What is Data Science || Roadmap || Applications || Job Roles

Components of Data Science || Facts

Numpy || Python

Pandas || Part-1

Pandas || Part 2

Matplotlib || Data Visualization || Data Science || Overview

Natural Language Processing || NLP || Uses and Problems Faced || Ambiguities in NLP

Probability and Statistics || Probability || Chance || Event || Outcome|| Sample space || Sets

Probability and Statistics || Counting Techniques || Sum || Product || Permutation || Combination

Statistics and Probability || Intro To Statistics and its types || Sample and Population

Statistics || Measure of Central Tendency || Mean || Median || Mode

Statistics || Measure of Dispersion || Range || Variance || SD || Quartiles || Interquartile range

Statistics || Measures Of Shape || Skewness and Kurtosis || @datatechinfo

Exploratory Data Analysis-EDA||Overview ||Importance of EDA in Data Analysis||EDA on Penguin dataset

Inter Quartile Range || IQR || Outlier Detection || Data Analysis || EDA || @datatechinfo

[Private video]

How Decision Tree works||Entropy and Information Gain ||Choosing a Root node & Predicting the output

One Hot Encoding || Encoding Techniques Practical Implementation || Data Science @datatechinfo

Label Encoding || Encoding Techniques Practical Implementation || Data Science @datatechinfo

What is Standardization || Explanation with Implementation Example|| Data Science @datatechinfo

What is Normalization || Explanation with Implementation Example|| Data Science @datatechinfo

Difference Between Standardization and Normalization || Use cases Data Science @datatechinfo

Pandas Data Cleaning Techniques @datatechinfo |Data Science |Data Analyst|| Python for data cleaning

Clear the confusion about Confusion Matrix || Machine Learning || Data Science || @datatechinfo

Understanding Type-1 and Type – 2 Errors || Data Science || Machine Learning @datatechinfo

Understanding Accuracy, Precision, Recall and F1-score with sample Data @datatechinfo