using Decision Trees 09 minLecture30.12
- Cases 12 minLecture30.13
- Code Samples 09 minLecture30.14
- Exercise: Decision Trees on Amazon reviews dataset 03 minLecture30.15
- Revision Questions 30 minLecture30.16
Interview Questions on decision Trees 0/0
- Questions & Answers 30 minLecture31.1
Ensemble Models 0/20
- What are ensembles? 06 minLecture32.1
- Bootstrapped Aggregation (Bagging) Intuition 17 minLecture32.2
- Random Forest and their construction 15 minLecture32.3
- Bias-Variance tradeoff 07 minLecture32.4
- Train and run time complexity 09 minLecture32.5
- Bagging:Code Sample 04 minLecture32.6
- Extremely randomized trees 08 minLecture32.7
- Random Tree :Cases 06 minLecture32.8
- Boosting Intuition 17 minLecture32.9
- Residuals, Loss functions and gradients 13 minLecture32.10
- Gradient Boosting 10 minLecture32.11
- Regularization by Shrinkage 08 minLecture32.12
- Train and Run time complexity 06 minLecture32.13
- XGBoost: Boosting + Randomization 14 minLecture32.14
- AdaBoost: geometric intuition 07 minLecture32.15
- Stacking models 22 minLecture32.16
- Cascading classifiers 15 minLecture32.17
- Kaggle competitions vs Real world 09 minLecture32.18
- Exercise: Apply GBDT and RF to Amazon reviews dataset. 04 minLecture32.19
- Revision Questions 30 minLecture32.20
Featurization and Feature engineering. 0/18
- Introduction 17 minLecture33.1
- Moving window for Time Series Data 25 minLecture33.2
- Fourier decomposition 22 minLecture33.3
- Deep learning features: LSTM 08 minLecture33.4
- Image histogram 23 minLecture33.5
- Keypoints: SIFT. 10 minLecture33.6
- Deep learning features: CNN 04 minLecture33.7
- Relational data 10 minLecture33.8
- Graph data 12 minLecture33.9
- Indicator variables 07 minLecture33.10
- Feature binning 14 minLecture33.11
- Interaction variables 08 minLecture33.12
- Mathematical transforms 04 minLecture33.13
- Model specific featurizations 09 minLecture33.14
- Feature orthogonality 11 minLecture33.15
- Domain specific featurizations 04 minLecture33.16
- Feature slicing 10 minLecture33.17
- Kaggle Winners solutions 07 minLecture33.18
Miscellaneous Topics 0/9
- Calibration of Models:Need for calibration 08 minLecture34.1
- Calibration Plots. 17 minLecture34.2
- Platt’s Calibration/Scaling. 08 minLecture34.3
- Isotonic Regression 11 minLecture34.4
- Code Samples 04 minLecture34.5
- Modeling in the presence of outliers: RANSAC 13 minLecture34.6
- Productionizing models 17 minLecture34.7
- Retraining models periodically. 08 minLecture34.8
- A/B testing. 22 minLecture34.9
- Data Science Life cycle 17 minLecture34.10
- VC dimension 22 minLecture34.11
Unsupervised learning/Clustering 0/14
- What is Clustering? 10 minLecture35.1
- Unsupervised learning 04 minLecture35.2
- Applications 16 minLecture35.3
- Metrics for Clustering 13 minLecture35.4
- K-Means: Geometric intuition, Centroids 08 minLecture35.5
- K-Means: Mathematical formulation: Objective function 11 minLecture35.6
- K-Means Algorithm. 11 minLecture35.7
- How to initialize: K-Means++ 24 minLecture35.8
- Failure cases/Limitations 11 minLecture35.9
- K-Medoids 19 minLecture35.10
- Determining the right K 05 minLecture35.11
- Code Samples 07 minLecture35.12
- Time and space complexity 04 minLecture35.13
- Exercise: Clustering the Amazon reviews 05 minLecture35.14
Hierarchical clustering Technique 0/7
- Agglomerative & Divisive, Dendrograms 13 minLecture36.1
- Agglomerative Clustering 09 minLecture36.2
- Proximity methods: Advantages and Limitations. 24 minLecture36.3
- Time and Space Complexity 04 minLecture36.4
