Канал: Statistical Learning and Data Science

I2ML - Supervised Classification - Linear Classifiers

I2ML - Supervised Classification - Linear Classifiers

I2ML - Supervised Classification - Naive Bayes

I2ML - Supervised Classification - Naive Bayes

I2ML - Supervised Classification - Discriminant Analysis

I2ML - Supervised Classification - Discriminant Analysis

I2ML - Supervised Classification - Basic Definitions

I2ML - Supervised Classification - Basic Definitions

I2ML - Supervised Classification - Logistic Regression

I2ML - Supervised Classification - Logistic Regression

I2ML - Supervised Classification - Tasks

I2ML - Supervised Classification - Tasks

I2ML - Random Forest - Bagging Ensembles

I2ML - Random Forest - Bagging Ensembles

I2ML - Random Forests - Out-of-Bag Error Estimate

I2ML - Random Forests - Out-of-Bag Error Estimate

I2ML - Random Forest - Proximities

I2ML - Random Forest - Proximities

I2ML - Random Forest - Feature Importance

I2ML - Random Forest - Feature Importance

I2ML - Random Forest - Basics

I2ML - Random Forest - Basics

I2ML - Tuning - In a Nutshell

I2ML - Tuning - In a Nutshell

SL - Regularization - Geometry of L2 Regularization

SL - Regularization - Geometry of L2 Regularization

SL - Regularization - Non-Linear Models and Structural Risk Minimization

SL - Regularization - Non-Linear Models and Structural Risk Minimization

SL - Regularization - Early Stopping

SL - Regularization - Early Stopping

SL - Regularization - Bayesian Priors

SL - Regularization - Bayesian Priors

SL - Regularization - Geometry of L1 Regularization

SL - Regularization - Geometry of L1 Regularization

SL - Regularization - Weight Decay and L2

SL - Regularization - Weight Decay and L2

SL - Regularization - Elastic Net and regularized GLMs

SL - Regularization - Elastic Net and regularized GLMs

SL - Regularization - Introduction

SL - Regularization - Introduction

SL - Regularization - Ridge Regression

SL - Regularization - Ridge Regression

SL - Regularization - Lasso vs. Ridge

SL - Regularization - Lasso vs. Ridge

SL - Regularization - Lasso Regression

SL - Regularization - Lasso Regression

SL - Regularization - Other Regularizers

SL - Regularization - Other Regularizers

SL - Information Theory - Information Theory for Machine Learning

SL - Information Theory - Information Theory for Machine Learning

SL - Information Theory - Joint Entropy and Mutual Information II

SL - Information Theory - Joint Entropy and Mutual Information II

SL - Information Theory - Joint Entropy and Mutual Information I

SL - Information Theory - Joint Entropy and Mutual Information I

SL - Information Theory - KL Divergence

SL - Information Theory - KL Divergence

SL - Information Theory - Cross Entropy and KL

SL - Information Theory - Cross Entropy and KL

SL - Information Theory - Differential Entropy

SL - Information Theory - Differential Entropy

SL - Information Theory - Entropy II

SL - Information Theory - Entropy II

SL - Information Theory - Entropy I

SL - Information Theory - Entropy I

I2ML - Evaluation - In a Nutshell

I2ML - Evaluation - In a Nutshell

SL - Advanced Risk Minimization - Properties of Loss Functions

SL - Advanced Risk Minimization - Properties of Loss Functions

SL - Advanced Risk Minimization - Bias-Variance Decomposition

SL - Advanced Risk Minimization - Bias-Variance Decomposition

SL - Advanced Risk Minimization - MLE vs ERM II

SL - Advanced Risk Minimization - MLE vs ERM II

SL - Advanced Risk Minimization - MLE vs ERM I

SL - Advanced Risk Minimization - MLE vs ERM I

I2ML - Neural Networks - In a Nutshell

I2ML - Neural Networks - In a Nutshell

I2ML - Random Forests - In a Nutshell

I2ML - Random Forests - In a Nutshell

I2ML - CART - In a Nutshell

I2ML - CART - In a Nutshell

I2ML - Supervised Classification - In a Nutshell

I2ML - Supervised Classification - In a Nutshell

I2ML - Supervised Regression - In a Nutshell

I2ML - Supervised Regression - In a Nutshell

I2ML - ML Basics - In a Nutshell

I2ML - ML Basics - In a Nutshell

SL - Feature Selection - Wrapper methods

SL - Feature Selection - Wrapper methods

SL - Feature Selection - Filter methods (Examples and caveats)

