Statistical and Mathematical Foundations of Data Science and Machine Learning

COURSE | Started On : Monday, 22 March 2021 10:46 | 2

Getting to know the Mathematics and Statistics behind the ML algorithms. Appreciation of these conce...

COURSE OVERVIEW

Getting to know the Mathematics and Statistics behind the ML algorithms. Appreciation of these concepts helps in 1. Understanding how the algorithms learn from data 2. Explaining how the results have been obtained 3. Selecting appropriate evaluation parameters Discussion of these topics are done in the context of solving business problems with Machine Learning algorithms and theoretical depth is determined by these considerations. While the Foundation module is adequate for practitioners, researchers in Machine Learning need to take higher level courses as well.

 

 Linear Algebra

 How AIML problems are   formulated using the   concepts of Scalar, Vector,   Matrices and Tensors 

 

 Understanding how Matrix   multiplication, identity,   inverse and other related   concepts such as norm,   span, dependence and   determinant accelerate   AIML algorithms 

 Probability 

 Probabilistic nature of   machine learning -   understanding how theory   of probability forms the   basis of predictive analysis 

 

 Applying probability   distributions, marginal and   conditional probability,   Bayes' rule to solving   simple problems and how it   is scaled up to machine   learning algorithms,   Hypothesis testing 

 Calculus and     Optimisation

 Finding minima and   maxima with calculus - how   simple principles of calculus   when combined with vast   computing power can   provide solution to complex   problems 

 

 Stochastic Gradient Descent   Optimisation, Constrained   Optimisation and Linear   Least Square 

 Statistics for     Machine Learning 

 Measure of Central tendency - Mean, Median, Mode 

 

 Dispersion Measures,   Range,  Variance,     Covariance, Standard   Deviation, Z-score,   Kurtosis,  Skewness 

 

 Correlation and covariance,   VIF 

 

 Normal distribution and   other types of distribution,   z- score and cumulative   distribution function 

 

 RMSE, MAE, R-squared 


AVAILABLE SUBSCRIPTION PLANS
2 week  ₹ 2999
Course Information
Creator
Evelyn Benedict

Type
Paid

Published Date
2021-03-22 10:46:25

Certificate Term
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INSTRUCTORS
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Here are some of the group discussions for this course
Linear Algebra - Vector
Linear Algebra - Matrix
Probability
Bayes theorem - probability
Bayes theorem for prediction and how much can we rely on ML predictions - context matters
Independence and Chain rule - probability
Calculus
Statistics
Expected value-Variance-Covariance-Standard Deviation and normal distribution