Канал: Mathematics behind Data Science

Build a Simple Recurrent Neural Network Using PyTorch | Depth Analysis | Deep Learning | PyTorch

Build a Simple Recurrent Neural Network Using PyTorch | Depth Analysis | Deep Learning | PyTorch

Build a Simple Neural Network Using PyTorch | Deep Learning | PyTorch

Build a Simple Neural Network Using PyTorch | Deep Learning | PyTorch

Russia-Ukraine war - Tweets Analysis | Machine Learning Projects for Beginners | #16

Russia-Ukraine war - Tweets Analysis | Machine Learning Projects for Beginners | #16

Math behind Training a single Neuron | Lec 8 | Neural Networks and Backpropagation | Deep Learning

Math behind Training a single Neuron | Lec 8 | Neural Networks and Backpropagation | Deep Learning

Understand Notations | Lec 7 | Introduction to Neural Networks and Backpropagation | Deep Learning

Understand Notations | Lec 7 | Introduction to Neural Networks and Backpropagation | Deep Learning

Complex Math. Function | Lec 6 | Introduction to Neural Networks and Backpropagation | Deep Learning

Complex Math. Function | Lec 6 | Introduction to Neural Networks and Backpropagation | Deep Learning

Introduction to MLP | Lec 5 | Introduction to Neural Networks and Backpropagation | Deep Learning

Introduction to MLP | Lec 5 | Introduction to Neural Networks and Backpropagation | Deep Learning

Log. Reg vs Perceptron | Lec 4 | Introduction to Neural Networks and Backpropagation | Deep Learning

Log. Reg vs Perceptron | Lec 4 | Introduction to Neural Networks and Backpropagation | Deep Learning

Work and Growth | Lec 3 | Introduction to Neural Networks and Backpropagation | Deep Learning

Work and Growth | Lec 3 | Introduction to Neural Networks and Backpropagation | Deep Learning

Various Applications | Lec 2 | Introduction to Neural Networks and Backpropagation | Deep Learning

Various Applications | Lec 2 | Introduction to Neural Networks and Backpropagation | Deep Learning

History and Motivation | Lec 1 | Introduction to Neural Networks and Backpropagation | Deep Learning

History and Motivation | Lec 1 | Introduction to Neural Networks and Backpropagation | Deep Learning

Uber Trips Analysis | Machine Learning Projects for Beginners | #15

Uber Trips Analysis | Machine Learning Projects for Beginners | #15

Language Classification | Machine Learning Projects for Beginners | #14

Language Classification | Machine Learning Projects for Beginners | #14

Titanic Survival Prediction | Machine Learning Projects for Beginners | #13

Titanic Survival Prediction | Machine Learning Projects for Beginners | #13

Book Recommendation System | Machine Learning Projects for Beginners | #12

Book Recommendation System | Machine Learning Projects for Beginners | #12

SMS Spam Detection | Machine Learning Projects for Beginners | #11

SMS Spam Detection | Machine Learning Projects for Beginners | #11

Resume Screening | Machine Learning Projects for Beginners | #10

Resume Screening | Machine Learning Projects for Beginners | #10

US Election Sentiment Analysis using tweet | Machine Learning Projects for Beginners | #9

US Election Sentiment Analysis using tweet | Machine Learning Projects for Beginners | #9