- Limitations of Hierarchical Clustering 05 minLecture36.5
- Code sample 03 minLecture36.6
- Exercise: Amazon food reviews 03 minLecture36.7
DBSCAN (Density based clustering) Technique 0/11
- Density based clustering 05 minLecture37.1
- MinPts and Eps: Density 06 minLecture37.2
- Core, Border and Noise points 07 minLecture37.3
- Density edge and Density connected points. 06 minLecture37.4
- DBSCAN Algorithm 11 minLecture37.5
- Hyper Parameters: MinPts and Eps 10 minLecture37.6
- Advantages and Limitations of DBSCAN 10 minLecture37.7
- Time and Space Complexity 03 minLecture37.8
- Code samples. 03 minLecture37.9
- Exercise: Amazon Food reviews 03 minLecture37.10
- Revision Questions 30 minLecture37.11
Recommender Systems and Matrix Factorization 0/16
- Problem formulation: Movie reviews 23 minLecture38.1
- Content based vs Collaborative Filtering 11 minLecture38.2
- Similarity based Algorithms 16 minLecture38.3
- Matrix Factorization: PCA, SVD 23 minLecture38.4
- Matrix Factorization: NMF 03 minLecture38.5
- Matrix Factorization for Collaborative filtering 23 minLecture38.6
- Matrix Factorization for feature engineering 09 minLecture38.7
- Clustering as MF 21 minLecture38.8
- Hyperparameter tuning 10 minLecture38.9
- Matrix Factorization for recommender systems: Netflix Prize Solution 30 minLecture38.10
- Cold Start problem 06 minLecture38.11
- Word vectors as MF 20 minLecture38.12
- Eigen-Faces 15 minLecture38.13
- Code example. 11 minLecture38.14
- Exercise: Word Vectors using Truncated SVD. 07 minLecture38.15
- Revision Questions 30 minLecture38.16
Interview Questions on Recommender Systems and Matrix Factorization. 0/0
- Questions & Answers 30 minLecture39.1
Case study 2: Stackoverflow tag predictor 0/17
- Business/Real world problem 10 minLecture40.1
- Business objectives and constraints 05 minLecture40.2
- Mapping to an ML problem: Data overview 04 minLecture40.3
- Mapping to an ML problem:ML problem formulation. 05 minLecture40.4
- Mapping to an ML problem:Performance metrics. 21 minLecture40.5
- Hamming loss 07 minLecture40.6
- EDA:Data Loading 13 minLecture40.7
- EDA:Analysis of tags 11 minLecture40.8
- EDA:Data Preprocessing 11 minLecture40.9
- Data Modeling : Multi label Classification 18 minLecture40.10
- Data preparation. 08 minLecture40.11
- Train-Test Split 02 minLecture40.12
- Featurization 06 minLecture40.13
- Logistic regression: One VS Rest 07 minLecture40.14
- Sampling data and tags+Weighted models. 04 minLecture40.15
- Logistic regression revisited 04 minLecture40.16
- Why not use advanced techniques 03 minLecture40.17
- Assignments. 05 minLecture40.18
Case Study 3: Quora question Pair Similarity Problem 0/16
- Business/Real world problem : Problem definition 06 minLecture41.1
- Business objectives and constraints. 05 minLecture41.2
- Mapping to an ML problem : Data overview 05 minLecture41.3
- Mapping to an ML problem : ML problem and performance metric. 04 minLecture41.4
- Mapping to an ML problem : Train-test split 05 minLecture41.5
- EDA: Basic Statistics. 07 minLecture41.6
- EDA: Basic Feature Extraction 06 minLecture41.7
- EDA: Text Preprocessing 10 minLecture41.8
- EDA: Advanced Feature Extraction 31 minLecture41.9
- EDA: Feature analysis. 09 minLecture41.10
- EDA: Data Visualization: T-SNE. 03 minLecture41.11
- EDA: TF-IDF weighted Word2Vec featurization. 06 minLecture41.12
- ML Models :Loading Data 06 minLecture41.13
- ML Models: Random Model 07 minLecture41.14
- ML Models : Logistic Regression and Linear SVM 11 minLecture41.15
- ML Models : XGBoost 06 minLecture41.16
- Assignments 04 minLecture41.17
