Python for Data Science Introduction 0/0
- Python, Anaconda and relevant packages installations 23 minLecture1.1
- Why learn Python? 04 minLecture1.2
- Keywords and identifiers 06 minLecture1.3
- comments, indentation and statements 09 minLecture1.4
- Variables and data types in Python 32 minLecture1.5
- Standard Input and Output 07 minLecture1.6
- Operators 14 minLecture1.7
- Control flow: if else 10 minLecture1.8
- Control flow: while loop 16 minLecture1.9
- Control flow: for loop 15 minLecture1.10
- Control flow: break and continue 10 minLecture1.11
Python for Data Science: Data Structures 0/6
- Lists 38 minLecture2.1
- Tuples part 1 10 minLecture2.2
- Tuples part-2 04 minLecture2.3
- Sets 16 minLecture2.4
- Dictionary 21 minLecture2.5
- Strings 16 minLecture2.6
Python for Data Science: Numpy 0/2
- Numpy Introduction 41 minLecture3.1
- Numerical operations on Numpy 41 minLecture3.2
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: Matplotlib 0/1
- Getting started with Matplotlib 20 minLecture5.1
Python for Data Science: Pandas 0/3
- Getting started with pandas 08 minLecture6.1
- Data Frame Basics 09 minLecture6.2
- Key Operations on Data Frames 31 minLecture6.3
Python for Data Science: Computational Complexity 0/4
- Space and Time Complexity: Find largest number in a list 20 minLecture7.1
- Binary search 17 minLecture7.2
- Find elements common in two lists 06 minLecture7.3
- Find elements common in two lists using a Hashtable/Dict 12 minLecture7.4
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 minLecture8.1
- 3D scatter plot 06 minLecture8.2
- Pair plots 14 minLecture8.3
- Limitations of pair plots 02 minLecture8.4
- Histogram and Introduction to PDF(Probability Density Function) 17 minLecture8.5
- Univariate Analysis using PDF 06 minLecture8.6
- CDF(Cumulative Distribution Function) 15 minLecture8.7
- Mean, Variance and Standard Deviation 17 minLecture8.8
- Median 10 minLecture8.9
- Percentiles and Quantiles 09 minLecture8.10
- IQR(Inter Quartile Range) and MAD(Median Absolute Deviation) 06 minLecture8.11
- Box-plot with Whiskers 09 minLecture8.12
- Violin Plots 04 minLecture8.13
- Summarizing Plots, Univariate, Bivariate and Multivariate analysis 06 minLecture8.14
- Multivariate Probability Density, Contour Plot 09 minLecture8.15
- Exercise: Perform EDA on Haberman dataset 04 minLecture8.16
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 minLecture9.1
- Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector 14 minLecture9.2
- Dot Product and Angle between 2 Vectors – 14 minLecture9.3
- Projection and Unit Vector 05 minLecture9.4
- Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane 23 minLecture9.5
- Distance of a point from a Plane/Hyperplane, Half-Spaces 10 minLecture9.6
- Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D) 07 minLecture9.7
- Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D) 06 minLecture9.8
- Square ,Rectangle 06 minLecture9.9
- Hyper Cube,Hyper Cuboid 03 minLecture9.10
- Assignment[Optional] 30 minLecture9.11
Interview Questions on Linear Algebra 0/0
- Questions & Answers 30 minLecture10.1
Probability and Statistics 0/28
- Introduction to Probability and Stats 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 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
- Discrete and Continuous Uniform distributions 13 minLecture11.10
- How to randomly sample data points (Uniform Distribution) 10 minLecture11.11
- Bernoulli and Binomial Distribution 11 minLecture11.12
- Log Normal Distribution 12 minLecture11.13
- Power law distribution 12 minLecture11.14
- Box cox transform 12 minLecture11.15
- Co-variance 14 minLecture11.16
- Pearson Correlation Coefficient 13 minLecture11.17
- Spearman Rank Correlation Coefficient 07 minLecture11.18
- Correlation vs Causation 03 minLecture11.19
- Confidence interval (C.I) Introduction 08 minLecture11.20
- Discrete and Continuous Uniform distributions 11 minLecture11.21
