After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Introduction to End-to-End Machine Learning
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models with this comprehensive course. Through engaging, real-world examples and hands-on exercises, you'll learn to tackle complex data problems and build powerful ML models. By the end of this course, you'll be equipped with the skills needed to create, monitor, and maintain high-performing models that deliver actionable insights. Transform your machine learning expertise with this comprehensive, hands-on course and become an end-to-end ML pro!
Evaluate and Improve Your Model
Start by learning the essentials of exploratory data analysis (EDA) and data preparation - you'll clean and preprocess your data, ensuring it's ready for model training. Next, master the art of feature engineering and selection to optimize your models for real-world challenges; learn how to use the Boruta library for feature selection, log experiments with MLFlow, and fine-tune your models using k-fold cross-validation. Uncover the secrets of effective error metrics and diagnose overfitting, setting your models up for success.
Deploy and Monitor Your Model
You'll also explore the importance of feature stores and model registries in end-to-end ML frameworks. Learn how to deploy and monitor your model's performance over time using Docker and AWS. Understand the concept of data drift and how to detect it using statistical tests. Implement feedback loops, retraining, and labeling strategies to maintain your models' performance in the face of ever-changing data.
This course will equip you with practical skills directly applicable to a career as a data scientist or machine learning engineer, allowing you to design, deploy, and maintain models; crucial skills to leverage the business impact of machine learning solutions.
Prerequisites
Curriculum
Course outline
1
Design and Exploration
In this initial chapter,you will engage in the foundational stages of any machine learning project: designing an end-to-end machine learning use case, exploratory data analysis, and data preparation. By the end of the chapter, you will have a solid understanding of the early stages of a machine learning project, from conceptualizing a use case to preparing the data for further processing and model training.
- Designing an End-to-End Machine Learning Use Case50 XP
- Machine learning lifecycle phase definitions50 XP
- Machine learning lifecycle100 XP
- Exploratory Data Analysis50 XP
- Visualizing your data100 XP
- Finding class imbalance100 XP
- Goals of EDA100 XP
- Data preparation50 XP
- Data preparation functions100 XP
- Advanced Imputation100 XP
- Cleaning your dataset100 XP
2
Model Training and Evaluation
This chapter will delve deep into the essential processes of model training and evaluation. It comprises four comprehensive lessons, focusing on various aspects of feature engineering, model training, logging experiments, and model evaluation.
3
Model Deployment
This chapter delves into the essential elements of model deployment, a crucial phase in the machine learning lifecycle. Starting with testing, the chapter then progresses to architectural components, with a focus on feature stores and model registries. Subsequently, we will dive into the realm of packaging and containerization. The chapter concludes with an overview of Continuous Integration and Continuous Deployment (CI/CD).
4
Model Monitoring
In the final chapter, you will navigate the intricacies of model monitoring, a critical phase in the machine learning lifecycle.
End-to-End Machine Learning
Course
Complete
