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End-to-End Machine Learning

Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.

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4 Hours16 Videos56 Exercises
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Course Description

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.

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.

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.

Transform your machine learning expertise with this comprehensive, hands-on course and become an end-to-end ML pro!

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In the following Tracks

Machine Learning Engineer

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  1. 1

    Design and Exploration

    Free

    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.

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    Designing an End-to-End Machine Learning Use Case
    50 xp
    Machine learning lifecycle phase definitions
    50 xp
    Machine learning lifecycle
    100 xp
    Exploratory Data Analysis
    50 xp
    Visualizing your data
    100 xp
    Finding class imbalance
    100 xp
    Goals of EDA
    100 xp
    Data preparation
    50 xp
    Data preparation functions
    100 xp
    Advanced Imputation
    100 xp
    Cleaning your dataset
    100 xp
  2. 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).

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In the following Tracks

Machine Learning Engineer

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Datasets

Heart Disease DatasetHeart Disease Cleaned

Collaborators

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George Boorman
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Arne Warnke
Joshua Stapleton HeadshotJoshua Stapleton

Machine Learning Engineer

Joshua Stapleton is a machine learning engineer and consultant with years of experience in the healthcare, defense, and education sectors. He currently works with a number of international companies and groups in a variety of capacities. He also works with AIExplained, a popular AI Youtuber, and is pursuing his Master’s in Machine Learning at Imperial College London.
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