- Getting Started
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How to Use Anaconda Notebooks 00:01:02
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Course Overview and Introduction 00:07:28
- Machine Learning Model Overview
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What is Machine Learning? 00:07:19
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Machine Learning Exercise: Inputs and Outputs 00:06:10
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Machine Learning vs. Traditional Programming 00:06:56
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CRISP-DM Framework 00:03:24
- Developing an ML Model End-to-End
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Problem Framing for Lab: Predict Home Values 00:08:48
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Model Performance Measures 00:08:20
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Lab Overview 00:03:04
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Exploratory Data Analysis and Preparation 00:11:56
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Exploratory Data Analysis and Preparation Continued 00:07:24
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Feature Engineering 00:02:26
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Data Cleaning 00:08:50
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Feature Scaling 00:04:46
- Model Training and Evaluation
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Model Training and Evaluation 00:03:35
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Model Selection 00:03:28
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Hyperparameter Optimization 00:02:00
- Model Development
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Model Development 00:04:07
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Evaluation Using Cross-validation 00:03:58
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Hyperparameter Optimization Techniques 00:05:20
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Test Set Evaluation 00:01:17
- Conclusion
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Considerations for MLOps 00:03:48
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Summary and ML Model Exercises
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End of Course Survey
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Certificate Info
Develop an End-to-End Machine Learning Model
Explore the ML development process through a standardized framework.
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.
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
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:
- Go to https://anaconda.cloud
- Click on 'Notebooks' from the top navigation menu
- Create an account or login if you already have one
Recommended preparation
- Introduction to Python Programming on-demand course
- Introduction to Machine Learning on-demand course
About the Instructor
John Sukup is Principal Consultant at Expected X, a data strategy and MLOps solutions consultancy providing clients with detailed, graduated approaches to realizing the full potential of working with data. His experience working with data spans 16 years from consumer market research, to data science, to machine learning engineering. He has acted as the lead professional trainer for machine learning and related topics at Cisco Systems and has been featured in Forbes, Oracle, and Data Science Central.
Questions? Issues? Contact [email protected].