Business Machine Learning - AI-Powered Course

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

Course Content

Course Introduction

About This Course

Linear Regression

Introduction to Linear Regression

Welcome and Data Overview Exploratory Data Analysis Variance and Covariance Machine Learning Model Evaluation Model Explainability Finalizing and Serializing the Model Model Deployment in the Notebook Cross-validation Summary Exercise: Model the Boston Housing Dataset Solution: Model the Boston Housing Dataset Exercise: Model the King County's House Sales Dataset Solution: Model the King County's House Sales Dataset Quiz: Linear Regression

Regularization

Introduction to Regularization Penalties Effect of Regularization Model Performance Summary Quiz: Regularization

Bias-Variance Trade-off

Introduction and Data Generation Morale Function and Model Error Generating Student Samples and Plot Model Building to Predict the Morale Bias-Variance Trade-off Summary Quiz: Bias Variance Trade-off

Categorical Features

Quantitative vs. Qualitative Data and Creating Dummies Redundant Variables and Machine Learning Summary Exercise: Model the Auto MPG Dataset Solution: Model the Auto MPG Dataset Quiz: Categorical Features

Logistic Regression

7 Lessons

Logistic Regression: Titanic Data

10 Lessons

Multiclass Classification and Handling Imbalanced Classes

6 Lessons

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_

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_

Evan Dunbar

ML Engineer

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