• Home
  • Courses
  • News
  • E-Magazine
  • Contact
    • Login
      • GET STARTED
    PrepTube.inPrepTube.in
    • Home
    • Courses
    • News
    • E-Magazine
    • Contact
    • Login
      • GET STARTED

      Machine Learning

      • Home
      • All courses
      • 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
        This content is protected, please login and enroll course to view this content!
        Prev Gradient Boosting | Ensemble Methods
        Next Naive Bayes | Graphical Models

        Leave A Reply Cancel reply

        Your email address will not be published. Required fields are marked *

        Become an Instructor

        Join our community of students around the world and sell your courses.

        LEARN MORE

        Contact

        •   [email protected]
        •   +91-9871457944
        •   +91-8130332886
        •   1150 Mahavir Apartment,
          Sector 29, Noida-201301

        Company

        • About Us
        • Contact
        • Become a Teacher

        Useful Links

        • Courses
        • Gallery

        Our Network

        • News
        • Engineering
        • Management
        • Government Jobs

        Mobile

        Click and Get started in seconds

        Copyright 2019 by PrepTube.in

        • Comment Policy
        • Disclaimer
        • Copyright
        • Terms of Use
        • Privacy Policy

        Connect with:

        Login with Google Login with Twitter

        logo

        Login with your site account

        Connect with:

        Login with Google Login with Twitter
        logo


        Lost your password?