03:58:38

Complete SQL course for Data Science - From Scratch

08:26

Zero Inflated Regression - Teach your models to predict zeros | Hidden Gems of Data Science

08:46

Poisson Distribution using Example | #27 in Statistics for Data Science

09:55

Exercise: Binomial Distribution (solved) | #26 in Statistics for Data Science

10:09

Understanding Binomial Distribution using Examples | #25 in Statistics for Data Science

05:48

Bernoulli Distribution Clearly Explained | #24 in Statistics for Data Science

04:56

Kernel Density Estimation | #23 in Statistics for Data Science

04:53

Box Cox Transformation | #22 in Statistics for Data Science

05:12

Skewness and Kurtosis | #16 in Statistics for Data Science

04:11

Central Limit Theorem | #19 in Statistics for Data Science

06:00

Z-Score and Standardization | #17 in Statistics for Data Science

08:58

Proof that R Squared is Squared Pearson Correlation Coefficient | Hidden Gems of Data Science

15:59

R Squared is Problematic Regression metric | Hidden Gems of Data Science

10:34

CDF of Normal Distribution | #15 in Statistics for Data Science

08:23

PDF of Normal Distribution | #14 in Statistics for Data Science

05:53

Normal Distribution aka Gaussian | #13 in Statistics for Data Science

03:31

Law of Large Numbers - Exercise | #12 in Statistics for Data Science

03:29

Law of Large Numbers | #11 in Statistics for Data Science

02:30

Why divide variance by n-1 (Part 2) | #9 in Statistics for Data Science

09:18

Why divide variance by n-1 (Part 1) | #8 in Statistics for Data Science

07:41

Population vs Sample | #7 in Statistics for Data Science

05:15

Standard Error | #6 in Statistics for Data Science Course

08:07

Measures of Dispersion | #5 in Statistics for Data Science Course

04:42

Quantiles vs Percentiles vs Quartiles vs Deciles | #4 in Statistics for Data Science Course

10:17

Measures of Central Tendency | #3 in Statistics for Data Science Course

06:22

#2 Types of Data in Statistics | Statistics for Data Science Course

01:38

Complete Statistics for Data Science Course - Introduction

12:05

Decile Analysis in Action: Real-World application with coding example + Jupyter notebook

05:42

How to Interpret Quantile Quantile Plot (QQ Plot)

09:28

Robust Regression with Huber Loss - Clearly Explained

05:45

Theil-Sen Regression Algorithm, Clearly Explained

07:12

RANSAC - Outlier Resistant Regression, Algorithm Clearly Explained

05:18

How to convert Python to Cython in Jupyter Notebook (and Speed Up 100X)

14:46

How to convert Python to Cython (and Speed Up 100X)

10:52

KL Divergence - Intuition and Math Clearly Explained

13:07

The Probe method for Feature Selection (highly overlooked)

28:24

Bayesian Target Encoding to boost model accuracy - Clearly Explained

18:04

Feature Scaling Techniques-Avoid this untraceable mistake at all costs

11:14

Train, Test and Split: This unforgivable mistake will cost your model's credibility

01:02

Announcement: Want to Learn SQL Advanced? | Free Live Session, Don't miss.

14:10

Understanding Target Encoding for Categorical Features

08:15

Feature Encoding in ML: Beyond the Basics

18:02

Isolation Forest: A Tree based approach for Outlier Detection (Clearly Explained)

15:40

Understanding Cooks Distance to detect influential observations

18:48

Why mahalanobis distance is incredibly powerful for outlier detection

12:44

How to Detect Outliers with Z Score | Clearly Explained

11:53

How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python

08:41

How to impute missing data in categorical features (using MICE)

15:25

Multiple Imputation by Chained Equations (MICE) clearly explained

33:17

How to handle missing data for machine learning

34:18

Exploratory Data Analysis (EDA) - Use these 5 tactics on any ML project (..and wow!!)

12:38

Reduce the memory size of Pandas Dataframe: Do this to make your code run 5X FASTER

15:55

What is ML Modeling? (Problem statement and Data) | Build Your First ML Project - Part 5

29:17

Jupyter Notebook Tutorial - How to Install and complete walkthrough

15:30

Setup Python Environment using ANACONDA

10:47

Build Your First ML Project part 2: How to FORMULATE Machine Learning Problem

01:46

Build Your FIRST Machine Learning Project (Code + Concepts)

04:23

Role of Significance Test in Machine Learning | #28 of 28 | Foundations of ML: The Big Picture

03:52

Basics Statistical Concepts in ML - Part 2 | #27 of 28 | Foundations of ML: The Big Picture

06:58

Basics Statistical Concepts in ML - Part 1 | #26 of 28 | Foundations of ML: The Big Picture