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All LessonsFree Lessons (3)

Introduction to Course

Getting Started Why Learn Machine Learning

Introduction to Machine Learning

Types of Self Learning

Introduction to Datasets

Machine Learning Libraries

Exploratory Data Analysis

Import Dataset Data Frame Functions Quiz

Data Scrubbing

Introduction to Data Scrubbing Data Scrubbing Operation: Removing Variables Data Scrubbing Operation: One-Hot Encoding Data Scrubbing Operation: Drop Missing Values Data Scrubbing Operation: Dimension Reduction Quiz

Pre-Model Algorithms

PCA Implementation Steps: 1 to 3 PCA Implementation Steps: 4 to 6 K-Means Clustering Implementation Steps: 1 to 3 K-Means Clustering Implementation Steps: 4 to 6 Quiz

Project

Customer Segmentation with K-Means Clustering

Split Validation

Quick Overview of Split Validation Quiz

Model Design

Introduction to Model Design MD Implementation Steps: 3 to 8 MD Implementation Steps: 9 and 10 Quiz

Linear Regression

Implementation of Linear Regression LR Implementation Steps: 1 to 3 LR Implementation Steps: 4 to 7 LR Implementation Steps: 8 and 9 Quiz

Logistic Regression

Implementation of Logistic Regression

Logistic Regression Steps: 1 to 4

Logistic Regression Steps: 5 to 7

Logistic Regression Steps: 8 and 9

Support Vector Machines

Implementation of Support Vector Machines SVM Implementation Steps: 1 to 7 SVM Implementation Steps: 8 and 9 Quiz

K-Nearest Neighbors

Introduction to K-Nearest Neighbors k-NN Implementation Steps: 1 to 5 k-NN Implementation Steps: 6 to 9 Quiz

Tree-Based Methods

Introduction to Tree-Based Methods 1- Decision Trees DT Implementation Steps: 1 to 4 DT Implementation Steps: 5 to 7 2- Random Forests Implementation of Random Forest 3- Gradient Boosting Implementation of Gradient Boosting Classifier Implementation of Gradient Boosting Regressor Quiz

Project

Cardiovascular Disease Risk Prediction with Random Forest

Conclusion

Appendix

Basics of Python Programming Development Environment

A Practical Guide to Machine Learning with Python/

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Quiz

Quiz

We'll cover the following...

Technical Quiz

1.

We need to build a prediction model to predict the gender of five different species of penguins based on their weight, height, and color. How many output variables will our model have?

A.

3

B.

2

C.

5

D.

1

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