Web Scraping | Machine Learning Projects for Beginners | #8

Web Scraping | Machine Learning Projects for Beginners | #8

Predict Diabetes | Machine Learning Projects for Beginners | #7

Predict Diabetes | Machine Learning Projects for Beginners | #7

Make a Simple Chatbot | Machine Learning Projects for Beginners | #6

Make a Simple Chatbot | Machine Learning Projects for Beginners | #6

Text Summarization | Machine Learning Projects for Beginners | #5

Text Summarization | Machine Learning Projects for Beginners | #5

Covid-19 Vaccines Analysis | Machine Learning Projects for Beginners | #4

Covid-19 Vaccines Analysis | Machine Learning Projects for Beginners | #4

WhatsApp Chat Sentiment Analysis | Machine Learning Projects for Beginners | #3

WhatsApp Chat Sentiment Analysis | Machine Learning Projects for Beginners | #3

Google Search Analysis | Machine Learning Projects for Beginners | #2

Google Search Analysis | Machine Learning Projects for Beginners | #2

World Billionaires Analysis 2021 | Machine Learning Projects for Beginners | #1

World Billionaires Analysis 2021 | Machine Learning Projects for Beginners | #1

Data Science Life Cycle | Productionization and Deployment of ML Models | EP #10

Data Science Life Cycle | Productionization and Deployment of ML Models | EP #10

Vapnik–Chervonenkis(VC) Dimension | Productionization and Deployment of ML Models | EP #9

Vapnik–Chervonenkis(VC) Dimension | Productionization and Deployment of ML Models | EP #9

AB testing | Productionization and Deployment of ML Models | EP #8

AB testing | Productionization and Deployment of ML Models | EP #8

Productionizing Models | Productionization and Deployment of ML Models | EP #6

Productionizing Models | Productionization and Deployment of ML Models | EP #6

Retraining Models Periodically | Productionization and Deployment of ML Models | EP #7

Retraining Models Periodically | Productionization and Deployment of ML Models | EP #7

Modeling in the presence of outliers: RANSAC | Productionization and Deployment of ML Models | EP #5

Modeling in the presence of outliers: RANSAC | Productionization and Deployment of ML Models | EP #5

Isotonic Calibration | Productionization and Deployment of ML Models | EP #4

Isotonic Calibration | Productionization and Deployment of ML Models | EP #4

Platt’s Calibration Scaling | Productionization and Deployment of ML Models | EP #3

Platt’s Calibration Scaling | Productionization and Deployment of ML Models | EP #3

Calibration Plots | Productionization and Deployment of ML Models | EP #2

Calibration Plots | Productionization and Deployment of ML Models | EP #2

Why need for Calibration ? | Productionization and Deployment of ML Models | EP #1

Why need for Calibration ? | Productionization and Deployment of ML Models | EP #1

Feature Slicing | Feature Engineering | EP #15

Feature Slicing | Feature Engineering | EP #15

Domain Specific Featurization | Feature Engineering | EP #16

Domain Specific Featurization | Feature Engineering | EP #16

Feature Orthogonality | Feature Engineering | EP #14

Feature Orthogonality | Feature Engineering | EP #14

Model Specific Featurization | Feature Engineering | EP #13

Model Specific Featurization | Feature Engineering | EP #13

Mathematical Transformation | Feature Engineering | EP #12

Mathematical Transformation | Feature Engineering | EP #12

Interaction Variables | Feature Engineering | EP #11

Interaction Variables | Feature Engineering | EP #11

Feature Binning | Feature Engineering | EP #10

Feature Binning | Feature Engineering | EP #10

Indicator Variables | Feature Engineering | EP #9

Indicator Variables | Feature Engineering | EP #9

Relational Data | Feature Engineering | EP #7

Relational Data | Feature Engineering | EP #7

Graph Data | Feature Engineering | EP #8

Graph Data | Feature Engineering | EP #8

Image Histogram | Feature Engineering | EP #5

Image Histogram | Feature Engineering | EP #5

Keypoints SIFT | Feature Engineering | EP #6

Keypoints SIFT | Feature Engineering | EP #6

Deep learning features (LSTM) | Feature Engineering | EP #4

Deep learning features (LSTM) | Feature Engineering | EP #4

Fourier Decomposition | Feature Engineering | EP #3

Fourier Decomposition | Feature Engineering | EP #3

Moving Window for Time Series Data | Feature Engineering | EP #2

Moving Window for Time Series Data | Feature Engineering | EP #2

Types of Data | Feature Engineering | EP #1

Types of Data | Feature Engineering | EP #1

Avg-Word2Vec, tf-idf weighted Word2Vec | Natural Language Processing | EP #8

Avg-Word2Vec, tf-idf weighted Word2Vec | Natural Language Processing | EP #8

Word2Vec | Natural Language Processing | EP #7

Word2Vec | Natural Language Processing | EP #7

Why use log in IDF ? | Natural Language Processing | EP #6

Why use log in IDF ? | Natural Language Processing | EP #6

Term Frequency(TF), Inverse Document Frequency(IDF) | Natural Language Processing | EP #5

