End-to-end Machine Learning Decision trees

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BESTSELLER | Video: AVC 1280x720 | Audio: AAC 48KHz 2ch | Duration: 3 Hours | Lec: 22 | 1.08 GB | Language: English | Sub: English [Auto-generated]

Build a transit time predictor in python

What you'll learn
Build a decision tree from scratch.
Tune a machine learning model's hyperparameters and evaluate its performance.
Use Pandas DataFrames to manipulate data and make statistical computations.
Create a custom model visualization using matplotlib.
Use decision trees to make predictions.
Build your own transit time predictor.
Handle dates and times cleanly using python's datetime package.
Save and restore data using python's pickle package.
Programmatically dowload and parse data in a JSON format.
Interpret command line arguments using python's argparse package.

Requirements
It will help if you are already familiar with the basics of python.

Description
In this course, we'll build and use decision trees, a popular and versatile tool that will serve you well in your applied machine learning work.

The data science problem we want to solve is predicting transit times on a public transportation system. We will walk through the entire process from end to end:

Define the problem

Gather the data

Clean and prepare the data

Build a decision tree

Train the model

Generate predictions

Wrap the model in some code that makes it easy to use

If you are a professor or a teacher at any level, you are welcome to evaluate the course for free, and I can set your students up with a deep educational discount. Just contact me for the coupon code ([email protected]).

I hope you enjoy the process of building a complete solution to a data science problem from the ground up.

Who this course is for?
Beginning and intermediate machine learning students and data scientists looking to round out their skill set on a realistic problem.
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