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Business Machine Learning

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.

114 Lessons

2 Projects

35h

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LEARNING OBJECTIVES

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

Learning Roadmap

114 Lessons12 Quizzes

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

6 Lessons

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

Bias-Variance Trade-off

7 Lessons

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

Categorical Features

6 Lessons

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

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

Premium

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

Premium

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?

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

Final Thoughts and Future Directions

18.

Appendix

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

R-square and Goodness of the Fit

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

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 also explore a range of metrics to evaluate the predictive power of your trained algorithms. In this course, you’ll explore strategies to find the best parameters and learn how to use SHAP and LIME approaches to increase the explainability of your trained model. By the end of this course, you’ll be able to implement a complete process pipeline to build customized machine learning solutions for organizations.

ABOUT THE AUTHOR

Dr. Junaid Qazi

Dr. Junaid Qazi is a subject matter specialist, data science, machine learning and blockchain consultant. He is a team builder, advisor, professional development coach, mentor, author, technical writer, and invited speaker in his domain of expertise.

Learn more about Dr.

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