Term Frequency(TF), Inverse Document Frequency(IDF) | Natural Language Processing | EP #5

Uni-gram, Bi-gram, n-grams | Natural Language Processing | EP #4

Uni-gram, Bi-gram, n-grams | Natural Language Processing | EP #4

Text Pre-processing Stemming | Natural Language Processing | EP #3

Text Pre-processing Stemming | Natural Language Processing | EP #3

Bag of Words (BoW) | Natural Language Processing | EP #2

Bag of Words (BoW) | Natural Language Processing | EP #2

Why convert text to a vector ? | Natural Language Processing | EP #1

Why convert text to a vector ? | Natural Language Processing | EP #1

Eigen Faces | Recommendation System and Matrix Factorization | EP #12

Eigen Faces | Recommendation System and Matrix Factorization | EP #12

Word Vector as Matrix Factorization | Recommendation System and Matrix Factorization | EP #11

Word Vector as Matrix Factorization | Recommendation System and Matrix Factorization | EP #11

Cold Start Problem | Recommendation System and Matrix Factorization | EP #10

Cold Start Problem | Recommendation System and Matrix Factorization | EP #10

Netflix Prize Solution | Recommendation System and Matrix Factorization | EP #13

Netflix Prize Solution | Recommendation System and Matrix Factorization | EP #13

Hyperparameter Tuning | Recommendation System and Matrix Factorization | EP #9

Hyperparameter Tuning | Recommendation System and Matrix Factorization | EP #9

Clustering as Matrix Factorization | Recommendation System and Matrix Factorization | EP #8

Clustering as Matrix Factorization | Recommendation System and Matrix Factorization | EP #8

MF for Feature Engineering | Recommendation System and Matrix Factorization | EP #7

MF for Feature Engineering | Recommendation System and Matrix Factorization | EP #7

MF for Collaborative Filtering | Recommendation System and Matrix Factorization | EP #6

MF for Collaborative Filtering | Recommendation System and Matrix Factorization | EP #6

Similarity based Algorithms | Recommendation System and Matrix Factorization | EP #3

Similarity based Algorithms | Recommendation System and Matrix Factorization | EP #3

Matrix Factorization for PCA, SVD | Recommendation System and Matrix Factorization | EP #4

Matrix Factorization for PCA, SVD | Recommendation System and Matrix Factorization | EP #4

Non Negative Matrix Factorization | Recommendation System and Matrix Factorization | EP #5

Non Negative Matrix Factorization | Recommendation System and Matrix Factorization | EP #5

Content-Based vs Collaborative Filtering | Recommendation System and Matrix Factorization | EP #2

Content-Based vs Collaborative Filtering | Recommendation System and Matrix Factorization | EP #2

What is Recommender System ? | Recommendation System and Matrix Factorization | EP #1

What is Recommender System ? | Recommendation System and Matrix Factorization | EP #1

Advantages and Limitations | Density-based Clustering: DBSCAN | EP #7

Advantages and Limitations | Density-based Clustering: DBSCAN | EP #7

Time and Space Complexity | Density-based Clustering: DBSCAN | EP #8

Time and Space Complexity | Density-based Clustering: DBSCAN | EP #8

Choose Hyper Parameters: MinPts and Eps | Density-based Clustering: DBSCAN | EP #6

Choose Hyper Parameters: MinPts and Eps | Density-based Clustering: DBSCAN | EP #6

How it works? | Density-based Clustering: DBSCAN | EP #5

How it works? | Density-based Clustering: DBSCAN | EP #5

Density Edge and Density Connected Points | Density-based Clustering: DBSCAN | EP #4

Density Edge and Density Connected Points | Density-based Clustering: DBSCAN | EP #4