SL - Feature Selection - Filter methods (Examples and caveats)

Advanced Machine Learning - Online learning - Simple learning algorithms

Advanced Machine Learning - Online learning - Simple learning algorithms

Advanced Machine Learning - Online learning - Online Convex Optimization 2

Advanced Machine Learning - Online learning - Online Convex Optimization 2

Advanced Machine Learning - Online learning - Online Convex Optimization 1

Advanced Machine Learning - Online learning - Online Convex Optimization 1

Advanced Machine Learning - Online learning - Follow the regularized leader

Advanced Machine Learning - Online learning - Follow the regularized leader

SL - Feature Selection - Introduction

SL - Feature Selection - Introduction

SL - Feature Selection - Motivating examples

SL - Feature Selection - Motivating examples

SL - Feature Selection - Filter methods

SL - Feature Selection - Filter methods

Advanced Machine Learning - Online learning - Follow the Leader on OQO problems

Advanced Machine Learning - Online learning - Follow the Leader on OQO problems

Advanced Machine Learning - Online learning - Follow the leader on OLO problems

Advanced Machine Learning - Online learning - Follow the leader on OLO problems

SL - Gradient Boosting - Advanced CWB

SL - Gradient Boosting - Advanced CWB

SL - Gradient Boosting - Component-Wise-Boosting Basics 2

SL - Gradient Boosting - Component-Wise-Boosting Basics 2

SL - Gradient Boosting - CWB and GLMs

SL - Gradient Boosting - CWB and GLMs

SL - Gradient Boosting - Component-Wise-Boosting Basics 1

SL - Gradient Boosting - Component-Wise-Boosting Basics 1

Advanced Machine Learning - Online learning - Introduction

Advanced Machine Learning - Online learning - Introduction

Advanced Machine Learning- Multi-target prediction - Loss Functions

Advanced Machine Learning- Multi-target prediction - Loss Functions

SL - Advanced Risk Minimization - Bias-Variance Decomposition

SL - Advanced Risk Minimization - Bias-Variance Decomposition

Advanced Machine Learning- Multi-target prediction - Methods for Multi-target Prediction 2

Advanced Machine Learning- Multi-target prediction - Methods for Multi-target Prediction 2

Advanced Machine Learning- Multi-target prediction - Methods for Multi-target Prediction 1

Advanced Machine Learning- Multi-target prediction - Methods for Multi-target Prediction 1

