Develop an End-to-End Machine Learning Model

Explore the ML development process through a standardized framework.

2 Hours 24 Lessons

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    What you'll learn—and how you can apply it

    By the end of this hands-on course, you’ll understand:

    Phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, EDA and data preparation/transformation, Stages in developing and evaluating an ML model, Long-term considerations for model maintenance (MLOps)

    And you’ll be able to:

    Use the CRISP-DM framework for developing ML solutions
    Frame a business problem as a data problem
    Evaluate the quality of training data and how to prepare it for modeling

    Description

    This course offers a deep dive into the topics of training, tuning, evaluating, and selecting a machine learning (ML) model. We’ll cover business understanding for ML solution development, exploratory data analysis (EDA), model development, hyperparameter optimization, and considerations for MLOps. Jupyter Notebook exercises in Python are integrated throughout the course.

    This training is for you because...

    • You’re an aspiring analyst, software developer/engineer interested in learning about ML
    • You work with data and understand the basics of Python coding
    • You are looking to become a data scientist and/or ML/MLOps engineer

      Prerequisites

      • Basic understanding of math, statistics, and probability
      • Basic understanding of ML fundamental concepts
      • Ability to understand mathematical notation
      • Experience programming in Python

      Setup

      To open Anaconda Notebooks:

      1. Go to Anaconda Notebooks
      2. Click on 'Sign Up' or 'Sign In' if you already have an account from the top navigation menu
      3. Click 'Launch Notebook'

      Recommended preparation

        Instructor

        John Sukup

        John is the Managing Director and Founder of Expected X, with 17+ years of experience in data analytics, data science, and AI/ML engineering.

        Curriculum

        24 Lessons
        Getting started with Anaconda Notebooks
        Course Overview and Introduction
        What is Machine Learning?
        Machine Learning Exercise: Inputs and Outputs
        Machine Learning vs. Traditional Programming
        CRISP-DM Framework
        Problem Framing for Lab: Predict Home Values
        Model Performance Measures
        Lab Overview
        Exploratory Data Analysis and Preparation
        Exploratory Data Analysis and Preparation Continued
        Feature Engineering
        Data Cleaning
        Feature Scaling
        Model Training and Evaluation
        Model Selection
        Hyperparameter Optimization
        Model Development
        Evaluation Using Cross-validation
        Hyperparameter Optimization Techniques
        Test Set Evaluation
        Considerations for MLOps
        Summary and ML Model Exercises
        End of course survey
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          Get Access

          Instructor

          John Sukup

          John is the Managing Director and Founder of Expected X, with 17+ years of experience in data analytics, data science, and AI/ML engineering.