Core, Border and Noise points | Density-based Clustering: DBSCAN | EP #3

Core, Border and Noise points | Density-based Clustering: DBSCAN | EP #3

MinPts and Eps | Density-based Clustering: DBSCAN | EP #2

MinPts and Eps | Density-based Clustering: DBSCAN | EP #2

Density-based Clustering: DBSCAN | EP #1

Density-based Clustering: DBSCAN | EP #1

Limitations | Hierarchical Clustering | EP #5

Limitations | Hierarchical Clustering | EP #5

Proximity Methods: Advantages and Limitations | Hierarchical Clustering | EP #3

Proximity Methods: Advantages and Limitations | Hierarchical Clustering | EP #3

Time and Space Complexity | Hierarchical Clustering | EP #4

Time and Space Complexity | Hierarchical Clustering | EP #4

Agglomerative & Divisive, Dendrograms | Hierarchical Clustering | EP #1

Agglomerative & Divisive, Dendrograms | Hierarchical Clustering | EP #1

Agglomerative Clustering | Hierarchical Clustering | EP #2

Agglomerative Clustering | Hierarchical Clustering | EP #2

Time and Space Complexity | K-Means Clustering | EP #11

Time and Space Complexity | K-Means Clustering | EP #11

K-Medoids | K-Means Clustering | EP #9

K-Medoids | K-Means Clustering | EP #9

Determining the right K | K-Means Clustering | EP #10

Determining the right K | K-Means Clustering | EP #10

How to initialize K-Means++ ? | K-Means Clustering | EP #7

How to initialize K-Means++ ? | K-Means Clustering | EP #7

Limitations | K-Means Clustering | EP #8

Limitations | K-Means Clustering | EP #8

Lloyd's Algorithm | K-Means Clustering | EP #6

Lloyd's Algorithm | K-Means Clustering | EP #6

Mathematical formulation Objective function | K-Means Clustering | EP #5

Mathematical formulation Objective function | K-Means Clustering | EP #5

K-Means Geometric intuition, Centroids | K-Means Clustering | EP #4

K-Means Geometric intuition, Centroids | K-Means Clustering | EP #4

Metrics for Clustering | K-Means Clustering | EP #3

Metrics for Clustering | K-Means Clustering | EP #3

Unsupervised Learning and its application | K-Means Clustering | EP #2

Unsupervised Learning and its application | K-Means Clustering | EP #2

What is Clustering ? | K-Means Clustering | EP #1

What is Clustering ? | K-Means Clustering | EP #1

Proportional Sampling | Probability and Statistics | Lec 34

Proportional Sampling | Probability and Statistics | Lec 34

How to use hypothesis testing ? | Probability and Statistics | Lec 33

How to use hypothesis testing ? | Probability and Statistics | Lec 33

Resampling and Permutation Test (Another Example) | Probability and Statistics | Lec 32

Resampling and Permutation Test (Another Example) | Probability and Statistics | Lec 32

Hypothesis testing (Another Example) | Probability and Statistics | Lec 31

Hypothesis testing (Another Example) | Probability and Statistics | Lec 31

K-S Test for similarity of two Distributions | Probability and Statistics | Lec 30

K-S Test for similarity of two Distributions | Probability and Statistics | Lec 30

Resampling and permutation test | Probability and Statistics | Lec 29

Resampling and permutation test | Probability and Statistics | Lec 29

Hypothesis Testing Intuition with coin toss example | Probability and Statistics | Lec 28

Hypothesis Testing Intuition with coin toss example | Probability and Statistics | Lec 28

Hypothesis testing methodology, Null-hypothesis, p-value | Probability and Statistics | Lec 27

Hypothesis testing methodology, Null-hypothesis, p-value | Probability and Statistics | Lec 27

Confidence Interval using Bootstrapping | Probability and Statistics | Lec 26

Confidence Interval using Bootstrapping | Probability and Statistics | Lec 26

Confidence Interval of mean for any Random Variable | Probability and Statistics | Lec 25

Confidence Interval of mean for any Random Variable | Probability and Statistics | Lec 25

Computing Confidence Interval (Normal Distribution) | Probability and Statistics | Lec 24

Computing Confidence Interval (Normal Distribution) | Probability and Statistics | Lec 24

