- Course Materials
- Introduction
- EDA
- Data Preprocessing
- Base Models
- Hyperparameter Optimization
- Stacking & Ensemble Learning
- Prediction for a New Observation
- Pipeline
- Prediction
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.
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!
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About the instructors
- 4.34 Calificación
- 5110 Estudiantes
- 18 Cursos
Miuul Data Science & Deep Learning
Data Science Team of Miuul.com
Miuul is an innovative education and technology company that focuses on creating career paths for the jobs of the future and offers consultancy services to companies undertaking data science projects.
Miuul enables people to learn high technologies quickly by designing learning experiences.
It has more than 30 thousand alums and more than 100 corporate customers.
Student feedback
Course Rating
Reviews
Poorly explained. A machine gun of information that makes hard to understand even for those used to.