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Machine Learning Training

Machine Learning training

Introduction to Machine learning

  • What is AI
  • What is machine learning
  • History of machine learning
  • Uses of machine learning

Types of machine learning

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Transfer learning
Tools for machine learning
  • Programming languages
  • Data repositories
  • Hierarchical databases
  • Software used
Basics of R programming
  • Installing R Studio
  • Matrix operations
  • Data loading/unloading
  • Plotting and visualizing
  • Algorithms - Predicting and modeling

Statistical methods

  • Graph theory
  • Probability
  • Bayes theorem
  • Regression models

Data modeling - Linear regression

  • Model representtion
  • Cost function
  • Gradient descent for linear regression

Data modeling - Logistic regression

  • Hypothesis representation
  • Decision boundary

Decision trees

  • Basics of decision trees
  • Uses for decision trees
  • Advantages and limitations
  • How decision trees work

Decision trees example

  • Create a decision tree
  • Requirement
  • Training the data

Classifiers

  • Support vector machines

Support vector machines

  • Linear and non linear classification
  • What are SVM
  • Where are SVM used

Association rules learning

  • What is ARL
  • Where are ASL rules used
  • Support, Confidence, lift and conviction
Clustering
  • What is clustering
  • Where is clustering used
  • Clustering mode

Clustering K means Model example

  • Preparing the data
  • Workbench method
  • Command-line method
  • Coded method
Basics of neural networks
  • Introduction to Neural Networks
  • Why Study Neural Networks
  • Real life examples of neural network

Types of neural networks

  • Perception
  • Recurrent neural networks
  • convolution neural network

Additional topics

  • Evaluaating Model Performance
  • Improving Model Performance
  • Similarity between R and Python
  • Specialized Machine Learning Topics
Course Id:
ML001 
Course Fees:
301 USD