Python Machine Learning Immersive

at Practical Programming - Midtown

(378)
Course Details
Price:
$1,895 30 seats left
Start Date:

Sat, Nov 12, 10:00am - Dec 17, 5:00pm Eastern Time (5 sessions)

Next start dates (1)

Location:
Midtown, Manhattan
185 Madison Ave 3rd Fl
34th & Madison
New York, New York 10016
(Map)
Important:
No classes on Nov 26
Purchase Options
Description
Class Level: All levels
Age Requirements: 13 and older
Average Class Size: 5

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:

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.

Prerequisite

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 Immersive 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

Course 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


Still have questions? Ask the community.

Refund Policy
If you withdraw two days before the course start date, any deposit paid will be refunded in full. 

No refunds will be given for cancellations made after that date. If you cannot attend our class or workshop, for which you were charged, you will receive 25% discount for any other course.

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Practical Programming

All classes at this location

Start Dates (2)
Start Date Time Teacher # Sessions Price
10:00am - 5:00pm Eastern Time TBD 5 $1,895
This course consists of multiple sessions, view schedule for sessions.
Sat, Nov 19 10:00am - 5:00pm Eastern Time TBD
Sat, Dec 03 10:00am - 5:00pm Eastern Time TBD
Sat, Dec 10 10:00am - 5:00pm Eastern Time TBD
Sat, Dec 17 10:00am - 5:00pm Eastern Time TBD
10:00am - 5:00pm Eastern Time TBD 5 $1,895
This course consists of multiple sessions, view schedule for sessions.
Tue, Nov 15 10:00am - 5:00pm Eastern Time TBD
Wed, Nov 16 10:00am - 5:00pm Eastern Time TBD
Thu, Nov 17 10:00am - 5:00pm Eastern Time TBD
Fri, Nov 18 10:00am - 5:00pm Eastern Time TBD

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