Machine Learning

CURRICULUM

  • What is machine learning?
  • Learning system model
  • Training and testing
  • Performance
  • Algorithms
  • Machine learning structure
  • What are we seeking?
  • Learning techniques
  • Applications
  • Instance Based Classifiers
  • Nearest-Neighbor Classifiers
  • Lazy vs. Eager Learning
  • k-NN variations
  • How to determine the good value for k
  • When to Consider Nearest Neighbors
  • Condensing
  • Nearest Neighbour Issues
  • Naïve Bayes Learning
  • Conditional Probability
  • Bayesian Theorem: Basics
  • The Bayes Classifier
  • Model Parameters
  • Naïve Bayes Training
  • Types of errors
  • Sensitivity and Specificity
  • ROC Curve
  • Key Requirements
  • Decision Tree as a Rule Set
  • How to Create a Decision Tree
  • Choosing Attributes
  • ID3 Heuristic
  • Entropy
  • Pruning Trees – Pre and Post
  • Subtree Replacement, Raising
  • Cross-validation
  • Ensemble Approaches
  • Bagging Model
  • Boosting
  • Gradient Boosting
  • Random Forests
  • Advantages, Disadvantages
  • Background of Brain and Neuron
  • Neural Networks
  • Neurons Diagram
  • Neuron Models- step function
  • Perceptrons
  • Network Architectures
  • single-layer feed-forward
  • Support Vector Machines for Classification
  • Linear Discrimination
  • Nonlinear Discrimination
  • SVM Mathematically
  • Extensions
  • Application in Drug Design
  • Data Classification
  • Kernel Functions
  • XgBoost for Classification
  • What is XGBoost? Why is it so good?
  • How does XGBoost work?
  • Understanding XGBoost Tuning Parameters
  • Adaboost usuage with examples

To buy this course mail us at info@myanalyticsmentor.com

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