Advanced Machine Learning- Multi-target prediction - Introduction

Advanced Machine Learning- Multi-target prediction - Introduction

Advanced Machine Learning- Imbalanced Learning - Sampling Methods Part 2

Advanced Machine Learning- Imbalanced Learning - Sampling Methods Part 2

Advanced Machine Learning- Imbalanced Learning - Sampling Methods Part 1

Advanced Machine Learning- Imbalanced Learning - Sampling Methods Part 1

Advanced Machine Learning- Imbalanced Learning - Performance Measures

Advanced Machine Learning- Imbalanced Learning - Performance Measures

Advanced Machine Learning- Imbalanced Learning - Cost Curves 2

Advanced Machine Learning- Imbalanced Learning - Cost Curves 2

Advanced Machine Learning- Imbalanced Learning - Cost Curves 1

Advanced Machine Learning- Imbalanced Learning - Cost Curves 1

Advanced Machine Learning- Imbalanced Learning - Introduction

Advanced Machine Learning- Imbalanced Learning - Introduction

Advanced Machine Learning- Imbalanced Learning - Cost-Sensitive Learning 3

Advanced Machine Learning- Imbalanced Learning - Cost-Sensitive Learning 3

Advanced Machine Learning- Imbalanced Learning - Cost-Sensitive Learning 2

Advanced Machine Learning- Imbalanced Learning - Cost-Sensitive Learning 2

Advanced Machine Learning- Imbalanced Learning - Cost-Sensitive Learning 1

Advanced Machine Learning- Imbalanced Learning - Cost-Sensitive Learning 1

I2ML - CART - Splitting criteria for Classification

I2ML - CART - Splitting criteria for Classification

I2ML - CART - Splitting criteria for Regression

I2ML - CART - Splitting criteria for Regression

I2ML - CART - Stopping criteria & Pruning

I2ML - CART - Stopping criteria & Pruning

I2ML - CART - Advantages and Disadvantages

I2ML - CART - Advantages and Disadvantages

I2ML - CART - Computational Aspects of Finding Splits

I2ML - CART - Computational Aspects of Finding Splits

I2ML - CART - Growing a tree

I2ML - CART - Growing a tree

I2ML - CART - Predictions with CART

I2ML - CART - Predictions with CART

Interpretable Machine Learning - Counterfactuals and Adversarial Examples - Adversarial Examples

Interpretable Machine Learning - Counterfactuals and Adversarial Examples - Adversarial Examples

Interpretable Machine Learning - Counterfactuals and Adversarial Examples - Increasing Trust

Interpretable Machine Learning - Counterfactuals and Adversarial Examples - Increasing Trust

Interpretable Machine Learning - Counterfactuals and Adversarial Examples - Methods of CEs

Interpretable Machine Learning - Counterfactuals and Adversarial Examples - Methods of CEs

Interpretable Machine Learning - Counterfactuals and Adversarial Examples - Counterf. Explanations

Interpretable Machine Learning - Counterfactuals and Adversarial Examples - Counterf. Explanations

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - Pitfalls

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - Pitfalls

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - Examples

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - Examples

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - LIME

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - LIME

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - Intro

Interpretable Machine Learning - Local Interpretable Model-agnostic Explanations (LIME) - Intro

Interpretable Machine Learning - Shapley - SHAP (SHapley Additive exPlanation) Values

Interpretable Machine Learning - Shapley - SHAP (SHapley Additive exPlanation) Values

Interpretable Machine Learning - Shapley - Shapley Values for Local Explanations

Interpretable Machine Learning - Shapley - Shapley Values for Local Explanations

Interpretable Machine Learning - Shapley - Shapley Values

Interpretable Machine Learning - Shapley - Shapley Values

Interpretable Machine Learning - Feature Importance - Permutation Feature Importance (PFI)

Interpretable Machine Learning - Feature Importance - Permutation Feature Importance (PFI)

Interpretable Machine Learning - Feature Importance - Leave One Covariate Out (LOCO)

Interpretable Machine Learning - Feature Importance - Leave One Covariate Out (LOCO)

Interpretable Machine Learning - Feature Importance - Conditional Feature Importance (CFI)

Interpretable Machine Learning - Feature Importance - Conditional Feature Importance (CFI)

Interpretable Machine Learning - Feature Importance - Introduction to Loss-based Feature Importance

Interpretable Machine Learning - Feature Importance - Introduction to Loss-based Feature Importance

Interpretable Machine Learning - Feature Effects - Partial Dependence (PD) Plot

Interpretable Machine Learning - Feature Effects - Partial Dependence (PD) Plot

Interpretable Machine Learning - Feature Effects - Individual Conditional Expectation (ICE) Plots

Interpretable Machine Learning - Feature Effects - Individual Conditional Expectation (ICE) Plots

Interpretable Machine Learning - Feature Effects - Accumulated Local Effect (ALE) Plot

Interpretable Machine Learning - Feature Effects - Accumulated Local Effect (ALE) Plot

Interpretable Machine Learning - Feature Effects - Introduction to Feature Effects

Interpretable Machine Learning - Feature Effects - Introduction to Feature Effects

Interpretable Machine Learning - Interpretable Models - GAM and Boosting

Interpretable Machine Learning - Interpretable Models - GAM and Boosting

Interpretable Machine Learning - Interpretable Models - Generalized Linear Models

Interpretable Machine Learning - Interpretable Models - Generalized Linear Models

Interpretable Machine Learning - Interpretable Models - Extensions of Linear Regression Models

Interpretable Machine Learning - Interpretable Models - Extensions of Linear Regression Models

