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What is Machine Learning?
What is Machine Learning?
Hypothesis and Inductive Bias
Hypothesis and Inductive Bias
Linear Regression
Linear Regression
Least Mean Square (LMS)
Least Mean Square (LMS)
Gradient Descent
Gradient Descent
Lasso and Ridge Regression
Lasso and Ridge Regression
Polynomial Regression
Polynomial Regression
Logistic Regression
Logistic Regression
Maximum Likelihood Estimation
Maximum Likelihood Estimation
Decision Tree
Decision Tree
CART in Machine Learning
CART in Machine Learning
Overfitting and Underfitting
Overfitting and Underfitting
Ensemble Methods: Bagging, Boosting, Random Forest & XGBoost
Ensemble Methods: Bagging, Boosting, Random Forest & XGBoost
Bayesian Learning: A Dive into Probabilistic Modeling
Bayesian Learning: A Dive into Probabilistic Modeling
Support Vector Machine (SVM)
Support Vector Machine (SVM)
K-Means Clustering: Centroid, Inertia, Convergence & More
K-Means Clustering: Centroid, Inertia, Convergence & More
Adaptive Hierarchical Clustering & Gaussian Mixture Models
Adaptive Hierarchical Clustering & Gaussian Mixture Models
Biological Neural Network in Artificial Neural Network
Biological Neural Network in Artificial Neural Network
Terminologies in ANN: Activation Function, Weights, Bias & Learning Rate
Terminologies in ANN: Activation Function, Weights, Bias & Learning Rate
McCulloch-Pitts Neuron & Hebb Network
McCulloch-Pitts Neuron & Hebb Network
Power of Perceptron: Training Rule, Gradient Descent & Delta Rule
Power of Perceptron: Training Rule, Gradient Descent & Delta Rule
Multilayer Network: Threshold Unit & Feedforward Networks
Multilayer Network: Threshold Unit & Feedforward Networks
Backpropagation Algorithm: Convergence, Local Minima & Space Complexity
Backpropagation Algorithm: Convergence, Local Minima & Space Complexity
Regularization: Parameter Norm Penalties, Dataset Noise & More
Regularization: Parameter Norm Penalties, Dataset Noise & More
Training Deep Models: Neural Network Optimization & Basic Algorithms
Training Deep Models: Neural Network Optimization & Basic Algorithms
Convolution Network: Sparse Interactions & Parameter Sharing
Convolution Network: Sparse Interactions & Parameter Sharing
Recurrent Neural Network: Bidirectional RNN & Deep Networks
Recurrent Neural Network: Bidirectional RNN & Deep Networks
Unsupervised Learning: Kohonen Self-Organizing Feature Maps
Unsupervised Learning: Kohonen Self-Organizing Feature Maps
Linear Factor Methods: Probabilistic PCA Analysis & Sparse Coding
Linear Factor Methods: Probabilistic PCA Analysis & Sparse Coding
Undercomplete Autoencoders: Regularized & Stochastic Encoders
Undercomplete Autoencoders: Regularized & Stochastic Encoders
Generative Adversarial Networks (GAN) vs Discriminative Models
Generative Adversarial Networks (GAN) vs Discriminative Models
Explaining & Interpreting Black Box to White Box Models: SHAP & Shapley Values
Explaining & Interpreting Black Box to White Box Models: SHAP & Shapley Values
Reinforcement Learning Elements vs Supervised & Policy-based Methods
Reinforcement Learning Elements vs Supervised & Policy-based Methods
Bandit Problems: Value & Action-based Methods, Greedy Problem Solving
Bandit Problems: Value & Action-based Methods, Greedy Problem Solving
Linear Reward Penalty Algorithm & Parameterized Policy Gradient
Linear Reward Penalty Algorithm & Parameterized Policy Gradient
Immediate & Full Reinforcement Learning: Agents, Goals & Rewards
Immediate & Full Reinforcement Learning: Agents, Goals & Rewards
Markov Decision Process: Property, Finite Value & Bellman's Equations
Markov Decision Process: Property, Finite Value & Bellman's Equations
Policy Evaluation, Improvement & Iteration: Value & Dynamic Programming
Policy Evaluation, Improvement & Iteration: Value & Dynamic Programming
Monte Carlo Method over Dynamic Programming: Control & More
Monte Carlo Method over Dynamic Programming: Control & More
Temporal Difference Learning Methods over Monte Carlo
Temporal Difference Learning Methods over Monte Carlo
Function Approximation: Tabular Implementation & Gradient Methods
Function Approximation: Tabular Implementation & Gradient Methods
Deep Learning in Reinforcement: Training Workflow & More
Deep Learning in Reinforcement: Training Workflow & More