Math for Machine Learning 2020-04-15T15:56:11+00:00

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Math for Machine Learning

This course is designed to build an intuitive understanding of maths required / involved / related in Data Science and Machine Learning

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Video-Course Description

Key Features

  • Gain experience with handson exercises

  • Instructor led training

  • Flexibility of learning in class or online

  • Learn by doing – Assignments, Tasks

  • Curriculum – par with Industry

  • Innovative Approach – Discussions, Quiz, webinar

  • Solutions for your skill gaps

  • Upskilling and Level Setting

  • Dedicating mentoring sessions from industry experts

  • Career Support

  • Industry valued Certificate

Schedule

BatchModeTimeFee
Weekends/ WeekdaysOnline Training
Instructor Led
FlexibleINR 12000/-
Enroll Now
Weekends/ WeekdaysClassroom TrainingFlexibleINR 10000/-
Call/ Whatsapp

9841557655

Prerequisite : None

Curriculum

  • Systems of Linear Equations
  • Matrices
  • Solving Systems of Linear Equations
  • Vector Spaces
  • Linear Independence
  • Basis and Rank
  • Linear Mappings
  • Affine Spaces
  • Norms
  • Inner Products
  • Lengths and Distances
  • Angles and Orthogonality
  • Orthonormal Basis
  • Orthogonal Complement
  • Inner Product of Functions
  • Orthogonal Projections
  • Rotations
  • Determinant and Trace
  • Eigenvalues and Eigenvectors
  • Cholesky Decomposition
  • Eigen decomposition and Diagonalization
  • Singular Value Decomposition
  • Matrix Approximation
  • Matrix Phylogeny
  • Differentiation of Univariate Functions
  • Partial Differentiation and Gradients
  • Gradients of Vector-Valued Functions
  • Gradients of Matrices
  • Useful Identities for Computing Gradients
  • Backpropagation and Automatic Differentiation
  • Higher-Order Derivatives
  • Linearization and Multivariate Taylor Series
  • Cases, variables and levels of measurement
  • Data matrix and frequency table
  • Graphs and shapes of distributions
  • Measures of central tendency Mode, median and mean
  • Variance and standard deviation
  • Range, interquartile range and box plot
  • Z-scores
  • Dispersion
  • Null hypothesis testing
  • P-values
  • Confidence intervals and two-sided tests
  • Power
  • Two independent proportions, means
  • Two dependent proportions, means
  • Controlling for other variables
  • Categorical association and independence.
  • The Chi-squared test6m
  • Interpreting the Chi-squared test
  • Chi-squared as goodness-of-fit
  • The Chi-squared test – sidenotes
  • Fisher’s exact test
  • Construction of a Probability Space
  • Discrete and Continuous Probabilities
  • Sum Rule, Product Rule, and Bayes’ Theorem
  • Summary Statistics and Independence
  • Gaussian distribution
  • Conjugacy and the Exponential Family
  • Change of Variables/Inverse Transform
  • Optimization Using Gradient Descent
  • Constrained Optimization and Lagrange Multipliers
  • Convex Optimization

Tools Covered

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