Business Machine Learning - AI-Powered Learning for Developers

Delve into business machine learning, gaining insights into core algorithms, tuning techniques, and evaluation metrics. Learn about SHAP, LIME, and developing customized machine learning solutions.

Intermediate

114 Lessons

35h

Certificate of Completion

Delve into business machine learning, gaining insights into core algorithms, tuning techniques, and evaluation metrics. Learn about SHAP, LIME, and developing customized machine learning solutions.

AI-POWERED

Explanations

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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

Get familiar with developing practical machine learning skills through lessons and hands-on exercises.

About This Course

Linear Regression

Get started with linear regression, its data exploration, modeling, evaluation, and deployment.

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

Master the steps to using regularization techniques to control overfitting and enhance model accuracy.

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

Bias-Variance Trade-off

Grasp the fundamentals of the bias-variance trade-off in modeling student morale trends.

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

Take a closer look at handling categorical data, creating dummies, and eliminating redundancy for effective ML models.

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

Follow the process of implementing, understanding, and evaluating logistic regression for binary classification.

Logistic Regression: Titanic Data

10 Lessons

Build on logistic regression for Titanic dataset, covering preprocessing, modeling, evaluation, and feature importance.

Sentiment Analysis Using Multinomial Logistic Regression

Project

Multiclass Classification and Handling Imbalanced Classes

6 Lessons

Learn how to use logistic regression for multiclass classification and handle imbalanced datasets.

Project: Predicting Chronic Kidney Disease

4 Lessons

Solve challenges with predicting chronic kidney disease using advanced machine learning techniques.

10.

K-Nearest Neighbors

5 Lessons

Break apart K-Nearest Neighbors for a better understanding of its principles and challenges.

11.

Implementation of K-Nearest Neighbors

7 Lessons

Grasp the fundamentals of implementing, optimizing, and comparing KNN models for effective decision-making.

12.

Logistic Regression vs. KNN

6 Lessons

Solve problems in selecting and optimizing logistic regression or KNN for classification.

13.

Decision Tree Learning

14 Lessons

Tackle decision trees, random forests, EDA, feature importance, hyperparameters, and visualization techniques.

Implement the Decision Tree Classifier from Scratch

Project

14.

Bootstrapping and Confidence Interval

5 Lessons

Build on estimating uncertainty with bootstrapping and describing confidence intervals for mean and median.

15.

Support Vector Machine

9 Lessons

Sharpen your skills in SVM through visualization, feature selection, hyperparameter tuning, and model evaluation.

16.

Practice and Comparisons

3 Lessons

Unpack the core of model performance comparisons using SVMs, CNNs, and logistic regression.

17.

What's Next?

1 Lesson

Master the steps to advance in machine learning careers and explore further learning opportunities.

18.

Appendix

1 Lesson

Grasp the fundamentals of evaluating model fit with R-squared and adjusted R-squared.

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

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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