Mathematics & Statistics of Machine Learning & Data Science

Mathematics & Statistics of Machine Learning & Data Science

What you’ll learn

  • Mathematics and Statistics behind Machine Learning
  • Mathematics and Statistics behind Neural Networks
  • Mathematics and Statistics behind Deep Learning
  • Probably Approximately Correct (PAC) Learning
  • Vapnik-Chervonenkis (VC) Dimension
  • Bayesian Decision Theory
  • Parametric Methods
  • Bernoulli Density
  • Tuning Model Complexity
  • Gaussian (Normal) Density
  • Multivariate Methods
  • Multivariate Normal Distribution
  • Tuning Complexity
  • Dimensionality Reduction
  • Linear Discriminant Analysis
  • Clustering
  • Expectation-Maximization Algorithm
  • Supervised Learning after Clustering
  • k-Means Clustering
  • Nonparametric Density Estimation
  • Kernel Estimator
  • k-Nearest Neighbor Estimator
  • Condensed Nearest Neighbor
  • Pruning
  • Multivariate Trees
  • Learning Vector Quantization
  • v-SVM
  • Multiclass Kernel Machines
  • Model Selection in HMM

Who this course is for:

  • People who want to start their career in Machine Learning
  • People who want to learn Machine Learning
  • People who want to learn Deep Learning

How to Enroll Mathematics & Statistics of Machine Learning & Data Science course?

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