Learn the Basics of Machine Learning with IBM Watson Studio – W7L160G

Course Name:

Learn the Basics of Machine Learning with IBM Watson Studio

W7L160G

Skill Level:

Intermediate

Modality:

ILO – Instructor Lead Online Training

Duration:

0.75 Day/s

Price:
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Overview:

This course introduces a case study, data set, machine learning concepts, and developing a machine learning model with Watson Studio.

This course introduces a case study, data set, machine learning concepts, and developing a machine learning model with Watson Studio.

Initially, you will be introduced to the case study and the challenges company facing, and the company data set. Next, you will be introduced to supervised, unsupervised learning, deep and reinforcement learning algorithms. Finally, you will develop a supervised machine learning model IBM Watson Studio with the dataset provided using Python.


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Target Audience:

AI Specialists who want to learn machine learning algorithms

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Prerequisites:

  • Some experience in Python 
  • Some experience in Jupyter notebook 
  • Some experience in Watson Studio or completion of Watson Studio Primer
  • A Watson Studio Lite plan

Topic:

  • Introduction to the case study and the data set
  • Introduction to Machine Learning
  • Developing the model

Objective:

  • DesCR – Classroom Trainingibe the use case and the data set
  • Distinguish between supervised and unsupervised machine learning
  • Define deep learning and reinforcement learning
  • Demonstrate the basic functions of Watson Studio for machine learning

Remarks:

Category:

Data and AI

Product Name:

IBM Watson Studio Premium for IBM Cloud Pak for Data

Badge and Certification Info:

acd65123-99a5-431a-8a17-a1baf7d4f6b1

Learn the Basics of Machine Learning with IBM Watson Studio – Code: W7L160G

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Brand: 

Cloud Pak for Data

Follow on Courses:

 

 

Replaced By:

This is a new course that provides a hands-on model development experience.

W7L160;data science; models; learning algorithms; python

SPVC Public Cloud hosting for a fee (26)

Lab Access Duration:

 

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