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Python Machine Learning Bootcamp

Master the practical skills needed to solve real-world problems with machine learning. Explore time-tested algorithms and statistical concepts, and learn how to measure the accuracy of your models. Take your Python programming skills to the next level and excel in the field of machine learning.

  • Level All levels
  • Age 18 and older
  • Price $1,895
  • Location 185 Madison Ave, New York, NY
Python Machine Learning Bootcamp

Start Dates (6)

  • $1,895
  • Career Centers @ 185 Madison Ave Suite 1104, New York, NY 10016
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  • Mon, Mar 25 at 10:00am - 5:00pm
  • Tue, Mar 26 at 10:00am - 5:00pm
  • Wed, Mar 27 at 10:00am - 5:00pm
  • Thu, Mar 28 at 10:00am - 5:00pm
  • Fri, Mar 29 at 10:00am - 5:00pm
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  • Mon, Jun 10 at 6:00pm - 9:00pm
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  • Mon, Jun 17 at 6:00pm - 9:00pm
  • Mon, Jun 24 at 6:00pm - 9:00pm
  • Wed, Jun 26 at 6:00pm - 9:00pm
  • Mon, Jul 01 at 6:00pm - 9:00pm
  • Mon, Jul 08 at 6:00pm - 9:00pm
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  • Mon, Jul 15 at 6:00pm - 9:00pm
  • Wed, Jul 17 at 6:00pm - 9:00pm
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  • Sat, Jun 15 at 10:00am - 5:00pm
  • Sat, Jun 22 at 10:00am - 5:00pm
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  • Sat, Jul 20 at 10:00am - 5:00pm
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  • Mon, Jul 08 at 10:00am - 5:00pm
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  • Thu, Jul 11 at 10:00am - 5:00pm
  • Fri, Jul 12 at 10:00am - 5:00pm
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  • Mon, Sep 09 at 10:00am - 5:00pm
  • Tue, Sep 10 at 10:00am - 5:00pm
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  • Thu, Sep 12 at 10:00am - 5:00pm
  • Fri, Sep 13 at 10:00am - 5:00pm
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  • Tue, Nov 05 at 6:00pm - 9:00pm
  • Thu, Nov 07 at 6:00pm - 9:00pm
  • Tue, Nov 12 at 6:00pm - 9:00pm
  • Thu, Nov 14 at 6:00pm - 9:00pm
  • Tue, Nov 19 at 6:00pm - 9:00pm
  • Thu, Nov 21 at 6:00pm - 9:00pm
  • Tue, Nov 26 at 6:00pm - 9:00pm
  • Tue, Dec 03 at 6:00pm - 9:00pm
  • Thu, Dec 05 at 6:00pm - 9:00pm
  • Tue, Dec 10 at 6:00pm - 9:00pm
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Class Description

Description

What you'll learn in this python class:

This skillset is in high demand, as machine learning algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix.


This course will begin with linear and logistic regression, the most time-tested and reliable tools for approaching a machine learning problem. The course will then progress to algorithms with a very different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. You’ll also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.


The course will be focused on the practical skills needed to solve real-world problems with machine learning. The mathematical foundations for each machine learning algorithm will be explained visually, but there will not be a formal math component. Entering students are expected to be comfortable with writing Python programs, as well as the Numpy and Pandas libraries.

This course does require students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course. 

Pre-requisite:

This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course.

Learn more about Python Machine Learning Bootcamp at NYC Career Centers.

  

Syllabus

Fundamentals

Basic Regression Analysis

  • Linear Regression
  • Mean squared error
  • Training set vs Test set
  • Cross validation

Advanced Regression Analysis

  • Multi-linear regression
  • Feature engineering
  • Overfitting

Classification

Logistic Regression

  • Regression vs Classification
  • Logistic Regression
  • Sigmoid function

K-nearest Neighbors

  • K-nearest neighbors
  • Model-based vs memory-based
  • Parametric vs non-parametric
  • Evaluating performance

Decision Trees

Decision Trees

  • Decision tree
  • Interpretability
  • Bias-variance tradeoff

Random forest

  • Random forest
  • Ensemble methods
  • Hyperparameters

Final Portfolio Project

Refund Policy

  • Students may cancel up to 11 business days before the class/program start date and receive a refund, less the registration fee (10% of the undiscounted course price).
  • Cancellations within 11 business days are not permitted, but students may reschedule up to 4 business days before the class start date.

For cancellations and reschedules, please email us at [email protected]

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NYC Career Centers

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NYC Career Centers

NYC Career Centers

All classes at this location

We are located in New York City on 34th street and Madison Avenue, a few blocks from Penn Station, Port Authority and Times Square. You may also attend this class live online (virtual training) via Zoom. We will reach out with additional information, including the Zoom info and class files.

Also available virtually online

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