Statistical and Mathematical Foundations of Data Science and Machine Learning

COURSE | Started On : Monday, 22 March 2021 10:46 | 1
Instructor: Evelyn Benedict Evelyn Benedict

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


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 


 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 

2 week ₹ 2999
Course Information
Evelyn Benedict


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

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Enrolled Users
Linear Algebra - Vector
Linear Algebra - Matrix
Bayes theorem - probability
Bayes theorem for prediction and how much can we rely on ML predictions - context matters
Independence and Chain rule - probability
Expected value-Variance-Covariance-Standard Deviation and normal distribution
Statistical analysis with Python
Statistical foundations of data science and Machine Learning