Interpretable Machine Learning - Interpretable Models - Linear Regression Model

Interpretable Machine Learning - Interpretable Models - Linear Regression Model

Interpretable Machine Learning - Interpretable Models - Rule-based Models

Interpretable Machine Learning - Interpretable Models - Rule-based Models

Interpretable Machine Learning - Interpretable Models - Motivation

Interpretable Machine Learning - Interpretable Models - Motivation

Interpretable Machine Learning - Introduction - Interpretation Goals

Interpretable Machine Learning - Introduction - Interpretation Goals

Interpretable Machine Learning - Introduction - Dimensions of Interpretability

Interpretable Machine Learning - Introduction - Dimensions of Interpretability

I2ML - Supervised Regression - Deep Dive: Proof OLS Regression

I2ML - Supervised Regression - Deep Dive: Proof OLS Regression

I2ML - Supervised Regression - Linear Models with L2 Loss

I2ML - Supervised Regression - Linear Models with L2 Loss

I2ML - Supervised Regression - Polynomial Regression

I2ML - Supervised Regression - Polynomial Regression

I2ML - Supervised Regression - Linear Models with L1 Loss

I2ML - Supervised Regression - Linear Models with L1 Loss

Interpretable Machine Learning - Introduction - Correlation and Dependencies

Interpretable Machine Learning - Introduction - Correlation and Dependencies

Interpretable Machine Learning - Introduction - Feature Interactions

Interpretable Machine Learning - Introduction - Feature Interactions

Interpretable Machine Learning - Introduction - Introduction, Motivation and History

Interpretable Machine Learning - Introduction - Introduction, Motivation and History

Optimization in Machine Learning - Evolutionary Algorithms - CMA-ES Algorithm

Optimization in Machine Learning - Evolutionary Algorithms - CMA-ES Algorithm

Optimization in Machine Learning - Bayesian Optimization - Important Surrogate Models

Optimization in Machine Learning - Bayesian Optimization - Important Surrogate Models

Optimization in Machine Learning - Bayesian Optimization - Posterior Uncertainty and Acquisition II

Optimization in Machine Learning - Bayesian Optimization - Posterior Uncertainty and Acquisition II

Optimization in Machine Learning - Bayesian Optimization - Posterior Uncertainty and Acquisition I

Optimization in Machine Learning - Bayesian Optimization - Posterior Uncertainty and Acquisition I

Optimization in Machine Learning - Bayesian Optimization - Basic BO Loop and Surrogate Modelling

Optimization in Machine Learning - Bayesian Optimization - Basic BO Loop and Surrogate Modelling

Optimization in Machine Learning - Bayesian Optimization - Black Box Optimization

Optimization in Machine Learning - Bayesian Optimization - Black Box Optimization

Optimization in Machine Learning - Evolutionary Algorithms - CMA-ES Algorithm

Optimization in Machine Learning - Evolutionary Algorithms - CMA-ES Algorithm

Optimization in Machine Learning - Evolutionary Algorithms - CMA-ES Wrap Up

Optimization in Machine Learning - Evolutionary Algorithms - CMA-ES Wrap Up

Optimization in Machine Learning - Evolutionary Algorithms - GA / Bit Strings

Optimization in Machine Learning - Evolutionary Algorithms - GA / Bit Strings

Optimization in Machine Learning - Evolutionary Algorithms - Introduction

Optimization in Machine Learning - Evolutionary Algorithms - Introduction

Optimization in Machine Learning - Evolutionary Algorithms - ES / Numerical Encodings

Optimization in Machine Learning - Evolutionary Algorithms - ES / Numerical Encodings

Optimization in Machine Learning - Derivative Free Optimization - Multi-Start Optimization

Optimization in Machine Learning - Derivative Free Optimization - Multi-Start Optimization

Optimization in Machine Learning - Derivative Free Optimization - Simulated Annealing

Optimization in Machine Learning - Derivative Free Optimization - Simulated Annealing

Optimization in Machine Learning - Derivative Free Optimization - Nelder-Mead

Optimization in Machine Learning - Derivative Free Optimization - Nelder-Mead

Optimization in Machine Learning - Derivative Free Optimization - Coordinate Descent

Optimization in Machine Learning - Derivative Free Optimization - Coordinate Descent

Optimization in Machine Learning - Constrained Optimization - Nonlinear programs and Lagrangian

Optimization in Machine Learning - Constrained Optimization - Nonlinear programs and Lagrangian