Case Study 4: Amazon fashion discovery engine 0/27
- Problem Statement: Recommend similar apparel products in e-commerce using product descriptions and Images 12 minLecture42.1
- Plan of action 07 minLecture42.2
- Amazon product advertising API 10 minLecture42.3
- Data folders and paths 06 minLecture42.4
- Overview of the data and Terminology 12 minLecture42.5
- Data cleaning and understanding:Missing data in various features 22 minLecture42.6
- Understand duplicate rows 09 minLecture42.7
- Remove duplicates : Part 1 12 minLecture42.8
- Remove duplicates: Part 2 15 minLecture42.9
- Text Pre-Processing: Tokenization and Stop-word removal 10 minLecture42.10
- Stemming 04 minLecture42.11
- Text based product similarity :Converting text to an n-D vector: bag of words 14 minLecture42.12
- Code for bag of words based product similarity 26 minLecture42.13
- TF-IDF: featurizing text based on word-importance 17 minLecture42.14
- Code for TF-IDF based product similarity 10 minLecture42.15
- Code for IDF based product similarity 09 minLecture42.16
- Text Semantics based product similarity: Word2Vec(featurizing text based on semantic similarity) 19 minLecture42.17
- Code for Average Word2Vec product similarity 15 minLecture42.18
- TF-IDF weighted Word2Vec 09 minLecture42.19
- Code for IDF weighted Word2Vec product similarity 06 minLecture42.20
- Weighted similarity using brand and color 09 minLecture42.21
- Code for weighted similarity 07 minLecture42.22
- Building a real world solution 05 minLecture42.23
- Deep learning based visual product similarity:ConvNets: How to featurize an image: edges, shapes, parts 11 minLecture42.24
- Using Keras + Tensorflow to extract features 08 minLecture42.25
- Visual similarity based product similarity 06 minLecture42.26
- Measuring goodness of our solution :A/B testing 07 minLecture42.27
- Exercise :Build a weighted Nearest neighbor model using Visual, Text, Brand and Color 09 minLecture42.28
Case Study 5: Microsoft Malware Detection 0/20
- Business/real world problem :Problem definition 06 minLecture43.1
- Business/real world problem :Objectives and constraints 07 minLecture43.2
- Machine Learning problem mapping :Data overview. 13 minLecture43.3
- Machine Learning problem mapping :ML problem 12 minLecture43.4
- Machine Learning problem mapping :Train and test splitting 04 minLecture43.5
- Exploratory Data Analysis :Class distribution. 03 minLecture43.6
- Exploratory Data Analysis :Feature extraction from byte files 08 minLecture43.7
- Exploratory Data Analysis :Multivariate analysis of features from byte files 03 minLecture43.8
- Exploratory Data Analysis :Train-Test class distribution 03 minLecture43.9
- ML models – using byte files only :Random Model 11 minLecture43.10
- k-NN 07 minLecture43.11
- Logistic regression 05 minLecture43.12
- Random Forest and Xgboost 07 minLecture43.13
- ASM Files :Feature extraction & Multiprocessing. 11 minLecture43.14
- File-size feature 02 minLecture43.15
- Univariate analysis 03 minLecture43.16
- t-SNE analysis. 02 minLecture43.17
- ML models on ASM file features 07 minLecture43.18
- Models on all features :t-SNE 02 minLecture43.19
- Models on all features :RandomForest and Xgboost 04 minLecture43.20
- Assignments. 04 minLecture43.21
Case study 6:Netflix Movie Recommendation System 0/27
- Business/Real world problem:Problem definition 06 minLecture44.1
- Objectives and constraints 07 minLecture44.2
- Mapping to an ML problem:Data overview. 04 minLecture44.3
- Mapping to an ML problem:ML problem formulation 05 minLecture44.4
