Three-Project Series
End-to-End Machine Learning for Rain Prediction you own this product
- prerequisites
- basic Python • basic pandas, Numpy, Matplotlib, seaborn, and scikit-learn • basic Jupyter Notebook • basic machine learning • basic exploratory analysis • basic Joblib/Pickle • basic Flask, Heroku, pipenv/virtualenv
- skills learned
- manipulate data and conduct exploratory analysis • input missing values • engineer outliers • evaluate predictions and accuracy • visualize patterns with classes • conduct hyperparameter tuning • prepare a model for production deployment • create virtual environments • GET and POST requests • create a web framework for a prediction app • deploy an app remotely on Heroku
In this multi-part liveProject series, you’ll harness the power of machine learning to make predictions about future rainfall. The Weather Department of Australia is having trouble handling meteorological data manually, and your challenge is to build an end-to-end machine learning model that can make on-the-fly predictions. You’ll use common Python data tools to clean and classify your dataset for analysis, train and evaluate your model, and then deploy your model to a remote server using Flask and Heroku. Work from beginning to end, or dive into whichever section will best augment your skills.
These projects are designed for learning purposes and are not complete, production-ready applications or solutions.
here's what's included
Project 1 Train a Binary Classifier
In this liveProject, you’ll explore a pre-made dataset of meteorological records. You’ll learn the shape, size and type of data at hand, and discover factors that affect rainfall. You use scikit-learn and logistics regression to make initial predictions about future rainfall, evaluate their accuracy, and visualize emerging patterns using Seaborn and Matplotlib.
Project 2 Evaluate a Binary Classifier
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.
Project 3 Deploy a Predictive Model
In this liveProject, you’ll deploy a machine learning model to production so it can be easily used by colleagues. You’ll create a virtual environment for deploying your application, use Flask to build a local web service that returns predictions, and Heroku for remote deployment.
book and video resources
When you start each of the projects in this series, you'll get full access to the following book and video for 90 days.
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- share your subscription with one other person
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End-to-End Machine Learning for Rain Prediction project for free
End-to-End Machine Learning for Rain Prediction project for free
team
monthly
annual
$49.99
$499.99
only $41.67 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
-
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End-to-End Machine Learning for Rain Prediction project for free
Prerequisites
This liveProject is for Python data scientists who want to expand their capabilities in preparing data for machine learning. To begin this liveProject you will need to be familiar with the following:
TOOLS
- Basic Python
- Basic pandas
- Basic NumPy
- Basic Matplotlib
- Basic seaborn
- Basic scikit-learn
- Basic Jupyter Notebook
- Basics of machine learning
- Basics of exploratory analysis
Note: The final milestone of Project 3 Deploy a Predictive Model uses Heroku to deploy the completed application. Heroko incurs a cost. There is intermittent use, and the Eco option ($5) will be sufficient to get the app working as Eco covers 1000 hours, and we will be using far less than that for this project.
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
- Imputing missing values, engineering outliers, and one-hot encoding
- Evaluating predictions, accuracy, and other metrics
- Visualizing patterns with correlation pair plots and heatmaps
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.
