Statistics in Machine Learning

Overview

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Dive into a comprehensive 18-hour course on Statistics in Machine Learning. Learn essential statistical concepts and their applications in data science, starting from the basics and progressing to advanced topics. Explore population vs. sample, probability distributions, central limit theorem, correlation coefficients, feature scaling, hypothesis testing, and more. Gain practical skills in handling outliers, performing sampling techniques, and interpreting statistical measures. Apply your knowledge using Python for data analysis and machine learning tasks. Master the fundamentals of probability, permutations, and combinations. Understand the importance of statistics in real-world scenarios and prepare for data science interviews with targeted question discussions.

Syllabus

How to Learn Statistics for Data Science As A Self Starter- Follow My Way.
Introduction To Statistics And Its Types For Starters.
Population vs Sample in Statistics.
Gaussian distribution or Normal Distribution in statisctics.
Log Normal Distribution in Statistics.
Covariance in Statistics.
STATISTICS- Mean, Median And Mode Explained Easily.
STATISTICS- Population VS Sample and it's Importance.
STATISTICS- What are Random Variables and It's Types and its Importance?.
STATISTICS- Gaussian/ Normal Distribution.
STATISTICS- What is Central Limit Theorem?.
STATISTICS- Chebyshev's InEquality.
Statistics- What is Pearson Correlation Coefficient? Difference between Correlation and Covariance.
Spearman's rank correlation coefficient- Statistics.
Statistics-Finding Outliers in Dataset using Z- score and IQR.
Standardization Vs Normalization- Feature Scaling.
What Is P Value In Statistics In Simple Language?.
Statistics-Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode.
Stats Interview Series #1- Asked In Interview.
Stats Interview Series #2-Asked In Interview.
Confidence Intervals In Statistics- Part 1.
Bernoulli distribution- Mean, Variance And Standard Deviation OF Bernoulli distribution.
Stats Interview Series #3-Asked In Interview #shorts⭐ ⭐⭐⭐⭐⭐⭐⭐.
5 Number Summary And How To handle Outliers Using IQR-Statistics.
Different Type Of Sampling Techniques With Examples| Statistics Interview Question.
Whether We Should Reduce False Positive Or Negative In Confusion Matrix-Machine Learning Interviews.
Z Score And Its Applications- Important Stats Interview Question.
Power Law Distribution And Its Examples And Application- Statistics Interview Question.
How To Perform Hypothesis Testing-Confidence Interval|Z Test Statistics| Derive Conclusion- Part 1.
All Important Topics In Probability For Data Science In 1 Video.
Permutation And Combination Easily Explained.
Why Sample Variance is Divided by n-1.
Covariance,Pearson Correlation And Spearman Correlation Coefficient With Real World Examples.
We Use Stats Everywhere!!.
Live Day 1- Introduction To statistics In Data Science.
Live Day 2- Basic To Intermediate Statistics.
Live Day 3- Intermediate Statistics With Python In Data Science.
Live Day 4- Advance Statistics With Python In Data Science.
Live Day 5- Advance Statistics With Python In Data Science.
Live Day 6- Advance Statistics With Python In Data Science.
Live Day 7- Summarizing Statistics With Python In Data Science.
How To Calculate P Value In Hypothesis Testing.