- Exploratory Data Analysis:Data preprocessing 07 minLecture44.5
- Exploratory Data Analysis:Temporal Train-Test split. 06 minLecture44.6
- Exploratory Data Analysis:Preliminary data analysis. 15 minLecture44.7
- Exploratory Data Analysis:Sparse matrix representation 08 minLecture44.8
- Exploratory Data Analysis:Average ratings for various slices 08 minLecture44.9
- Exploratory Data Analysis:Cold start problem 05 minLecture44.10
- Computing Similarity matrices:User-User similarity matrix 20 minLecture44.11
- Computing Similarity matrices:Movie-Movie similarity 06 minLecture44.12
- Computing Similarity matrices:Does movie-movie similarity work? 06 minLecture44.13
- ML Models:Surprise library 06 minLecture44.14
- Overview of the modelling strategy. 08 minLecture44.15
- Data Sampling. 05 minLecture44.16
- Google drive with intermediate files 02 minLecture44.17
- Featurizations for regression. 11 minLecture44.18
- Data transformation for Surprise. 02 minLecture44.19
- Xgboost with 13 features 06 minLecture44.20
- Surprise Baseline model. 09 minLecture44.21
- Xgboost + 13 features +Surprise baseline model 04 minLecture44.22
- Surprise KNN predictors 15 minLecture44.23
- Matrix Factorization models using Surprise 05 minLecture44.24
- SVD ++ with implicit feedback 11 minLecture44.25
- Final models with all features and predictors. 04 minLecture44.26
- Comparison between various models. 04 minLecture44.27
- Comparison between various models. 04 minLecture44.28
Case Study 7: Personalized Cancer Diagnosis 0/21
- Business/Real world problem : Overview 13 minLecture45.1
- Business objectives and constraints. 11 minLecture45.2
- ML problem formulation :Data 05 minLecture45.3
- ML problem formulation: Mapping real world to ML problem. 19 minLecture45.4
- ML problem formulation :Train, CV and Test data construction 04 minLecture45.5
- Exploratory Data Analysis:Reading data & preprocessing 07 minLecture45.6
- Exploratory Data Analysis:Distribution of Class-labels 07 minLecture45.7
- Exploratory Data Analysis: “Random” Model 19 minLecture45.8
- Univariate Analysis:Gene feature 34 minLecture45.9
- Univariate Analysis:Variation Feature 19 minLecture45.10
- Univariate Analysis:Text feature 15 minLecture45.11
- Machine Learning Models:Data preparation 08 minLecture45.12
- Baseline Model: Naive Bayes 23 minLecture45.13
- K-Nearest Neighbors Classification 09 minLecture45.14
- Logistic Regression with class balancing 10 minLecture45.15
- Logistic Regression without class balancing 04 minLecture45.16
- Linear-SVM. 06 minLecture45.17
- Random-Forest with one-hot encoded features 07 minLecture45.18
- Random-Forest with response-coded features 06 minLecture45.19
- Stacking Classifier 08 minLecture45.20
- Majority Voting classifier 05 minLecture45.21
- Assignments. 05 minLecture45.22
Case study 8:Taxi demand prediction in New York City 0/28
- Business/Real world problem Overview 09 minLecture46.1
- Objectives and Constraints 11 minLecture46.2
- Mapping to ML problem :Data 08 minLecture46.3
- Mapping to ML problem :dask dataframes 11 minLecture46.4
- Mapping to ML problem :Fields/Features. 06 minLecture46.5
- Mapping to ML problem :Time series forecasting/Regression 08 minLecture46.6
- Mapping to ML problem :Performance metrics 06 minLecture46.7
- Data Cleaning :Latitude and Longitude data 04 minLecture46.8
- Data Cleaning :Trip Duration. 07 minLecture46.9
- Data Cleaning :Speed. 05 minLecture46.10
- Data Cleaning :Distance. 02 minLecture46.11
- Data Cleaning :Fare 06 minLecture46.12
- Data Cleaning :Remove all outliers/erroneous points 03 minLecture46.13
