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    Machine Learning

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    • Machine Learning & AI
    • Machine Learning
    CoursesMachine Learning & AIMachine Learning
    • Machine Learning 82

      • Lecture1.1
        What is Machine Learning? Quick Introduction
      • Lecture1.2
        What is Supervised Learning ?
      • Lecture1.3
        What is Unsupervised Learning ?
      • Lecture1.4
        What is Reinforcement Learning ?
      • Lecture1.5
        Probability Theory: Bayes’ Rule with Example
      • Lecture1.6
        Probability Theory: Random variables, Expectations
      • Lecture1.7
        Linear Algebra: Vector Spaces, Subspaces, Orthogonal Matrices, Quadratic Form
      • Lecture1.8
        Linear Algebra: Eigenvalues, Eigenvectors, Diagonlization, SVD & Matrix Calculus
      • Lecture1.9
        Statistical Decision Theory: Regression
      • Lecture1.10
        Statistical Decision Theory: Classification
      • Lecture1.11
        Bias-Variance
      • Lecture1.12
        Linear Regression
      • Lecture1.13
        Multivariate Regression
      • Lecture1.14
        Dimensionality Reduction: Subset Selection (1/2)
      • Lecture1.15
        Dimensionality Reduction: Subset Selection (2/2)
      • Lecture1.16
        Dimensionality Reduction: Shrinkage Methods
      • Lecture1.17
        Principal Components Regression
      • Lecture1.18
        Partial Least Squares (PLS) Regression | Dimension Reduction
      • Lecture1.19
        Linear Classification
      • Lecture1.20
        Logistic Regression | Classification
      • Lecture1.21
        Linear Discriminant Analysis: Classification (1/3)
      • Lecture1.22
        Linear Discriminant Analysis: Classification (2/3)
      • Lecture1.23
        Linear Discriminant Analysis: Classification (3/3)
      • Lecture1.24
        Weka Tutorial For Beginners
      • Lecture1.25
        Optimization: Problems & Algorithms
      • Lecture1.26
        Perceptron Learning | Classification
      • Lecture1.27
        Support Vector Machine (SVM) Formulation
      • Lecture1.28
        Interpretation & Analysis | Support Vector Machine
      • Lecture1.29
        Support Vector Machine for Linearly Non Separable Data
      • Lecture1.30
        Support Vector Machine Kernels
      • Lecture1.31
        Hinge Loss Formulation in Support Vector Machine
      • Lecture1.32
        Artificial Neural Networks: Early Models
      • Lecture1.33
        Artificial Neural Networks: Backpropogation 01
      • Lecture1.34
        Artificial Neural Networks: Backpropogation 02
      • Lecture1.35
        ANNs: Initialization, Training & Validation
      • Lecture1.36
        Maximum Likelihood Estimate | Parameter Estimation
      • Lecture1.37
        Priors & MAP Estimate | Parameter Estimation
      • Lecture1.38
        Bayesian Parameter Estimation
      • Lecture1.39
        Decision Trees: Introduction
      • Lecture1.40
        Regression Trees | Decision Trees
      • Lecture1.41
        Stopping Criteria & Pruning | Decision Trees
      • Lecture1.42
        Loss Functions for Classification | Decision Trees
      • Lecture1.43
        Categorical Attributes | Decision Trees
      • Lecture1.44
        Multiway Splits | Decision Trees
      • Lecture1.45
        Missing Values, Imputation & Surrogate Splits
      • Lecture1.46
        Instability, Smoothness & Repeated Subtrees
      • Lecture1.47
        Decision Trees Tutorial
      • Lecture1.48
        Evaluation Measures
      • Lecture1.49
        Bootstrapping & Cross Validation
      • Lecture1.50
        2 Class Evaluation Measures
      • Lecture1.51
        ROC Curve
      • Lecture1.52
        Minimum Description Length & Exploratory Analysis
      • Lecture1.53
        Hypothesis Testing: An Introduction
      • Lecture1.54
        Hypothesis Testing: Basic Concepts
      • Lecture1.55
        Sampling Distributions and the Z Test
      • Lecture1.56
        Student’s t-test | Hypothesis Testing
      • Lecture1.57
        Two Sample & Paired Sample t-tests | Hypothesis Testing
      • Lecture1.58
        Confidence Intervals | Hypothesis Testing
      • Lecture1.59
        Bagging, Committee Machines & Stacking | Ensemble Methods
      • Lecture1.60
        Boosting | Ensemble Methods
      • Lecture1.61
        Gradient Boosting | Ensemble Methods
      • Lecture1.62
        Random Forest | Ensemble Methods
      • Lecture1.63
        Naive Bayes | Graphical Models
      • Lecture1.64
        Bayesian Networks | Graphical Models
      • Lecture1.65
        Undirected Graphical Models: Introduction
      • Lecture1.66
        Undirected Graphical Models: Potential Functions
      • Lecture1.67
        Hidden Markov Models: Graphical Models
      • Lecture1.68
        Variable Elimination | Graphical Models
      • Lecture1.69
        Belief Propagation | Graphical Models
      • Lecture1.70
        Partitional Clustering
      • Lecture1.71
        Hierarchical Clustering
      • Lecture1.72
        Threshold Graphs | Clustering
      • Lecture1.73
        BIRCH Algorithm | Clustering
      • Lecture1.74
        CURE Algorithm | Clustering
      • Lecture1.75
        Density Based Clustering
      • Lecture1.76
        Gaussian Mixture Model
      • Lecture1.77
        Expectation Maximization Algorithm with Example
      • Lecture1.78
        Expectation Maximization Algorithm (Part 02)
      • Lecture1.79
        Spectral Clustering Algorithm Explained
      • Lecture1.80
        Computational Learning Theory
      • Lecture1.81
        Frequent Itemset Mining
      • Lecture1.82
        Apriori Property | Frequent Itemset Mining
      • Lecture1.83
        Reinforcement Learning: Introduction
      • Lecture1.84
        RL Framework, Temporal Difference (TD) Learning
      • Lecture1.85
        Solution Methods & Applications | Reinforcement Learning
      • Lecture1.86
        Multiclass Classification

      What is Supervised Learning ?

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      Next What is Unsupervised Learning ?

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