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Python & Machine Learning Bootcamp 1.0

at General Assembly - FINANCIAL DISTRICT

(2541)
Course Details
Price:
$499
Start Date:

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Location:
FINANCIAL DISTRICT
225 Bush St 5th Fl East Entrance
Btwn Sansome & Montgomery Streets
San Francisco, California 94104
(Map)
Important:
A computer will not be provided.
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Description
Class Level: Beginner
Age Requirements: 18 and older
Average Class Size: 20
Teacher: Craig Sakuma

Flexible Reschedule Policy: This provider has flexible, free rescheduling for any-in person workshop. Please see the cancellation policy for more details

What you'll learn in this python class:

Full course name: Python & Machine Learning Bootcamp 1.0: Data Cleaning & Classification

This bootcamp will provide hands-on experience completing the end-to-end data science process. Students will learn to collect, explore, and clean data in a variety of formats. After cleaning and preparing data, machine learning algorithms will be applied to build a predictive model. 

This bootcamp is for analysts, product managers, mathematicians, business managers, or anyone else that wants to learn how to automate analysis and build predictive models in Python.

This is a very practical and hands-on workshop that has lots of class exercises. You'll build your own library of Python scripts that can be reused after your done with the bootcamp. Students will have the skills to automate data collection/cleaning processes and implement machine learning algorithms.

Day 1
  • Introduction to Pandas Package
  • Reading and writing data with Pandas
  • Data exploration and analysis
  • Data cleaning with pandas
Day 2
  • Machine learning overview
  • K-Nearest Neighbors algorithm
  • Random Forest algorithm
  • Measuring model performance with Crossvalidation
  • Implementing predictive models with Scikit-Learn
Takeaways:
  • Collect data from a variety of sources (e.g., CSV files, Excel and SQL databases) using the Pandas package
  • Explore and clean/munge the data to prepare it for analysis
  • Test the K Nearest Neighbors and Random Forest classification algorithms using the Scikit-Learn package
  • Implement predictive model

Prereqs & Preparation:
Students should have completed the Introduction to Python Bootcamp or have equivalent skills, which include the following:
  • If Statements
  • For Loops
  • Function Definitions
  • Variables
  • Creating and Indexing Lists and Dictionaries
  • Importing Packages
You must bring a laptop with a text editor. Students should install Anaconda, which is a free package that includes python and a number of tools that will be used in class (http://continuum.io/downloads).

Advance your learning with General Assembly's Python Programming Course.

School Notes:
For students enrolling in 12 week part time and immersive classes, it is not recommended that you book more than one class simultaneously.

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Refund Policy
If you can't make it to a class/workshop, please email us at [email protected] at least 7 days before the scheduled event date. No refunds will be given after this timeframe.

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