Optimization in Machine Learning - Constrained Optimization - Duality in optimization

Optimization in Machine Learning - Constrained Optimization - Duality in optimization

Optimization in Machine Learning - Constrained Optimization - Linear Programming

Optimization in Machine Learning - Constrained Optimization - Linear Programming

Optimization in Machine Learning - Constrained Optimization - Constrained Optimization Introduction

Optimization in Machine Learning - Constrained Optimization - Constrained Optimization Introduction

Optimization in Machine Learning - Second order methods - Gauss-Newton

Optimization in Machine Learning - Second order methods - Gauss-Newton

Optimization in Machine Learning - Second order methods - Newton-Raphson

Optimization in Machine Learning - Second order methods - Newton-Raphson

Optimization in Machine Learning - First order methods - Deep Dive: Gradient descent and optimality

Optimization in Machine Learning - First order methods - Deep Dive: Gradient descent and optimality

Optimization in Machine Learning - First order methods - SGD Further Details

Optimization in Machine Learning - First order methods - SGD Further Details

Optimization in Machine Learning - First order methods - SGD

Optimization in Machine Learning - First order methods - SGD

Optimization in Machine Learning - First order methods - ADAM and friends

Optimization in Machine Learning - First order methods - ADAM and friends

Optimization in Machine Learning - First order methods - GD on quadratic forms

Optimization in Machine Learning - First order methods - GD on quadratic forms

Optimization in Machine Learning - First order methods - GD with Momentum

Optimization in Machine Learning - First order methods - GD with Momentum

Optimization in Machine Learning - First order methods - GD - Mulitmodality and Saddle points

Optimization in Machine Learning - First order methods - GD - Mulitmodality and Saddle points

Optimization in Machine Learning - First order methods - Weaknesses of GD – Curvature

Optimization in Machine Learning - First order methods - Weaknesses of GD – Curvature

Optimization in Machine Learning - First order methods - Step size and optimality

Optimization in Machine Learning - First order methods - Step size and optimality

Optimization in Machine Learning - First order methods - Gradient descent

Optimization in Machine Learning - First order methods - Gradient descent

Optimization in Machine Learning - Univariate Optimization - Brent's method

Optimization in Machine Learning - Univariate Optimization - Brent's method

Optimization in Machine Learning - Univariate Optimization - Golden ratio

Optimization in Machine Learning - Univariate Optimization - Golden ratio

Optimization in Machine Learning - Optimization Problems - Other optimization problems

Optimization in Machine Learning - Optimization Problems - Other optimization problems

Optimization in Machine Learning - Optimization Problems - Constrained Problems

Optimization in Machine Learning - Optimization Problems - Constrained Problems

Optimization in Machine Learning - Optimization Problems - Unconstrained Problems

Optimization in Machine Learning - Optimization Problems - Unconstrained Problems

Optimization in Machine Learning - Mathematical Concepts - Conditions for optimality

Optimization in Machine Learning - Mathematical Concepts - Conditions for optimality

Optimization in Machine Learning - Mathematical Concepts - Matrix calculus

Optimization in Machine Learning - Mathematical Concepts - Matrix calculus

Optimization in Machine Learning - Mathematical Concepts - Quadratic forms II

Optimization in Machine Learning - Mathematical Concepts - Quadratic forms II

Optimization in Machine Learning - Mathematical Concepts - Quadratic forms I

Optimization in Machine Learning - Mathematical Concepts - Quadratic forms I

Optimization in Machine Learning - Mathematical Concepts - Taylor Approximations

Optimization in Machine Learning - Mathematical Concepts - Taylor Approximations

Optimization in Machine Learning - Mathematical Concepts - Differentiation and Derivatives

Optimization in Machine Learning - Mathematical Concepts - Differentiation and Derivatives