Confidence Interval (C.I) Introduction | Probability and Statistics | Lec 23

Confidence Interval (C.I) Introduction | Probability and Statistics | Lec 23

How to use Correlations? | Probability and Statistics | Lec 22

How to use Correlations? | Probability and Statistics | Lec 22

Correlation vs Causation | Probability and Statistics | Lec 21

Correlation vs Causation | Probability and Statistics | Lec 21

Pearson Correlation Coefficient | Probability and Statistics | Lec 19

Pearson Correlation Coefficient | Probability and Statistics | Lec 19

Spearman Rank Correlation Coefficient | Probability and Statistics | Lec 20

Spearman Rank Correlation Coefficient | Probability and Statistics | Lec 20

Co-Variance | Probability and Statistics | Lec 18

Co-Variance | Probability and Statistics | Lec 18

Applications of Non-Gaussian Distributions | Probability and Statistics | Lec 17

Applications of Non-Gaussian Distributions | Probability and Statistics | Lec 17

Box Cox Transform | Probability and Statistics | Lec 16

Box Cox Transform | Probability and Statistics | Lec 16

Power Law Distribution | Probability and Statistics | Lec 15

Power Law Distribution | Probability and Statistics | Lec 15

Log Normal Distribution | Probability and Statistics | Lec 14

Log Normal Distribution | Probability and Statistics | Lec 14

Bernoulli and Binomial Distribution | Probability and Statistics | Lec 13

Bernoulli and Binomial Distribution | Probability and Statistics | Lec 13

Discrete and Continuous Uniform Distributions | Probability and Statistics | Lec 12

Discrete and Continuous Uniform Distributions | Probability and Statistics | Lec 12

Chebyshev’s Inequality | Probability and Statistics | Lec 11

Chebyshev’s Inequality | Probability and Statistics | Lec 11

How distributions are used ? | Probability and Statistics | Lec 10

How distributions are used ? | Probability and Statistics | Lec 10

Q-Q plot | Probability and Statistics | Lec 9

Q-Q plot | Probability and Statistics | Lec 9

Sampling Distribution & Central Limit Theorem | Probability and Statistics | Lec 8

Sampling Distribution & Central Limit Theorem | Probability and Statistics | Lec 8

Kernel Density Estimation | Probability and Statistics | Lec 7

Kernel Density Estimation | Probability and Statistics | Lec 7

CDF of Gaussian Normal distribution | Probability and Statistics | Lec 4

CDF of Gaussian Normal distribution | Probability and Statistics | Lec 4

Standard normal variate and Standardization | Probability and Statistics | Lec 6

Standard normal variate and Standardization | Probability and Statistics | Lec 6

Symmetric distribution, Skewness and Kurtosis | Probability and Statistics | Lec 5

Symmetric distribution, Skewness and Kurtosis | Probability and Statistics | Lec 5

Introduction to Probability and Statistics | Probability and Statistics | Lec 1

Introduction to Probability and Statistics | Probability and Statistics | Lec 1

Gaussian Normal Distribution | Probability and Statistics | Lec 3

Gaussian Normal Distribution | Probability and Statistics | Lec 3

Population and Sample | Probability and Statistics | Lec 2

Population and Sample | Probability and Statistics | Lec 2

Cascading Classifiers | Ensemble Models | Lec 15

Cascading Classifiers | Ensemble Models | Lec 15

Stacking Models | Ensemble Models | Lec 14

Stacking Models | Ensemble Models | Lec 14

AdaBoost Geometric Intuition | Ensemble Models | Lec 13

AdaBoost Geometric Intuition | Ensemble Models | Lec 13

Train and Run time complexity (Gradient Boosting Decision Tree) | Ensemble Models | Lec 12

Train and Run time complexity (Gradient Boosting Decision Tree) | Ensemble Models | Lec 12

