End-to-End Machine Learning

Evaluate a Binary Classifier

This project is part of the liveProject series End-to-End Machine Learning for Rain Prediction.
prerequisites
basic Python • basic pandas • basic NumPy • basic Matplotlib • basic seaborn • basic scikit-learn • basic Jupyter Notebook • basics of machine learning • basics of exploratory analysis
skills learned
evaluate predictions and accuracy • visualize patterns with classes • conduct hyperparameter tuning • prepare a model for production deployment
Harshit Tyagi
1 week · 3-5 hours per week · INTERMEDIATE

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Judging the effectiveness of a machine learning model requires in-depth analysis. This quick liveProject builds on the work you have completed in Machine Learning for Classification. You’ll assess your early models and consider better alternatives. You’ll plot the ROC curves of the model and compare it to multiple dummy models, and tune your hyperparameters to deliver the most accurate results possible.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

project author

Harshit Tyagi
Harshit Tyagi has helped over a thousand students master the fundamentals of programming and data science. In his roles at OpenClassrooms and Coding Ninjas, he leverages his technical expertise to conduct workshops and help students bring their course projects to the finish line. He also has a YouTube channel, where he covers fundamental concepts in data science and Python, interview tips, and more. In addition to focusing on data science education, Harshit has developed data processing algorithms with research scientists at Yale, MIT and UCLA.

prerequisites

This liveProject is for Python data scientists who want to expand their capabilities in evaluating and tuning machine learning models. To begin this liveProject you will need to be familiar with:

TOOLS
  • Basic Python
  • Basic pandas
  • Basic NumPy
  • Basic Matplotlib
  • Basic seaborn
  • Basic scikit-learn
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of machine learning
  • Basics of exploratory analysis
  • you will learn

    In this liveProject, you’ll learn skills for cleaning and exploring data in preparation for training a machine learning model.

    • Manipulating data and exploratory analysis
    • Evaluating predictions, accuracy, and other metrics
    • Visualizing patterns with correlation pair plots and heatmaps
    • Hyperparameter tuning with classes and other methods
    • Preparing a model for production deployment

    features

    Self-paced
    You choose the schedule and decide how much time to invest as you build your project.
    Project roadmap
    Each project is divided into several achievable steps.
    Get Help
    While within the liveProject platform, get help from other participants and our expert mentors.
    Compare with others
    For each step, compare your deliverable to the solutions by the author and other participants.
    book resources
    Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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