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
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Content
1.
Course Introduction
1 Lessons
Get familiar with developing practical machine learning skills through lessons and hands-on exercises.
2.
Linear Regression
16 Lessons
Get started with linear regression, its data exploration, modeling, evaluation, and deployment.
Introduction to Linear RegressionWelcome and Data OverviewExploratory Data AnalysisVariance and CovarianceMachine LearningModel EvaluationModel ExplainabilityFinalizing and Serializing the ModelModel Deployment in the NotebookCross-validationSummaryExercise: Model the Boston Housing DatasetSolution: Model the Boston Housing DatasetExercise: Model the King County's House Sales DatasetSolution: Model the King County's House Sales DatasetQuiz: Linear Regression
3.
Regularization
6 Lessons
Master the steps to using regularization techniques to control overfitting and enhance model accuracy.
4.
Bias-Variance Trade-off
7 Lessons
Grasp the fundamentals of the bias-variance trade-off in modeling student morale trends.
5.
Categorical Features
6 Lessons
Take a closer look at handling categorical data, creating dummies, and eliminating redundancy for effective ML models.
6.
Logistic Regression
7 Lessons
Follow the process of implementing, understanding, and evaluating logistic regression for binary classification.
7.
Logistic Regression: Titanic Data
10 Lessons
Build on logistic regression for Titanic dataset, covering preprocessing, modeling, evaluation, and feature importance.
8.
Multiclass Classification and Handling Imbalanced Classes
6 Lessons
Learn how to use logistic regression for multiclass classification and handle imbalanced datasets.
9.
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.
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 Lessons
Master the steps to advance in machine learning careers and explore further learning opportunities.
18.
Appendix
1 Lessons
Grasp the fundamentals of evaluating model fit with R-squared and adjusted R-squared.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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