- C.I for mean of a normal random variable 14 minLecture11.22
- Confidence interval using bootstrapping 17 minLecture11.23
- Hypothesis testing methodology, Null-hypothesis, p-value 16 minLecture11.24
- Resampling and permutation test 15 minLecture11.25
- K-S Test for similarity of distributions 15 minLecture11.26
- K-S Test for similarity of two distributions 06 minLecture11.27
- Hypothesis Testing Intution with coin toss example 27 minLecture11.28
- Hypothesis testing Mean differences Example 18 minLecture11.29
- Resampling 19 minLecture11.30
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: Column Normalization 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 Dataset 12 minLecture13.10
Interview Questions on Dimensionality Reduction 0/0
- Questions & Answers 30 minLecture14.1
PCA(principal component analysis) 0/0
- Why learn PCA? 04 minLecture15.1
- Geometric intuition of PCA 14 minLecture15.2
- Mathematical objective function of PCA 13 minLecture15.3
- Alternative formulation of PCA: Distance minimization 10 minLecture15.4
- Eigen values and Eigen vectors 23 minLecture15.5
- PCA for Dimensionality Reduction and Visualization 10 minLecture15.6
- Visualize MNIST dataset 05 minLecture15.7
- Limitations of PCA 05 minLecture15.8
- PCA Code example using Visualization 19 minLecture15.9
- PCA Code example using non-Visualization 15 minLecture15.10
(t-SNE)T-distributed Stochastic Neighbourhood Embedding 0/1
- What is t-SNE? 07 minLecture16.1
- Neighborhood of a point, Embedding 07 minLecture16.2
- Geometric intuition of t-SNE 09 minLecture16.3
- Crowding Problem 08 minLecture16.4
- How to apply t-SNE and interpret its output 38 minLecture16.5
- t-SNE on MNIST 07 minLecture16.6
- Code example of t-SNE 09 minLecture16.7
- Assignment 30 minLecture16.8
Real world problem: Predict rating given product reviews on Amazon 0/17
- 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
Classification And Regression Models: K-Nearest Neighbors 0/31
- 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
Interview Questions on K-NN(K Nearest Neighbour) 0/0
- Questions & Answers 30 minLecture19.1
Classification algorithms in various situations 0/20
- 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
- best and wrost case of algorithm 06 minLecture20.20
Interview Questions on Classification algorithms in various situations 0/0
- Questions & Answers 30 minLecture21.1
Performance measurement of models 0/8
- Accuracy 15 minLecture22.1
- Confusion matrix, TPR, FPR, FNR, TNR 25 minLecture22.2
- Precision and recall, F1-score 10 minLecture22.3
- Receiver Operating Characteristic Curve (ROC) curve and AUC 19 minLecture22.4
- Log-loss 12 minLecture22.5
- R-Squared/Coefficient of determination 14 minLecture22.6
- Median absolute deviation (MAD) 05 minLecture22.7
- Distribution of errors 07 minLecture22.8
Interview Questions on Performance Measurement Models 0/0
- Questions & Answers 30 minLecture23.1
Naive Bayes 0/21
- Conditional probability 13 minLecture24.1
- Independent vs Mutually exclusive events 06 minLecture24.2
- Bayes Theorem with examples 18 minLecture24.3
- Exercise problems on Bayes Theorem 30 minLecture24.4
- Naive Bayes algorithm 26 minLecture24.5
- Toy example: Train and test stages 26 minLecture24.6
- Naive Bayes on Text data 16 minLecture24.7
- Laplace/Additive Smoothing 24 minLecture24.8
- Log-probabilities for numerical stability 11 minLecture24.9
- Bias and Variance tradeoff 14 minLecture24.10
- Feature importance and interpretability 10 minLecture24.11
- Imbalanced data 14 minLecture24.12
- Outliers 06 minLecture24.13
- Missing values 03 minLecture24.14
- Handling Numerical features (Gaussian NB) 13 minLecture24.15
- Multiclass classification 02 minLecture24.16
- Similarity or Distance matrix 03 minLecture24.17
- Large dimensionality 02 minLecture24.18
- Best and worst cases 08 minLecture24.19
- Code example 07 minLecture24.20
