Statistics & Probability for Data Science & Machine Learning

LeeAndro · May 24, 2022

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

This course is designed to get an in-depth knowledge of Statistics and Probability for Data Science and Machine Learning point of view.

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

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

Ma 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

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

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

Ma 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.

Anyone looking for a career in Data Science and Machine Learning

Anyone looking to learn Statistics from basics to Advanced

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Statistics & Probability for Data Science & Machine Learning

LeeAndro

Trusted Editor