Statistics & Probability for Data Science & Machine Learning

This course is designed to get an in–depth knowledge of Statistics and Probability for Data Science and Machine Learning point of view. Here we are talking about each and every concept of Descriptive and Inferential statistics and Probability.

We are covering the following topics in detail with many examples so that the concepts will be crystal clear and you can apply them in the day to day work.

Extensive coverage of statistics in detail:

The measure of Central Tendency (Mean Median and Mode) 

The Measure of Spread (Range, IQR, Variance, Standard Deviation and Mean Absolute deviation) 

Regression and Advanced regression in details with Hypothesis understanding (P–value) 

Covariance Matrix, Karl Pearson Correlation Coefficient, and Spearman Rank Correlation Coefficient with examples

Detailed understanding of Normal Distribution and its properties

Symmetric Distribution, Skewness, Kurtosis, and KDE.

Probability and its in–depth knowledge

Permutations and Combinations

Combinatorics and Probability

Understanding of Random Variables

Various distributions like Binomial, Bernoulli, Geometric, and Poisson

Sampling distributions and Central Limit Theorem

Confidence Interval

Margin of Error

T–statistic and F–statistic

Significance tests in detail with various examples

Type 1 and Type 2 Errors

Chi–Square Test

ANOVA and F–statistic

By completing this course we are sure you will be very much proficient in Statistics and able to talk to anyone about stats with confidence apply the knowledge in your day to day work.