Business Machine Learning

Business Machine Learning

This course uses a hands-on approach to introduce core concepts of data science. You will work on projects using real world data.

Intermediate

114 Lessons

35h

Certificate of Completion

This course uses a hands-on approach to introduce core concepts of data science. You will work on projects using real world data.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

359 Playgrounds
12 Quizzes

This course includes

359 Playgrounds
12 Quizzes

Course Overview

AI has enabled us to develop machine learning algorithms that learn from patterns in the data to make predictions and help organizations make informed decisions and optimize their business workflow. This course uses a hands-on approach to introduce core algorithms that are considered a workhorse in the field of data science and business machine learning. Along with business statistics, you’ll learn the working principles behind these algorithms and how they can be tuned for improved performance. You’ll als...Show More

What You'll Learn

An understanding of the theoretical foundations with hands-on coding examples

The ability to train, optimize, evaluate, and deploy various machine learning models

Familiarity with the process to select the most suitable models to tackle practical problems

Hands-on experience with handling different types of data for machine learning modeling

The ability to tweak various parameters to improve accuracy of machine learning models

A working knowledge of using hands-on projects and exercises on real data sets

What You'll Learn

An understanding of the theoretical foundations with hands-on coding examples

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Course Content

1.

Course Introduction

2.

Linear Regression

3.

Regularization

4.

Bias-Variance Trade-off

5.

Categorical Features

6.

Logistic Regression

7 Lessons

7.

Logistic Regression: Titanic Data

10 Lessons

8.

Multiclass Classification and Handling Imbalanced Classes

6 Lessons

9.

Project: Predicting Chronic Kidney Disease

4 Lessons

10.

K-Nearest Neighbors

5 Lessons

11.

Implementation of K-Nearest Neighbors

7 Lessons

12.

Logistic Regression vs. KNN

6 Lessons

13.

Decision Tree Learning

14 Lessons

14.

Bootstrapping and Confidence Interval

5 Lessons

15.

Support Vector Machine

9 Lessons

16.

Practice and Comparisons

3 Lessons

17.

What's Next?

1 Lesson

18.

Appendix

1 Lesson

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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