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Intermediate

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Business Machine Learning
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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.

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Overview

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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.

AI has enabled us to develop machine learning algorithms that learn from patterns in the data to make predictions and help organ...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

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

Content

114 Lessons12 Quizzes

Course Introduction

1 Lessons

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

About This Course

Linear Regression

16 Lessons

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.

Introduction to Regularization

Penalties

Effect of Regularization

Model Performance

Summary

Quiz: Regularization

Bias-Variance Trade-off

7 Lessons

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

6 Lessons

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.

Introduction to Logistic Regression

Probability and Odds

Understanding Logistic Regression

Logistic Regression Implementation

Model Evaluation

Summary

Quiz: Logistic Regression

Logistic Regression: Titanic Data

10 Lessons

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

The Dataset and Exploratory Data Analysis

Data Preprocessing

Getting Data Ready and Building Machine Learning Model

Predictions and Evaluation

Model Evaluation

Feature Importance and Model Explainability

Summary

Exercise: Retrain the Model Without Pclass Dummies

Solution: Retrain the Model Without Pclass Dummies

Quiz: Logistic Regression—Titanic Data

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.

Introduction to Multiclass Logistic Regression Classification

Imbalanced Datasets and Techniques to Handle Them

Machine Learning and Imbalanced Data

Performance of the Trained Models on Unseen Data

Summary

Quiz: Multiclass Classification and Handling Imbalanced Classes

Project: Predicting Chronic Kidney Disease

4 Lessons

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

Problem Definition and the Data

Exploratory Data Analysis

Data Preprocessing

Model Training and Evaluation

10.

K-Nearest Neighbors

5 Lessons

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

Introduction to K-Nearest Neighbors

Working Principle of K-Nearest Neighbors

Visualize the Working of K-Nearest Neighbors

Final Thoughts

Quiz: K-Nearest Neighbors

11.

Implementation of K-Nearest Neighbors

7 Lessons

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

The Dataset and Exploratory Data Analysis

Model Training Using Unscaled Data

Effect of Feature Scaling

Model Training Using Scaled Features

Elbow Method to Chose the K Value

Model Comparisons

Quiz: Implementation of K-Nearest Neighbors

12.

Logistic Regression vs. KNN

6 Lessons

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

The Dataset and Exploratory Data Analysis

Baseline Accuracy and Machine Learning Model Selection

Exercise: Find the Best Combination of Parameters

Solution: Find the Best Combination of Parameters

Exercise: Find the Best Value for K

Solution: Find the Best Value for K

13.

Decision Tree Learning

14 Lessons

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

Introduction to Decision Trees

Bootstrap, Bagging, and Random Forests

The Dataset and Exploratory Data Analysis

Machine Learning

Feature Importance

Hyperparameters and Their Tuning

ROC Curve

Playing With Probability Cut-off

Tree Visualization

Exercise: Train a Decision Tree Model Using the Gini Criteria

Solution: Train a Decision Tree Model Using the Gini Criteria

Exercise: Train a Random Forest Model Using the Entropy Criteria

Solution: Train a Random Forest Model Using the Entropy Criteria

Quiz: Decision Tree Learning

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.

Introduction to Bootstrapping and Confidence Interval

The Data

Confidence Interval of Mean

Confidence Interval for the Median

Quiz: Bootstrapping and Confidence Interval

15.

Support Vector Machine

9 Lessons

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

Introduction to Support Vector Machine

Visualize the Working of the Support Vector Machine

The Dataset and Exploratory Data Analysis

ROC Curve and Saving the Model

Quiz: Support Vector Machine

16.

Practice and Comparisons

3 Lessons

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

MNIST: Handwritten Digits Dataset

The Iris Dataset

The Circles Dataset

17.

What's Next?

1 Lessons

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

Final Thoughts and Future Directions

18.

Appendix

1 Lessons

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.

Course Author:

Dr. Junaid Qazi

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Business Machine Learning Save

Business Machine Learning
Save