Regularization by Shrinkage | Ensemble Models | Lec 11

Regularization by Shrinkage | Ensemble Models | Lec 11

Gradient Boosting | Ensemble Models | Lec 10

Gradient Boosting | Ensemble Models | Lec 10

Boosting Intuition | Ensemble Models | Lec 8

Boosting Intuition | Ensemble Models | Lec 8

Residuals, Loss functions and gradients | Ensemble Models | Lec 9

Residuals, Loss functions and gradients | Ensemble Models | Lec 9

Random Tree Cases | Ensemble Models | Lec 7

Random Tree Cases | Ensemble Models | Lec 7

Extremely randomized trees | Ensemble Models | Lec 6

Extremely randomized trees | Ensemble Models | Lec 6

Train and run time complexity | Ensemble Models | Lec 5

Train and run time complexity | Ensemble Models | Lec 5

Bias-Variance trade-off | Ensemble Models | Lec 4

Bias-Variance trade-off | Ensemble Models | Lec 4

Random Forest and its Construction | Ensemble Models | Lec 3

Random Forest and its Construction | Ensemble Models | Lec 3

Bootstrapped Aggregation (Bagging) Intuition | Ensemble Models | Lec 2

Bootstrapped Aggregation (Bagging) Intuition | Ensemble Models | Lec 2

What are ensembles ? | Ensemble Models | Lec 1

What are ensembles ? | Ensemble Models | Lec 1

Some real world Cases | Decision Tree | Lec 13

Some real world Cases | Decision Tree | Lec 13

Regression | Decision Tree | Lec 12

Regression | Decision Tree | Lec 12

Train and Run time and space complexity | Decision Tree | Lec 11

Train and Run time and space complexity | Decision Tree | Lec 11

Overfitting and Underfitting | Decision Tree | Lec 10

Overfitting and Underfitting | Decision Tree | Lec 10

Categorical features with many possible values | Decision Tree | Lec 9

Categorical features with many possible values | Decision Tree | Lec 9

Do we need Feature Standardization ? | Decision Tree | Lec 8

Do we need Feature Standardization ? | Decision Tree | Lec 8

Splitting Numerical Features | Decision Tree | Lec 7

Splitting Numerical Features | Decision Tree | Lec 7

Decision Tree Constructing | Decision Tree | Lec 6

Decision Tree Constructing | Decision Tree | Lec 6

Gini Impurity | Decision Tree | Lec 5

Gini Impurity | Decision Tree | Lec 5

Information Gain | Decision Tree | Lec 4

Information Gain | Decision Tree | Lec 4

Entropy | Decision Tree | Lec 3

Entropy | Decision Tree | Lec 3

Sample Example | Decision Tree | Lec 2

Sample Example | Decision Tree | Lec 2

Geometric Intuition | Decision Tree | Lec 1

Geometric Intuition | Decision Tree | Lec 1

Probabilistic Class Label | K Nearest Neighbors | Lec 27

Probabilistic Class Label | K Nearest Neighbors | Lec 27

Locality Sensitive Hashing for Euclidean Distance | K Nearest Neighbors | Lec 26

Locality Sensitive Hashing for Euclidean Distance | K Nearest Neighbors | Lec 26

Locality Sensitive Hashing for Cosine Similarity | K Nearest Neighbors | Lec 25

Locality Sensitive Hashing for Cosine Similarity | K Nearest Neighbors | Lec 25

Hashing vs Locality Sensitive Hashing | K Nearest Neighbors | Lec 24

Hashing vs Locality Sensitive Hashing | K Nearest Neighbors | Lec 24

Limitations of KD tree | K Nearest Neighbors | Lec 23

Limitations of KD tree | K Nearest Neighbors | Lec 23

Find nearest neighbors using KD-Tree | K Nearest Neighbors | Lec 22

Find nearest neighbors using KD-Tree | K Nearest Neighbors | Lec 22

How to build a KD-Tree? | K Nearest Neighbors | Lec 21

How to build a KD-Tree? | K Nearest Neighbors | Lec 21

Binary Search Tree | K Nearest Neighbors | Lec 20

Binary Search Tree | K Nearest Neighbors | Lec 20

Weighted k-NN | K Nearest Neighbors | Lec 19

Weighted k-NN | K Nearest Neighbors | Lec 19

