Dive into a comprehensive 4.5-hour tutorial on machine learning data pre-processing and data wrangling using Python. Explore essential Python libraries for data science, learn to handle missing values and perform imputation, and master techniques like one-hot encoding for categorical variables. Discover how to split data into training and test sets, apply feature scaling methods, and detect and treat outliers. Gain proficiency in log and square root transformations for normalizing skewed data, and learn to manipulate DataFrames using Pandas, including adding and dropping columns, creating pivot tables, and merging datasets. Master the use of regular expressions for string manipulation and explore advanced Pandas functions like map, apply, and applymap to enhance your data wrangling skills.
Machine Learning Data Pre-processing and Data Wrangling Using Python
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
Related Courses
Related articles
Reviews
4.5 rating, based on 2 Class Central reviews
-
I recently completed the “Machine Learning Data Pre-processing and Data Wrangling Using Python” course offered by The AI University on YouTube. This course is an excellent resource for anyone looking to enhance their data science skills. The instructor does a fantastic job of breaking down complex concepts into easy-to-understand segments. The hands-on exercises and practical examples are particularly helpful in solidifying the learning experience. I appreciated the focus on real-world applications, which makes the content highly relevant and useful. Overall, I highly recommend this course to anyone interested in mastering data pre-processing and wrangling techniques using Python.
-
Very useful to learn about various machine learning techniques. It discusses all the details from scratch to end very clearly
