Ultimate ML Bootcamp #8: Machine Learning Pipeline

Name: Ultimate ML Bootcamp #8: Machine Learning Pipeline
Rating: 3.4 (10 reviews)

Master the Fundamentals of Machine Learning Pipeline

Created byMiuul Data Science & Deep Learning

Last updated 9/2024

English

English [Auto],

What you'll learn

Build and manage a complete machine learning pipeline from data preparation to model deployment.
Perform Exploratory Data Analysis (EDA) to uncover insights and guide model development.
Optimize model performance through hyperparameter tuning and ensemble learning techniques.
Deploy machine learning models to make predictions on new, unseen data.

Course content

1 section • 10 lectures • 1h 10m total length

Course Materials0:03
Introduction1:50
Join this introduction to building an end-to-end machine learning pipeline, automating data engineering, model selection, hyperparameter optimization, and stacking ensemble learning for a diabetes research task.
EDA13:56
Explore end-to-end diabetes modeling through exploratory data analysis, data preprocessing, feature engineering, and pipeline construction. Apply stacking and ensemble learning, with hyperparameter optimization, to predict new observations.
Data Preprocessing2:58
Standardize the independent variables with a standard scaler to support distance-based knn, and explore feature engineering on the diabetes data set as part of data preprocessing.
Base Models5:07
Explore base models in the research phase and apply hyperparameter optimization, then use a three-model ensemble with stacking to evaluate classifiers via cross-validated ROC AUC, F1, and accuracy.
Hyperparameter Optimization7:14
Automate hyperparameter optimization across multiple models using a single pipeline and grid search, comparing scores before and after optimization to identify the best models.
Stacking & Ensemble Learning6:29
Explore stacking ensemble learning and voting classifiers that combine diverse models like random forest, lightgbm, and knn to boost classification performance through hard and soft voting.
Prediction for a New Observation3:56
Test a voting classifier to predict diabetes for a random user; save and load the model with Joblib, ready for web or mobile deployment within a machine learning pipeline.
Pipeline20:20
Explore the machine learning pipeline from data reading and preprocessing to hyperparameter optimization and voting classifiers, then save the final model as a pkl and run from the command line.
Prediction8:40
Deploy the saved model by preparing new data with the same feature schema, importing a data prep module, and running prediction or scoring with joblib while addressing dimension errors.

Requirements

Basic understanding of machine learning concepts and Python programming.
Experience with machine learning frameworks is beneficial but not required.

Description

Welcome to the eighth and final chapter of Miuul's Ultimate ML Bootcamp—a comprehensive series designed to bring your machine learning expertise to its peak by mastering the complete machine learning pipeline. In this chapter, "Machine Learning Pipeline," you will learn to build an end-to-end workflow that integrates all the essential steps to develop, validate, and deploy robust machine learning models.

This chapter begins with an introduction, setting the foundation by outlining the critical stages involved in developing a successful machine learning solution. You will then move into Exploratory Data Analysis (EDA), where you will learn how to understand and prepare your data, identifying patterns, anomalies, and relationships that inform model development.

Next, we'll focus on Data Preprocessing, covering techniques for cleaning, transforming, and preparing your data to ensure optimal model performance. This will be followed by a session on building Base Models, providing you with a starting point for further model optimization.

We will then dive deep into Hyperparameter Optimization, where you will learn to fine-tune your models to enhance their predictive power. From there, the chapter progresses to Stacking and Ensemble Learning, combining multiple models to achieve superior performance.

Moving forward, we'll cover Prediction for a New Observation, guiding you through the process of making predictions on unseen data using your trained models. The chapter will then come full circle with a focus on constructing and implementing the entire Machine Learning Pipeline, tying together all the elements you've learned throughout the course.

Throughout this chapter, you will gain hands-on experience in each step of the machine learning pipeline, from data preparation to model deployment. You will learn how to create efficient workflows that streamline the development process and produce reliable, high-performing models ready for production.

We are excited to guide you through this final chapter, equipping you with the skills to build and deploy machine learning solutions end-to-end. Let’s embark on this final step of your journey and solidify your mastery of machine learning!

Who this course is for:

Data scientists and machine learning practitioners who want to enhance their end-to-end workflow skills.