Overview
Syllabus
1.Introduction to Python libraries(Data Scientist's arsenal).
2.Introduction to Python Datasets (.csv files).
3.Dataset Missing Values & Imputation (Detailed Python Tutorial) | Impute Missing values in ML.
4.One Hot Encoding to process Categorical variables (Python) | Process Categorical Features.
5.Split data into Training and Test set in Data Science (Python) | Train Test Split function in ML.
6.Feature Scaling in Machine Learning(Normalization & Standardization) | Feature Scaling Sklearn.
7.Outlier Detection and Treatment using Python - Part 1 | How to Detect outliers in Machine Learning.
8.Outlier Detection and Treatment using Python - Part 2 | How to Detect outliers in Machine Learning.
9.Outlier Detection and Treatment using Python - Part 3 | How to Detect outliers in Machine Learning.
Log Transformation for Outliers | Convert Skewed data to Normal Distribution.
Outlier Treatment through Square Root Transformation | Convert Skewed data to Normal Distribution.
Python Pandas Tutorial - Adding & Dropping columns (Machine Learning).
Create Pivot table using pandas DataFrame (Python).
Use Regular Expression to split string into Dataframe columns (Pandas).
Python Pandas Tutorial Series: Using Map, Apply and Applymap.
Python Pandas Tutorial - Merge Dataframes (Machine Learning).
Taught by
The AI University