Machine Learning Tutorial Chap 2| Part-4 Inferential Statistics | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 17/05/2019

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Welcome to the #DataScienceFridays Rohit Ghosh, a deep learning scientist, and an Instructor at GreyAtom will take us through Inferential Statistics for data science through a simple introduction series. 

Inferential statistics is a way to learn from data. It allows you to make predictions or inferences from the data. It is a tool in Machine Learning that uses a set of observations to discover underlying processes or patterns, then predict it.

Study Inferential Statistics in Machine Learning through this simple introduction for beginners. 

This is the 4th in 7 videos about Inferential Statistics in Machine Learning. 

In this video, we will explore ‘Sample vs Population & Central limit theorem’ in Inferential Statistics.

Complete Playlist for the Course: https://bit.ly/2Q1zvK6 


After completing our 7-part Series on Inferential Statistics, you will be able to do the following

- Learn what is Inferential Statistics and why is it useful
- Learn what is z-score and how it can be applied
- Understand point-estimates and confidence intervals
- Understand Hypothesis testing and related concepts
- Learn one-sample and two-sample t-test, Chi-Squared Goodness-Of-Fit Test, and ANOVA

Here’s the full syllabus of our 7-part video tutorial on Inferential Statistics in Machine Learning for beginners:

Introduction to Probability 
Basic Probability
Conditional Probability
Normal Distribution
Standard Deviation
Introduction to Inference
Sample Mean & Population Mean
Statistical Inference
Central Limit Theorem
Confidence Intervals
Hypothesis Testing
Type I and Type II error
Chi-squared Goodness of fit test


#machinelearningtutorial #inferentialstatistics #DataScience101 #Greyatom


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