Applied Machine Learning: Foundations

2h 17mBeginner2024-05-22

Authors

Matt Harrison

Python and Data Science Corporate Trainer, Author, Speaker, Consultant

Course details

AI models are transforming the workplace. Knowing what’s going behind those models can help you apply machine learning (ML) techniques more effectively. In this course, instructor Matt Harrison shows you how to get started mastering the essentials of machine learning using the power of the Python programming language.

Explore the fundamentals of an end-to-end machine learning application, as you gain hands-on experience of data exploration, data processing, model creation, model evaluation, model tuning, and model deployment with MLFlow. Along the way, test out your new coding skills in the practice challenges at the end of each section.

Skills covered

scikit-learnJupyterMachine LearningPythonArtificial Intelligence (AI)Open SourceOne-Off

Concepts

0. Introduction

01 - Mastering machine learning essentials
02 - What you should know

1. Introduction to Machine Learning

03 - Overview of types of machine learning
04 - Applications of ML
05 - Tools for ML
06 - Using GitHub Codespaces with this course

2. EDA

07 - Exploring the dataset
08 - Data preprocessing
09 - Scikit-learn pipelines
10 - Challenge - EDA plot
11 - Solution - EDA plot

3. Model Creation

12 - Dummy model
13 - Linear regression
14 - Decision trees
15 - CatBoost
16 - Challenge - Random forest pipeline
17 - Solution - Random forest pipeline

4. Model Evaluation

18 - R2
19 - Root mean squared
20 - Residual plot
21 - Challenge - Evaluate random forest
22 - Solution - Evaluate random forest

5. Model Tuning

23 - Hyperparameters and linear regression
24 - Tuning decision trees
25 - Tuning CatBoost
26 - Grid search
27 - Challenge - Tuning random forest
28 - Solution - Tuning random forest

6. Model Deployment

29 - End-to-end notebook
30 - Using MLFlow
31 - Challenge - MLFlow with random forest
32 - Solution - MLFlow with random forest

Conclusion

33 - Next steps