IBM AI Enterprise Workflow Data Science Specialist Training
In this program, we decided to take a different approach than traditional product certifications, and instead of building a product-centric certification,
we decided to build a process-centric certification with specific guiding principles:
1. Using a single use case/real world scenario as the foundation to work through what it takes to build an end to end AI solution
2. Leveraging Design thinking as a framework to work through the translation of business goals into AI technical implementations
3. Bringing together different capabilities such as Machine Learning, Optimization, and specific narrow-AI functionality
4. Leveraging python as the tool of choice for building AI models, and bringing in Watson Studio where it adds value on top of Python and other open source tools
Section 1: Scientific, Mathematical, and technical essentials for Data Science and AI
· Explain the difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics
· Describe and explain the key terms in the field of artificial intelligence (Analytics, Data Science, Machine Learning, Deep Learning, Artificial Intelligence etc.)
· Distinguish different streams of work within Data Science and AI (Data Engineering, Data Science, Data Stewardship, Data Visualization etc.)
· Describe the key stages of a machine learning pipeline.
· Explain the fundamental terms and concepts of design thinking
· Explain the different types of fundamental Data Science
· Distinguish and leverage key Open Source and IBM tools and technologies that can be used by a Data Scientist to implement AI solutions
Section 2: Applications of Data Science and AI in Business
· Identify use cases where artificial intelligence solutions can address business opportunities
· Translate business opportunities into a machine learning scenario
· Differentiate the categories of machine learning algorithms and the scenarios where they can be used
· Show knowledge of how to communicate technical results to business stakeholders
· Demonstrate knowledge of scenarios for application of machine learning
Section 3: Data understanding techniques in Data Science and AI
· Demonstrate knowledge of data collection practices
· Explain characteristics of different data types
· Show knowledge of data exploration techniques and data anomaly detection
· Use data summarization and visualization techniques to find relevant insight
Section 4: Data preparation techniques in Data Science and AI
· Demonstrate expertise cleaning data and addressing data anomalies
· Show knowledge of feature engineering and dimensionality reduction techniques
· Demonstrate mastery preparing and cleaning unstructured text data
Section 5: Application of Data Science and AI techniques and models
· Explain machine learning algorithms and the theoretical basis behind them
· Demonstrate practical experience building machine learning models and using different machine learning algorithms
Section 6: Evaluation of AI models
· Identify different evaluation metrics for machine learning algorithms and how to use them in the evaluation of model performance
· Demonstrate successful application of model validation and selection methods
· Show mastery of model results interpretation
· Apply techniques for fine tuning and parameter optimization
Section 7: Deployment of AI models
· Describe the key considerations when selecting a platform for AI model deployment
· Demonstrate knowledge of requirements for model monitoring, management and maintenance
· Identify IBM technology capabilities for building, deploying, and managing AI models
Section 8: Technology Stack for Data Science and AI
· Describe the differences between traditional programming and machine learning
· Demonstrate foundational knowledge of using python as a tool for building AI solutions
· Show knowledge of the benefits of cloud computing for building and deploying AI models
· Show knowledge of data storage alternatives
· Demonstrate knowledge on open source technologies for deployment of AI solutions
· Demonstrate basic understanding of natural language processing
· Demonstrate basic understanding of computer vision
· Demonstrate basic understanding of IBM Watson AI services
For Training Requirement Contact-
Sri Lanka
+94 0716092918
Singapore-
+65 86738158
MATLAB FUNDAMENTALS Course.
This course provides a comprehensive introduction to the MATLAB® technical computing environment.
No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.
You will learn:
• How to use Matlab to analyze data and report results
• How to load and save information using several file formats including text, binary and Excel
• How to automate analyses using basic programming and built-in functions
• How to create robust, reusable and maintainable code
• To design and present data in graphs and GUI windows
• The basic skills that will enable you to continue learning advanced topics by yourself, at your own pace
1. The Matlab environment
· Learn how to use the Matlab desktop
· The Matlab workspace and command window
· Using the command history
· Using the documentation system and other online resources
2. Using numeric and character-based data
· Matlab’s data types and precisions
· Matlab’s data storage types
· Creating and manipulating data
· Operators and expressions
· Using functions
· Accessing sub-elements and data ranges
3. Matlab Programming
· Creating your first Matlab script
· The Matlab editor
· Using the debugger
· Scripts vs. functions
· Controlling program control flow
· Coding conventions and best practices
4. Analyzing data
· Removing and fixing invalid data
· Fitting data
· Error handling
5. Saving and loading data
· To/from Matlab workspace
· To/from text or binary files
· To/from Excel
· To/from a webpage
6. Visualizing data
· Displaying results in the command window
· Plotting data in 2D and 3D graphs
· Presenting data tables
· Exporting graphics to external applications
For Training Requirement Contact-
Sri Lanka
+94 0716092918
Singapore-
+65 86738158
AI for Business Leaders Workshop Sri Lanka.
Recently I did AI for Business Leaders Workshop at Orel IT. I covered following topics during the workshop.
AI based business opportunities
AI development life cycle
Cloud based AI technologies
AI Use Cases
For Training Requirement Contact-
Sri Lanka
+94 0716092918
Singapore-
+65 86738158
Online Healthcare Machine Learning workshop at Melbourne.
Recently we had conducted online Machine Learning workshop for St John of God Health Care Doctors at Melbourne.
Following topics covered at the training.
Introduction to Data Science
Introduction to Machine Learning
Machine Learning in Healthcare
Healthcare research in Machine Learning.
Sri Lanka DevOps Community Online Meetup.
