Description

Sebastian Taylor & John Lee – Data Prep for Machine Learning in Python – CFI Education review, Sebastian Taylor & John Lee – Data Prep for Machine Learning in Python – CFI Education download, Sebastian Taylor & John Lee – Data Prep for Machine Learning in Python – CFI Education free

Sebastian Taylor & John Lee – Data Prep for Machine Learning in Python – CFI Education

Data Prep for Machine Learning in Python
Learn all the essential skills to prep your data in Python, from data cleaning and imputation to EDA, feature selection, and feature engineering.

• Improve the performance of machine learning models
• Quickly identify features of interest using efficient EDA
• Adapt datasets to the specific needs of each ML model

Overview
Data Prep for Machine Learning in Python Course Overview
Machine learning models rely on good data to produce meaningful insights. For that reason, data prep is one of the most critical skills for machine learning. In this course, you’ll learn how to import and clean data before populating missing values using imputation. You’ll learn how to visualize histograms, scatter charts, and box plots to identify trends of interest before using the analysis to select the most important features.   Feature engineering techniques such as one hot encoding, binning and scaling will help us transform the structure of our data to produce higher quality machine learning insights.

This data prep course in Python includes more interactive exercises and challenges than previous BIDA courses have. You will also have the opportunity to test your skills on a comprehensive guided Python case study before completing the final exam.

Data Prep for Machine Learning in Python Learning Objectives

• Upon completing this course, you will be able to:Import and clean your data in Python
• Apply imputation to estimate missing values in the dataset
• Conduct exploratory data analysis (EDA) to find initial patterns to guide our analysis
• Select features to focus on the most important variables
• Apply feature engineering to make datasets machine learning-friendly
• Select appropriate feature engineering techniques based on the model type

Who Should Take This Course?
Whether you are a business leader or an aspiring analyst exploring data science, this Data Prep for Machine Learning in Python course will serve as your comprehensive introduction to this fascinating subject. You’ll learn all the key terminology to allow you to talk data science with your teams, begin implementing analysis, and understand how data science can help your business.

What you’ll learn

Introduction to Data Prep
Introduction to Data Prep for Machine Learning
Pre-requisite Knowledge
Learning Objectives
A Quick Guide to Course Structure, Notebooks, and Exercises
Downloadable Files

Importing & Cleaning Data
Chapter Intro – Importing & Cleaning Data
Importing Data – CSV, Excel and SQL
Selecting Columns
Filtering Rows
Exercise – Import & Filter Data
Exercise Review – Import & Filter Data
Data Types Theory
Basic Data Validation Free Preview
Comparing to a Trusted Datasource
Exercise – Data Validation
Exercise Review – Data Validation
Imputation Theory
Cleaning Data
Data Type Errors
Imputation with Zeros
Basic Imputation of Values
Exercise – Cleaning & Imputation
Exercise Review – Cleaning & Imputation

Exploratory Data Analysis
Chapter Introduction – EDA
Descriptive Stats for Numeric Features
Basic Plots for Numeric Features
Basic Plots for Numeric Features + Combining Axis & Functions
Basic Plots for Categorical Features
Exercise Review – Visuals for Numeric & Categoric Features
Continuous vs Continuous Variable Analysis Part 1
Continuous vs Continuous Variable Analysis Part 2
Categorical vs Continuous Variable Analysis
Categorical vs Categorical Variable Analysis
Exercise Review – Creating and Analyzing Multivariate Plots

Train-Test Split (Recap)
Training Vs Testing
Train Test Split in SKLearn

Feature Engineering Part 1 – Encoding & Transformation
Chapter Intro – Feature Engineering
Training Vs Testing Theory
Encoding Theory (inc One Hot Encoding)
Identifying Categorical Columns & Values
One Hot Encoding in Pandas
One Hot Encoding in SKLearn
Exercise – One Hot Encoding
Exercise Review – One Hot Encoding
Exercise Review On Hot Encoding Pt 2
GetDummies vs OneHotEncoder
Transforming Distributions Theory
Identifying Skew in Python
Transforming Features in Python
Taking Logs Scenarios
Exercise – Transformations
Exercise Review – Transformations

Feature Engineering Part 2 – Outliers, Binning, and Scaling
Outliers Theory
Removing Outliers
Modifying Outliers
Exercise – Outliers
Exercise Review – Outliers
Binning Theory
Categorical Binning
Binning by Width & Frequency
Manual Binning
Final Thoughts on Binning
Smoothing
Smoothing in Practice
Exercise – Binning
Exercise Review – Binning
Final Thoughts on Binning
Why Feature Scaling Matters
Scaling Features Theory
Min Max Scaling
Scaling Testing Data
Final Thoughts on Scaling
Standard Scaler
Exercise – Scaling
Exercise Review – Scaling
Making Feature Engineering Decisions

Feature Selection
Chapter Intro – Feature Selection
Manual Feature Selection
Feature Selection with Continuous Target
Correlation Coefficients – Continuous Var + Continuous Feature
ANOVA – Continuous Target + Categorical Feature
Feature Selection with Categorical Target Variable
Box Plots – Categorical Var + Continous Feature
Chi-square – Categorical Var + Categorical Feature
Summary of Feature Selection Techniques

Course Summary
Course Summary

Qualified Assessment
Qualified Assessment

What our students say

This course is a must for anyone beginner of advanced, data analyst, data scientist and machine learning engineer.
MOHAMED ALIE KAMARA

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