- Exercise: Apply Naive Bayes to Amazon reviews 06 minLecture24.21
Interview Questions on Naive Bayes Algorithm 0/0
- Questions & Answers 30 minLecture25.1
Logistic Regression 0/18
- Geometric intuition of Logistic Regression 31 minLecture26.1
- Sigmoid function: Squashing 37 minLecture26.2
- Mathematical formulation of Objective function 24 minLecture26.3
- Weight vector 11 minLecture26.4
- L2 Regularization: Overfitting and Underfitting 26 minLecture26.5
- L1 regularization and sparsity 11 minLecture26.6
- Probabilistic Interpretation: Gaussian Naive Bayes 19 minLecture26.7
- Loss minimization interpretation 24 minLecture26.8
- hyperparameters and random search 16 minLecture26.9
- Column Standardization 05 minLecture26.10
- Feature importance and model interpretability 14 minLecture26.11
- Collinearity of features 14 minLecture26.12
- Test/Run time space and time complexity 10 minLecture26.13
- Real world cases 11 minLecture26.14
- Non-linearly separable data & feature engineering 28 minLecture26.15
- Code sample: Logistic regression, GridSearchCV, RandomSearchCV 23 minLecture26.16
- Exercise: Apply Logistic regression to Amazon reviews dataset. 06 minLecture26.17
- Extensions to Generalized linear models 09 minLecture26.18
Linear Regression 0/4
- Geometric intuition 13 minLecture27.1
- Mathematical formulation 14 minLecture27.2
- Real world Cases 08 minLecture27.3
- Code sample for Linear Regression 13 minLecture27.4
Solving optimization problems 0/12
- Differentiation 29 minLecture28.1
- Online differentiation tools 08 minLecture28.2
- Maxima and Minima 12 minLecture28.3
- Vector calculus: Grad 10 minLecture28.4
- Gradient descent: geometric intuition 19 minLecture28.5
- Learning rateLecture28.6
- Gradient descent for linear regression 08 minLecture28.7
- SGD algorithm 09 minLecture28.8
- Constrained Optimization & PCA 14 minLecture28.9
- Logistic regression formulation revisited 06 minLecture28.10
- Why L1 regularization creates sparsity? 17 minLecture28.11
- Exercise: Implement SGD for linear regression 06 minLecture28.12
- Interview Questions on Logistic Regression and Linear Regression 30 minLecture28.13
Interview Questions on Logistic Regression and Linear Regression 0/0
- Questions & Answers 30 minLecture29.1
Support Vector Machines (SVM) 0/15
- Geometric Intution 20 minLecture30.1
- Why we take values +1 and and -1 for Support vector planes 09 minLecture30.2
- Mathematical derivation 32 minLecture30.3
- Loss function (Hinge Loss) based interpretation 18 minLecture30.4
- Dual form of SVM formulation 16 minLecture30.5
- kernel trick 10 minLecture30.6
- Polynomial kernel 11 minLecture30.7
- RBF-Kernel 21 minLecture30.8
- Domain specific Kernels 06 minLecture30.9
- Train and run time complexities 08 minLecture30.10
- nu-SVM: control errors and support vectors 06 minLecture30.11
- SVM Regression 08 minLecture30.12
- Cases 09 minLecture30.13
- Code Samples 14 minLecture30.14
- Exercise: Apply SVM to Amazon reviews dataset 04 minLecture30.15
Interview Questions on Support Vector Machine 0/0
- Questions & Answers 30 minLecture31.1
Decision Trees 0/15
- Geometric Intuition of decision tree: Axis parallel hyperplanes 17 minLecture32.1
- Sample Decision tree 08 minLecture32.2
- Building a decision Tree:Entropy 19 minLecture32.3
- Building a decision Tree:Information Gain 10 minLecture32.4
- Building a decision Tree: Gini Impurity 07 minLecture32.5
- Building a decision Tree: Constructing a DT 21 minLecture32.6
- Building a decision Tree: Splitting numerical features 08 minLecture32.7
- Feature standardization 04 minLecture32.8
- Building a decision Tree:Categorical features with many possible values 07 minLecture32.9
- Overfitting and Underfitting 08 minLecture32.10
- Train and Run time complexity 07 minLecture32.11
- Regression using Decision Trees 09 minLecture32.12
- Cases 12 minLecture32.13
- Code Samples 09 minLecture32.14
- Exercise: Decision Trees on Amazon reviews dataset 03 minLecture32.15