Machine Learning Operations (MLOps) is based on DevOps principles and practices that increase the efficiency of workflows. For example, continuous integration, delivery, and deployment.
MLOps applies these principles to the machine learning process, with the goal of:
• Faster experimentation and development of models
• Faster deployment of models into production
• Quality assurance
Agenda –
• Azure Machine Learning
• ML Pipeline
• Azure DevOps Integration
Introduction to Machine Learning Workshop .
https://www.meetup.com/Colombo-AI-Technology-Meetup/events/271734830/
Register URL – https://bit.ly/3fdAK56
Contact us at +94 (071) 6092918
udithait@gmail.com
training@bluechiptraining.biz
Conducted By-
Uditha Bandara (MVP) is specializes in Data Science, Mobile App and Blockchain technologies. He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional).
He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Indonesia, Sri Lanka and India.
He has published several books,articles, tutorials, and demos on his Blog – https://uditha.wordpress.com
Data Science Solution on Azure Online Singapore Training.
Recently I had conducted Azure Data Science solution online training. Following topics covered at the training.
Module 1: Introduction to Azure Machine Learning
Module 2: No-Code Machine Learning with Designer
Module 3: Running Experiments and Training Models
Module 4: Working with Data
Module 5: Compute Contexts
Module 6: Orchestrating Operations with Pipelines
Module 7: Deploying and Consuming Models
Module 8: Training Optimal Models
Module 9: Interpreting Models
Module 10: Monitoring Models
Around 20 participants attended the training.
For Training Requirement Contact-
Sri Lanka
+94 0716092918
Singapore-
+65 86738158
Cloud Machine Learning Workshop Sri Lanka.
https://www.meetup.com/Colombo-AI-Technology-Meetup/events/270402228/
Register URL – https://bit.ly/3fdAK56
Contact us at +94 (071) 6092918
Feel free to contact us for any inquiries
Uditha Bandara (MVP) is specializes in Data Science, Mobile App and Blockchain technologies. He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional).
He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Indonesia, Sri Lanka and India. He has published several books,articles,
tutorials, and demos on his Blog – https://uditha.wordpress.com
Data Science and Machine Learning March 2020 Workshop Sri Lanka.
Few weeks ago I had conducted the workshop on Data Science and Machine Learning.
Around 12 attended the workshop.Employees from Stretch line,Enso,Sri Lanka Insurance (SLIC),Sri Lanka Tourism Development Authority
and Attune came to the event.
Topic covered at the workshop-
https://uditha.wordpress.com/2017/11/15/big-data-and-machine-learning-workshop-sri-lanka/
For Training Requirement Contact-
Machine learning and AI Bootcamp training at Colombo , Sri Lanka.
Recently I did Data Science and Machine learning Bootcamp training at Sri Lanka.
This 3 days training was focus on getting started with Data Science technologies. Azure machine learning studio, R studio, Jupyter Notebook ,
Spyder with Python for data science. It also includes real world usage of machine learning for regression, classification and product recommendations.
Course Outline –
Introduction to Machine Learning
This module introduces machine learning and discussed how
algorithms and languages are used.
Lessons
· What is machine learning?
· Introduction to machine learning algorithms
· Introduction to machine learning languages
Introduction to Azure Machine Learning
Describe the purpose of Azure Machine Learning, and list the
main features of Azure Machine Learning Studio.
Lessons
· Azure machine learning overview
· Introduction to Azure machine learning studio
· Developing and hosting Azure machine learning applications
Managing Datasets
At the end of this module the student will be able to explore
various types of data in Azure machine learning.
Lessons
· Categorizing your data
· Importing data to Azure machine learning
· Exploring and transforming data in Azure machine learning
Building Azure Machine Learning Models
This module describes how to use regression algorithms and
neural networks with Azure machine learning.
Lessons
· Azure machine learning workflows
· Using regression algorithms
· Using neural networks
Using Azure Machine Learning Models
This module explores how to provide end users with Azure
machine learning services, and how to share data generated
from Azure machine learning models. Lessons
· Deploying and publishing models
· Consuming Experiments
Introduction to Python
• Python History
• Installing Python
• Installing IDE
Data types
• Numbers
• Sequences
• File
• Tuples
• Dictionaries
Data Science Intro
• Why Python for Data Science
• Popular packages
• Use cases
• Popular Libraries
• Panda
• Numpy
• Matplotlib
• Scikit-learn
Working with data
• Reading & Writing to different data sources
• Cleaning data
• Visualization
• Data Transformation
Introduction to Deep Learning
· Deep learning basics
· Deep learning tools
· Introduction to neural network
· CNN, ANN, RNN neural network
Introduction to Tensor Flow
· Installing Tensor Flow
· Tensor Flow basics
· Basic Neural Networks using tenser flow
· Advanced Neural Networks using tenser flow (CNN, RNN, ANN)
24 software engineers attended 3 Days of training.
For Training Requirement Contact-
Data Science and Machine Learning Workshop Sri Lanka 2020.
https://www.meetup.com/Colombo-AI-Technology-Meetup/events/268774212/
Register URL – https://bit.ly/2NirW2l
Feel free to contact us for any inquiries
uditha bandara – 0716092918
Uditha Bandara (MVP) is specializes in Data Science, Mobile App and Blockchain technologies.
He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional).
He had delivered sessions at various events and conferences in Hong Kong, Malaysia,
Singapore, Indonesia, Sri Lanka and India. He has published several books,articles,
tutorials, and demos on his Blog – https://uditha.wordpress.com