Optimization in Machine Learning - Mathematical Concepts - Convexity

Optimization in Machine Learning - Mathematical Concepts - Convexity

I2ML - Tuning - Intro

I2ML - Tuning - Intro

I2ML - Tuning - Advanced tuning techniques

I2ML - Tuning - Advanced tuning techniques

I2ML - Tuning - Pipelines and AutoML

I2ML - Tuning - Pipelines and AutoML

I2ML - Tuning - Problem definition

I2ML - Tuning - Problem definition

I2ML - Tuning - Basic Techniques

I2ML - Tuning - Basic Techniques

I2ML - Tuning - Intro

I2ML - Tuning - Intro

I2ML - Boosting - XGBoost

I2ML - Boosting - XGBoost

I2ML - Boosting - Gradient Boosting with Trees II

I2ML - Boosting - Gradient Boosting with Trees II

I2ML - Boosting - Gradient Boosting with Trees I

I2ML - Boosting - Gradient Boosting with Trees I

I2ML - Boosting - Illustration

I2ML - Boosting - Illustration

I2ML - Boosting - Classification

I2ML - Boosting - Classification

I2ML - Boosting - Regularization

I2ML - Boosting - Regularization

I2ML - Evaluation - Precision-Recall Curves

I2ML - Evaluation - Precision-Recall Curves

I2ML - Evaluation - AUC Extensions

I2ML - Evaluation - AUC Extensions

I2ML - Evaluation - Resampling II

I2ML - Evaluation - Resampling II

I2ML - Evaluation - ROC Visualization

I2ML - Evaluation - ROC Visualization

I2ML - Evaluation - Resampling I

I2ML - Evaluation - Resampling I

I2ML - Evaluation - Overfitting and Underfitting

I2ML - Evaluation - Overfitting and Underfitting

I2ML - Evaluation - ROC Measures

I2ML - Evaluation - ROC Measures

I2ML - Evaluation - Measures Classification

I2ML - Evaluation - Measures Classification

I2ML - Evaluation - Test Error

I2ML - Evaluation - Test Error

I2ML - Evaluation - Train Error

I2ML - Evaluation - Train Error

I2ML - Evaluation - Generalization Error

I2ML - Evaluation - Generalization Error

I2ML - Evaluation - Measures Regression

I2ML - Evaluation - Measures Regression

I2ML - k-Nearest Neighbors

I2ML - k-Nearest Neighbors

I2ML - ML Basics - Data

I2ML - ML Basics - Data

I2ML - Boosting - Boosting concept

I2ML - Boosting - Boosting concept

I2ML - Boosting - Adaboost

I2ML - Boosting - Adaboost

I2ML - Regularization - Nonlinear & Bayesian Regularization

I2ML - Regularization - Nonlinear & Bayesian Regularization

I2ML - Advanced Risk Minimization - Maximum Likelihood Estimation II

I2ML - Advanced Risk Minimization - Maximum Likelihood Estimation II

I2ML - Advanced Risk Minimization - Maximum Likelihood Estimation I

I2ML - Advanced Risk Minimization - Maximum Likelihood Estimation I

I2ML - Advanced Risk Minimization - Classification Losses: Brier Score

I2ML - Advanced Risk Minimization - Classification Losses: Brier Score

I2ML - Advanced Risk Minimization - Advanced Classification Losses

I2ML - Advanced Risk Minimization - Advanced Classification Losses

I2ML - Advanced Risk Minimization - Pseudo-Residuals

I2ML - Advanced Risk Minimization - Pseudo-Residuals

I2ML - Advanced Risk Minimization - Advanced Regression Losses

I2ML - Advanced Risk Minimization - Advanced Regression Losses

I2ML - Advanced Risk Minimization - Classification Losses: 0-1 Loss

I2ML - Advanced Risk Minimization - Classification Losses: 0-1 Loss

I2ML - Advanced Risk Minimization - Classification Losses: Bernoulli Loss

I2ML - Advanced Risk Minimization - Classification Losses: Bernoulli Loss

I2ML - Advanced Risk Minimization - Risk Minimizers

I2ML - Advanced Risk Minimization - Risk Minimizers

I2ML - Advanced Risk Minimization - Regression Losses: L1 Loss

I2ML - Advanced Risk Minimization - Regression Losses: L1 Loss

I2ML - Advanced Risk Minimization - Regression Losses: L2 Loss

I2ML - Advanced Risk Minimization - Regression Losses: L2 Loss

I2ML - ML Basics - What is Machine Learning?

I2ML - ML Basics - What is Machine Learning?

I2ML - ML Basics - Supervised Tasks

I2ML - ML Basics - Supervised Tasks

I2ML - ML Basics - Components of Supervised Learning

I2ML - ML Basics - Components of Supervised Learning