Interview Questions on decision Trees 0/0
- Questions & Answers 30 minLecture33.1
Ensemble Models 0/19
- What are ensembles? 06 minLecture34.1
- Bootstrapped Aggregation (Bagging) Intuition 17 minLecture34.2
- Random Forest and their construction 15 minLecture34.3
- Bias-Variance tradeoff 07 minLecture34.4
- Train and run time complexity 09 minLecture34.5
- Bagging:Code Sample 04 minLecture34.6
- Extremely randomized trees 08 minLecture34.7
- Random Tree :Cases 06 minLecture34.8
- Boosting Intuition 17 minLecture34.9
- Residuals, Loss functions and gradients 13 minLecture34.10
- Gradient Boosting 10 minLecture34.11
- Regularization by Shrinkage 08 minLecture34.12
- Train and Run time complexity 06 minLecture34.13
- XGBoost: Boosting + Randomization 14 minLecture34.14
- AdaBoost: geometric intuition 07 minLecture34.15
- Stacking models 22 minLecture34.16
- Cascading classifiers 15 minLecture34.17
- Kaggle competitions vs Real world 09 minLecture34.18
- Exercise: Apply GBDT and RF to Amazon reviews dataset. 04 minLecture34.19
K-Means Clustering Technique 0/11
- Geometric intuition, CentroidsLecture35.1
- Mathematical formulation: Objective functionLecture35.2
- K-Means AlgoithmLecture35.3
- How to initialize: K-Means++Lecture35.4
- Failure cases/LimitationsLecture35.5
- K-MedoidsLecture35.6
- Kernel K-Means and Spectral ClusteringLecture35.7
- Determining the right KLecture35.8
- Time and space complexityLecture35.9
- Code SamplesLecture35.10
- Exercise: Cluster Amazon reviewsLecture35.11
Bias-Variance tradeoff 0/3
- Intuition: Underfit and OverfitLecture36.1
- Derivation for linear regressionLecture36.2
- Bias Variance tradeoff for k-NN, NaiveBayes, Logistic Regression, Linear regressionLecture36.3
Hierarchical clustering Technique 0/4
- Agglomerative vs DivisiveLecture37.1
- Agglomerative AlgorithmLecture37.2
- MIN, MAX, Average methodsLecture37.3
- Advantages and LimitationsLecture37.4
DBSCAN (Density based clustering) Technique 0/6
- MinPts and Eps: DensityLecture38.1
- Core, Border and Noise pointsLecture38.2
- Density edge and Density connected pointsLecture38.3
- DBSCAN AlgorithmLecture38.4
- Determining the optimal Hyper Parameters: MinPts and EpsLecture38.5
- Sensitivity issues of DBSCANLecture38.6
Recommender Systems and Matrix Factorization 0/4
- Problem formulation: IMDB Movie reviewsLecture39.1
- Content based vs Collaborative FilteringLecture39.2
- Item-Item and User-User Similarity based AlgorithmsLecture39.3
- Introduction to Matrix FactorizationLecture39.4
Python for Data Science: OOPS(object oriented program) 0/2
- Principles of Object oriented programmingLecture40.1
- classes and objectsLecture40.2
Python for Data Science: Seaborn 0/1
- Getting Started with SeabornLecture41.1
Python for Data Science: Scikit Learn 0/1
- Getting started with scikit learnLecture42.1
Interview Questions on Ensemble Models 0/0
- Questions & Answers 30 minLecture43.1
Facebook Friend Recommendation using Graph Mining 0/19
- Problem definition 06 minLecture44.1
- Overview of Graphs: node/vertex, edge/link, directed-edge, path. 11 minLecture44.2
- Data format & Limitations. 09 minLecture44.3
- Mapping to a supervised classification problem. 09 minLecture44.4
- Business constraints & Metrics. 07 minLecture44.5
- EDA:Basic Stats 01 hour 01 minLecture44.6
- EDA:Follower and following stats. 12 minLecture44.7
- EDA:Binary Classification Task 16 minLecture44.8
- EDA:Train and test split. 39 minLecture44.9
- Feature engineering on Graphs:Jaccard & Cosine Similarities 15 minLecture44.10
- PageRank 14 minLecture44.11
- Shortest Path 04 minLecture44.12
- Connected-components 12 minLecture44.13
- Adar Index 12 minLecture44.14
- Kartz Centrality 06 minLecture44.15
- HITS Score 10 minLecture44.16
- SVD 11 minLecture44.17
- Weight features 06 minLecture44.18
- Modeling 10 minLecture44.19
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