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Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with basic IT, Machine Learning, and Linux skills

Who should attend & recommended skills

  • This course is geared for attendees with basic Linux and computing skills who wish to learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services.
  • Skill-level: Foundation-level Machine Learning with IBM Watson skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Machine Learning: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This course is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You’ll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The course will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later lessons, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this course, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning with IBM Watson instructor, students will learn about and explore:
  • Implement data science and machine learning techniques to draw insights from real-world data
  • Understand what IBM Cloud platform can help you to implement cognitive insights within applications
  • Understand the role of data representation and feature extraction in any machine learning system
  • Understand key characteristics of IBM machine learning services
  • Run supervised and unsupervised techniques in the cloud
  • Understand how to create a Spark pipeline in Watson Studio
  • Implement deep learning and neural networks on the IBM Cloud with TensorFlow
  • Create a complete, cloud-based facial expression classification solution
  • Use biometric traits to build a cloud-based human identification system

Course breakdown / modules

  • Understanding IBM Cloud
  • Accessing the IBM Cloud
  • Cloud resources
  • The IBM Cloud and Watson Machine Learning services
  • Setting up the environment
  • Watson Studio Cloud
  • Setting up a new project
  • Data visualization tutorial

  • Preprocessing
  • Dimensional reduction
  • Data fusion
  • A bag of tricks

  • Model selection
  • Testing the model
  • Classification
  • Regression
  • Testing the predictive capability

  • Unsupervised learning
  • Semi-supervised learning
  • Anomaly detection
  • Online or batch learning

  • Watson Studio and Python
  • Setting up the environment
  • Data cleansing and preparation
  • K-means clustering using Python
  • K-nearest neighbors
  • Time series prediction example

  • Introduction to Apache Spark
  • Watson Studio and Spark
  • Creating a Spark-enabled notebook