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Statistics & Probability for Data Science & Machine Learning

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Statistics & Probability for Data Science & Machine Learning, available at $49.99, has an average rating of 4.3, with 43 lectures, based on 94 reviews, and has 508 subscribers.

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You will learn about 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 This course is ideal for individuals who are Anyone looking for a career in Data Science and Machine Learning or Anyone looking to learn Statistics from basics to Advanced It is particularly useful for Anyone looking for a career in Data Science and Machine Learning or Anyone looking to learn Statistics from basics to Advanced.

Enroll now: Statistics & Probability for Data Science & Machine Learning

Summary

Title: Statistics & Probability for Data Science & Machine Learning

Price: $49.99

Average Rating: 4.3

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: $22.99

Quality Status: approved

Status: Live

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

Who Should Attend

  • Anyone looking for a career in Data Science and Machine Learning
  • Anyone looking to learn Statistics from basics to Advanced

Target Audiences

  • Anyone looking for a career in Data Science and Machine Learning
  • Anyone looking to learn Statistics from basics to Advanced

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.

Course Curriculum

Chapter 1: Statistics – An Introduction

Lecture 1: Statistics – An Introduction

Chapter 2: Measure of Central Tendency

Lecture 1: Measure of Central Tendency (Mean, Median and Mode)

Chapter 3: Measure of Spread

Lecture 1: Measure of Spread – Part 1 (Range, IQR, Variance, Standard Deviation and MAD)

Lecture 2: Measure of Spread – Part 2 (Range, IQR, Variance, Standard Deviation and MAD)

Chapter 4: Regression – In-Depth

Lecture 1: Regression – An Introduction

Lecture 2: Covariance and Covariance Matrix

Lecture 3: Karl Pearson Correlation Coefficient

Lecture 4: Spearman Rank Correlation Coefficient

Lecture 5: Residuals

Lecture 6: R-Square and RMSE

Chapter 5: Normal Distribution

Lecture 1: Normal Distribution – Part 1

Lecture 2: Normal Distribution – Part 2

Lecture 3: Normal Distribution – Part 3

Chapter 6: Statistics – Symmetric Distribution, Skewness, Kurtosis and KDE

Lecture 1: Statistics – Symmetric Distribution, Skewness, Kurtosis and KDE

Chapter 7: Probability

Lecture 1: Probability – An Introduction

Lecture 2: Probability – Part 2

Lecture 3: Probability – Part 3.1

Lecture 4: Probability – Part 3.2

Chapter 8: Permutation and Combination

Lecture 1: Permutation and Combination

Chapter 9: Combinatorics and Probability

Lecture 1: Combinatorics and Probability

Chapter 10: Random Variables

Lecture 1: Introduction to Random Variables

Lecture 2: Random Variables – Variance

Chapter 11: Distributions

Lecture 1: Binomial and Bernoulli Distribution – Part 1

Lecture 2: Binomial and Bernoulli Distribution – Part 2

Lecture 3: Geometric Random Variables – Part 1

Lecture 4: Geometric Random Variables – Part 2

Lecture 5: Poisson Distribution

Chapter 12: Sample Distribution and Central Limit Theorem

Lecture 1: Sample Distribution

Lecture 2: Central Limit Theorem

Chapter 13: Confidence Interval

Lecture 1: Confidence Interval

Lecture 2: Margin of error

Chapter 14: TStatistic

Lecture 1: TStatistic

Chapter 15: Significance Tests

Lecture 1: Significance Tests – An Introduction

Lecture 2: Type 1 and Type 2 Errors

Lecture 3: Constructing Hypothesis for a significance test about a proportion

Lecture 4: Constructing Hypothesis for a significance test about a mean

Lecture 5: More Examples on Significance Testing

Lecture 6: Comparing two proportions

Lecture 7: Comparing two means

Chapter 16: Chi-Squared Distribution

Lecture 1: Introduction to Chi Squared Distribution

Lecture 2: Chi-Squared Test for Homogeneity and Association

Chapter 17: Advanced Regression – Inference and Transforming

Lecture 1: Advanced Regression – Inference and Transforming

Chapter 18: ANOVA and F-Statistic

Lecture 1: ANOVA and F-Statistic

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 5 votes
  • 3 stars: 12 votes
  • 4 stars: 26 votes
  • 5 stars: 50 votes

Frequently Asked Questions


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You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


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