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Python Machine Learning Immersive

Master the essential skills to excel in the booming field of machine learning, including regression and classification algorithms, feature selection, and model evaluation. Join us to gain the practical knowledge needed to tackle real-world problems and make a significant impact in the industry.

  • Level All levels
  • Age 13 and older
  • Price $1,895
  • Location Interactive Online Classroom

Start Dates (6)

  • $1,895
  • Interactive Online Classroom
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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
  • Wed, Jul 10 at 6:00pm - 9:00pm
  • 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
  • Sat, Jun 29 at 10:00am - 5:00pm
  • Sat, Jul 13 at 10:00am - 5:00pm
  • Sat, Jul 20 at 10:00am - 5:00pm
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  • Mon, Jul 08 at 10:00am - 5:00pm
  • Tue, Jul 09 at 10:00am - 5:00pm
  • Wed, Jul 10 at 10:00am - 5:00pm
  • 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
  • Wed, Sep 11 at 10:00am - 5:00pm
  • 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


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 as 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.

What You’ll Learn:

  • How to clean and balance your data using the Pandas library
  • Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
  • Choosing good features to use as input for your algorithms
  • Properly splitting data into training, test and cross-validation sets
  • Important theoretical concepts like overfitting, variance and bias
  • Evaluating the performance of your machine learning models

Learn more about Python Machine Learning Immersive at Practical Programming.

Remote Learning

This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.

Upon registration, the instructor will send along additional information about how to log-on and participate in the class.

Also available in-person in NYC



Basic Regression Analysis

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

Advanced Regression Analysis

  • Multi-linear regression
  • Feature engineering
  • Overfitting


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

To reschedule or cancel, email us at [email protected]

All courses include a non-refundable registration fee (10% of the undiscounted course price).

  • Students may cancel up to 11 business days before the class/program start date and receive a refund, less the registration fee.
  • Cancellations within 11 business days are not permitted; however, students may reschedule up to 4 business days before the class start date.

Note: Any refunds must be requested within 180 days from the original payment date; courses rescheduled within 11 business days of the start date are not eligible for refunds.

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