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Machine Learning with IBM Watson

  • Course Code: Artificial Intelligence - Machine Learning with IBM Watson
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning with IBM Watson skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • 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 

Audience & Pre-Requisites 

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. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills and Machine Learning knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to IBM Cloud 
  • Introduction to IBM Cloud 
  • 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  
  1. Feature Extraction – A Bag of Tricks 
  • Feature Extraction – A Bag of Tricks 
  • Preprocessing 
  • Dimensional reduction 
  • Data fusion 
  • A bag of tricks 
  1. Supervised Machine Learning Models for Your Data 
  • Supervised Machine Learning Models for Your Data 
  • Model selection 
  • Testing the model 
  • Classification 
  • Regression 
  • Testing the predictive capability 
  1. Implementing Unsupervised Algorithms 
  • Implementing Unsupervised Algorithms 
  • Unsupervised learning 
  • Semi-supervised learning 
  • Anomaly detection 
  • Online or batch learning 
  1. Machine Learning Workouts on IBM Cloud 
  • Machine Learning Workouts on IBM Cloud 
  • Watson Studio and Python 
  • Setting up the environment 
  • Data cleansing and preparation 
  • K-means clustering using Python 
  • K-nearest neighbors 
  • Time series prediction example 
  1. Using Spark with IBM Watson Studio 
  • Using Spark with IBM Watson Studio 
  • Introduction to Apache Spark 
  • Watson Studio and Spark 
  • Creating a Spark-enabled notebook 
  • Creating a Spark pipeline in Watson Studio 
  • Data preparation 
  • A data analysis and visualization example 
  1. Deep Learning Using TensorFlow on the IBM Cloud 
  • Deep Learning Using TensorFlow on the IBM Cloud 
  • Introduction to deep learning  
  • TensorFlow basics  
  • Neural networks and TensorFlow  
  • An example  
  • TensorFlow and image classifications 
  • Additional preparation 
  1. Creating a Facial Expression Platform on IBM Cloud 
  • Creating a Facial Expression Platform on IBM Cloud 
  • Understanding facial expression classification 
  • Exploring expression databases 
  • Preprocessing faces 
  • Preparing the environment 
  • Learning the expression classifier 
  1. The Automated Classification of Lithofacies Formation Using ML 
  • The Automated Classification of Lithofacies Formation Using ML 
  • Understanding lithofacies 
  • Exploring the data 
  • Training the classifier 
  • Evaluating the classifier 
  1. Building a Cloud-Based Multibiometric Identity Authentication Platform 
  • Building a Cloud-Based Multibiometric Identity Authentication Platform 
  • Understanding biometrics 
  • Exploring biometric data 
  • Feature extraction 
  • Multimodal fusion 
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