Regression | K Nearest Neighbors | Lec 18

Regression | K Nearest Neighbors | Lec 18

Time Based Splitting | K Nearest Neighbors | Lec 17

Time Based Splitting | K Nearest Neighbors | Lec 17

How to determine Overfitting and Underfitting? | K Nearest Neighbors | Lec 16

How to determine Overfitting and Underfitting? | K Nearest Neighbors | Lec 16

K-fold Cross Validation | K Nearest Neighbors | Lec 14

K-fold Cross Validation | K Nearest Neighbors | Lec 14

Visualizing Train, Validation and Test datasets | K Nearest Neighbors | Lec 15

Visualizing Train, Validation and Test datasets | K Nearest Neighbors | Lec 15

Why need for Cross validation? | K Nearest Neighbors | Lec 13

Why need for Cross validation? | K Nearest Neighbors | Lec 13

Overfitting and Underfitting | K Nearest Neighbors | Lec 12

Overfitting and Underfitting | K Nearest Neighbors | Lec 12

Decision surface as K changes | K Nearest Neighbors | Lec 11

Decision surface as K changes | K Nearest Neighbors | Lec 11

How to measure the effectiveness of k-NN? | K Nearest Neighbors | Lec 8

How to measure the effectiveness of k-NN? | K Nearest Neighbors | Lec 8

Limitations | K Nearest Neighbors | Lec 10

Limitations | K Nearest Neighbors | Lec 10

Test Evaluation Time and Space complexity | K Nearest Neighbors | Lec 9

Test Evaluation Time and Space complexity | K Nearest Neighbors | Lec 9

Cosine Distance & Cosine Similarity | K Nearest Neighbors | Lec 7

Cosine Distance & Cosine Similarity | K Nearest Neighbors | Lec 7

Distance Measures | K Nearest Neighbors | Lec 6

Distance Measures | K Nearest Neighbors | Lec 6

Failure Cases | K Nearest Neighbors | Lec 5

Failure Cases | K Nearest Neighbors | Lec 5

K-Nearest Neighbors Geometric Intuition | K Nearest Neighbors | Lec 4

K-Nearest Neighbors Geometric Intuition | K Nearest Neighbors | Lec 4

Classification vs Regression (examples) | K Nearest Neighbors | Lec 3

Classification vs Regression (examples) | K Nearest Neighbors | Lec 3

Data Matrix Notation | K Nearest Neighbors | Lec 2

Data Matrix Notation | K Nearest Neighbors | Lec 2

How “Classification” works | K Nearest Neighbors | Lec 1

How “Classification” works | K Nearest Neighbors | Lec 1

Best and Worst Cases | Naïve Bayes | Lec 18

Best and Worst Cases | Naïve Bayes | Lec 18

Large Dimensionality | Naïve Bayes | Lec 17

Large Dimensionality | Naïve Bayes | Lec 17

Similarity or Distance matrix | Naïve Bayes | Lec 16

Similarity or Distance matrix | Naïve Bayes | Lec 16

Multiclass Classification | Naïve Bayes | Lec 15

Multiclass Classification | Naïve Bayes | Lec 15

Handling Numerical features (Gaussian NB) | Naïve Bayes | Lec 14

Handling Numerical features (Gaussian NB) | Naïve Bayes | Lec 14

Missing Values | Naïve Bayes | Lec 13

Missing Values | Naïve Bayes | Lec 13

Imbalanced Data | Naïve Bayes | Lec 11

Imbalanced Data | Naïve Bayes | Lec 11

Bias and Variance trade off | Naïve Bayes | Lec 9

Bias and Variance trade off | Naïve Bayes | Lec 9

Outliers | Naïve Bayes | Lec 12

Outliers | Naïve Bayes | Lec 12

Laplace Additive Smoothing | Naïve Bayes | Lec 7

Laplace Additive Smoothing | Naïve Bayes | Lec 7

Feature Importance and Interpretability | Naïve Bayes | Lec 10

Feature Importance and Interpretability | Naïve Bayes | Lec 10

Log-Probabilities for numerical stability | Naïve Bayes | Lec 8

Log-Probabilities for numerical stability | Naïve Bayes | Lec 8

Naïve Bayes on Text Data | Naive Bayes | Lec 6

Naïve Bayes on Text Data | Naive Bayes | Lec 6

Toy example Train and Test stages | Naive Bayes | Lec 5

Toy example Train and Test stages | Naive Bayes | Lec 5