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Statistics & Probability for Data Science & Machine Learning
Know each & every concept - Descriptive, Inferential Statistics & Probability become expert in Stats for Data Science
This Course Includes
udemy
4.1 (99 reviews )
29h 14m 
english
Online - Self Paced
professional certificate
Udemy
About 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.
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 Show moreShow less.