- Data Preparation:Clustering/Segmentation 19 minLecture46.14
- Data Preparation:Time binning 05 minLecture46.15
- Data Preparation:Smoothing time-series data. 05 minLecture46.16
- Data Preparation:Smoothing time-series data cont.. 02 minLecture46.17
- Data Preparation: Time series and Fourier transforms. 13 minLecture46.18
- Ratios and previous-time-bin values 09 minLecture46.19
- Simple moving average 08 minLecture46.20
- Weighted Moving average. 05 minLecture46.21
- Exponential weighted moving average 06 minLecture46.22
- Results. 04 minLecture46.23
- Regression models :Train-Test split & Features 08 minLecture46.24
- Linear regression. 03 minLecture46.25
- Random Forest regression 04 minLecture46.26
- Xgboost Regression 02 minLecture46.27
- Model comparison 06 minLecture46.28
- Assignment. 06 minLecture46.29
Deep Learning:Neural Networks. 0/14
- History of Neural networks and Deep Learning. 25 minLecture47.1
- How Biological Neurons work? 10 minLecture47.2
- Growth of biological neural networks 16 minLecture47.3
- Diagrammatic representation: Logistic Regression and Perceptron 17 minLecture47.4
- Multi-Layered Perceptron (MLP). 23 minLecture47.5
- Notation 18 minLecture47.6
- Training a single-neuron model. 28 minLecture47.7
- Training an MLP: Chain Rule 40 minLecture47.8
- Training an MLP:Memoization 14 minLecture47.9
- Backpropagation. 26 minLecture47.10
- Activation functions 17 minLecture47.11
- Vanishing Gradient problem. 23 minLecture47.12
- Bias-Variance tradeoff. 10 minLecture47.13
- Decision surfaces: Playground 15 minLecture47.14
Deep Learning: Deep Multi-layer perceptrons 0/21
- Deep Multi-layer perceptrons:1980s to 2010s 16 minLecture48.1
- Dropout layers & Regularization. 21 minLecture48.2
- Rectified Linear Units (ReLU). 28 minLecture48.3
- Weight initialization. 24 minLecture48.4
- Batch Normalization. 21 minLecture48.5
- Optimizers:Hill-descent analogy in 2D 19 minLecture48.6
- Optimizers:Hill descent in 3D and contours. 13 minLecture48.7
- SGD Recap 18 minLecture48.8
- Batch SGD with momentum. 25 minLecture48.9
- Nesterov Accelerated Gradient (NAG) 08 minLecture48.10
- Optimizers:AdaGrad 15 minLecture48.11
- Optimizers : Adadelta andRMSProp 10 minLecture48.12
- Adam 11 minLecture48.13
- Which algorithm to choose when? 05 minLecture48.14
- Gradient Checking and clipping 10 minLecture48.15
- Softmax and Cross-entropy for multi-class classification. 25 minLecture48.16
- How to train a Deep MLP? 08 minLecture48.17
- Auto Encoders. 27 minLecture48.18
- Word2Vec :CBOW 19 minLecture48.19
- Word2Vec: Skip-gram 14 minLecture48.20
- Word2Vec :Algorithmic Optimizations. 12 minLecture48.21
Deep Learning: Tensorflow and Keras. 0/14
- Tensorflow and Keras overview 23 minLecture49.1
- GPU vs CPU for Deep Learning. 23 minLecture49.2
- Google Colaboratory. 05 minLecture49.3
- Install TensorFlow 06 minLecture49.4
- Online documentation and tutorials 06 minLecture49.5
- Softmax Classifier on MNIST dataset. 32 minLecture49.6
- MLP: Initialization 11 minLecture49.7
- Model 1: Sigmoid activation 22 minLecture49.8
- Model 2: ReLU activation. 06 minLecture49.9
- Model 3: Batch Normalization. 08 minLecture49.10
- Model 4 : Dropout. 05 minLecture49.11
- MNIST classification in Keras. 18 minLecture49.12
- Hyperparameter tuning in Keras. 11 minLecture49.13
- Exercise: Try different MLP architectures on MNIST dataset. 05 minLecture49.14
Deep Learning: Convolutional Neural Nets. 0/19
- Biological inspiration: Visual Cortex 17 minLecture50.1
- Convolution:Edge Detection on images. 28 minLecture50.2
