Statistics & Probability for Data Science & Machine Learning

Know each & every concept - Descriptive, Inferential Statistics & Probability become expert in Stats for Data Science

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Statistics & Probability for Data Science & Machine Learning

What You Will Learn!

Looking for in-depth knowledge of Statistics for Data Science
Each and every concepts like Measure of Central Tendency, Measure of Spread with various example
Get the in-depth knowledge of Regression, Covariance Matrix, Karl Pearson Correlation Coefficient and Spearman Rank Correlation Coefficient
Detailed understanding of Normal Distribution
Understanding of Skewness, Kurtosis, Symmetric distribution and KDE
Detailed knowledge on Basics of Probability, Conditional Probability
Permutations and Combinations
Combinatorics and Probability
Understanding of Random Variables its variance and mean
Various distributions like Binomial, Bernoulli, Geometric and Poisson
Sampling Distribution and Central Limit Theorem
Confidence Interval
Margin of error
T-statistic and Z statistic in detail
Significance testing
Type 1 and Type 2 Errors
Comparing two proportions
Comparing two means
Introduction to Chi Squared Distribution
Chi Square test for Homogeneity and association
Advanced Regression
Anova and FStatistic

Description

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.