Machine Learning Specialist – Supervised Learning: Regression and Classification – W7139G

Course Name:

Machine Learning Specialist – Supervised Learning: Regression and Classification

W7139G

Skill Level:

Intermediate

Modality:

SPVC – Self-Paced Virtual Training

Duration:

2.75 Day/s

Price:
Request Quote

Overview:

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression and Classification.

This course introduces you to two of the main types of modelling families of supervised Machine Learning: Regression and Classification. You start by learning how to train regression models to predict continuous outcomes and how to use error metrics to compare aCR – Classroom Trainingoss different models. You then learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare aCR – Classroom Trainingoss different models. This course also walks you through best practices, including train and test splits, and regularization techniques. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.

 

IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise SubsCR – Classroom Trainingiptions:

  • IBM Learning for Data and AI Individual SubsCR – Classroom Trainingiption (SUBR022G)
  • IBM Learning for Data and AI Enterprise SubsCR – Classroom Trainingiption (SUBR004G)
  • IBM Learning Individual SubsCR – Classroom Trainingiption with Red Hat Learning Services (SUBR023G)


Enroll here

Please enable JavaScript in your browser to complete this form.
Email
Multiple Choice
How did you hear about us?
Yes, I would like to receive special offers from CRS.
Yes, I would like to receive special offers from CRS.


Target Audience:

This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression and Classification techniques in a business setting.

[List]

Prerequisites:

To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Topic:

1. Introduction to Supervised Machine Learning and Linear Regression

2. Data Splits and CR – Classroom Trainingoss Validation

3. Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net

4. Logistic Regression

5. K Nearest Neighbors

6. Support Vector Machines

7. Decision Trees

8. Ensemble Models

9. Modeling Unbalanced Classes

 

Objective:

By the end of this course you should be able to:

  • Differentiate uses and applications of classification and regression in the context of supervised machine learning. 
  • DesCR – Classroom Trainingibe and use linear regression models, and use decision tree and tree-ensemble models. 
  • Use a variety of error metrics to compare and select a linear regression model or classification model that best suits your data. 
  • Articulate why regularization might help prevent overfitting. 
  • Use regularization regressions: Ridge, LASSO, and Elastic net. 
  • Use oversampling as techniques to handle unbalanced classes in a data set.

Remarks:

Category:

Data and AI

Product Name:

Non-Product Education

Badge and Certification Info:

0

Brand: 

Watson AI

Follow on Courses:

 

 

Replaced By:

ml;ai;ai ladder

Non-IRLP / Soleil Service Unit (24)

Lab Access Duration:

 

CRS is the top Global Training Provider for some of the world’s biggest brands.

Call Now +27 12 023 1959