Statistics & Probability for Data Science & Machine Learning

[Srbija]

Statistics & Probability for Data Science & Machine Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 43 lectures (29h 14m) | Size: 9.71 GB

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

What you'll 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

Requirements
Passion to Learn Statistics , Rest we will take care of it


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.

Who this course is for
Anyone looking for a career in Data Science and Machine Learning
Anyone looking to learn Statistics from basics to Advanced

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Edited August 17, 2021 by Bad Karma
Dead links removed