- Convolution:Padding and strides 19 minLecture50.3
- Convolution over RGB images. 11 minLecture50.4
- Convolutional layer. 23 minLecture50.5
- Max-pooling. 12 minLecture50.6
- CNN Training: Optimization 09 minLecture50.7
- Example CNN: LeNet [1998] 11 minLecture50.8
- ImageNet dataset. 06 minLecture50.9
- Data Augmentation. 07 minLecture50.10
- Convolution Layers in Keras 17 minLecture50.11
- AlexNet 13 minLecture50.12
- VGGNet 11 minLecture50.13
- Residual Network. 22 minLecture50.14
- Inception Network. 19 minLecture50.15
- What is Transfer learning. 23 minLecture50.16
- Code example: Cats vs Dogs. 15 minLecture50.17
- Code Example: MNIST dataset. 06 minLecture50.18
- Assignment: Try various CNN networks on MNIST dataset. 04 minLecture50.19
Deep Learning: Long Short-term memory (LSTMs) 0/11
- Why RNNs? 23 minLecture51.1
- Recurrent Neural Network. 29 minLecture51.2
- Training RNNs: Backprop. 16 minLecture51.3
- Types of RNNs. 14 minLecture51.4
- Need for LSTM/GRU. 10 minLecture51.5
- LSTM. 34 minLecture51.6
- GRUs. 07 minLecture51.7
- Deep RNN. 07 minLecture51.8
- Bidirectional RNN. 12 minLecture51.9
- Code example : IMDB Sentiment classification 33 minLecture51.10
- Exercise: Amazon Fine Food reviews LSTM model. 04 minLecture51.11
Interview Questions on Deep Learning 0/1
- Questions and Answers 30 minLecture52.1
Case Study 9: Self Driving Car 0/13
- Self Driving Car :Problem definition. 14 minLecture53.1
- Datasets. 09 minLecture53.2
- Data understanding & Analysis :Files and folders. 04 minLecture53.3
- Dash-cam images and steering angles. 05 minLecture53.4
- Split the dataset: Train vs Test 03 minLecture53.5
- EDA: Steering angles 06 minLecture53.6
- Mean Baseline model: simple 05 minLecture53.7
- Deep-learning model:Deep Learning for regression: CNN, CNN+RNN 10 minLecture53.8
- Batch load the dataset. 06 minLecture53.9
- NVIDIA’s end to end CNN model. 18 minLecture53.10
- Train the model. 13 minLecture53.11
- Test and visualize the output. 11 minLecture53.12
- Extensions. 05 minLecture53.13
- Assignment. 03 minLecture53.14
Case Study 10: Music Generation using Deep-Learning 0/11
- Real-world problem 15 minLecture54.1
- Music representation 17 minLecture54.2
- Char-RNN with abc-notation :Char-RNN model 23 minLecture54.3
- Char-RNN with abc-notation :Data preparation. 40 minLecture54.4
- Char-RNN with abc-notation:Many to Many RNN ,TimeDistributed-Dense layer 18 minLecture54.5
- Char-RNN with abc-notation : State full RNN 11 minLecture54.6
- Char-RNN with abc-notation :Model architecture,Model training. 13 minLecture54.7
- Char-RNN with abc-notation :Music generation. 11 minLecture54.8
- Char-RNN with abc-notation :Generate tabla music 03 minLecture54.9
- MIDI music generation. 04 minLecture54.10
- Survey blog: 05 minLecture54.11
Case Study 11: Human Activity Recognition 0/8
- Human Activity Recognition Problem definition 09 minLecture55.1
- Dataset understanding 22 minLecture55.2
- Data cleaning & preprocessing 04 minLecture55.3
- EDA:Univariate analysis. 05 minLecture55.4
- EDA:Data visualization using t-SNE 05 minLecture55.5
- Classical ML models. 13 minLecture55.6
- Deep-learning Model. 15 minLecture55.7
- Exercise: Build deeper LSTM models and hyper-param tune them 03 minLecture55.8
Case Studies 0/2
- AD-Click PredicitionLecture56.1
- Facebook Friend Recommendation using Graph MiningLecture56.2
Interview Questions 0/2
- Revision Questions 30 minLecture57.1
- Questions And Answers 30 minLecture57.2
- External resources for Interview Questions 30 minLecture57.3
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