Recently we had conducted online Python training for IT staff at Philippines Security Bank.
Following topics covered at the training.
https://uditha.wordpress.com/2018/04/25/data-science-bootcamp-training-sri-lanka-singapore/
For Training Requirement Contact-
Recently I did 2 workshop on Game Development and Machine Learning at Sabaragamuwa University and SLIIT.
Recently I had conducted the workshop on Data Science and Machine Learning.Around 7 participants attended the workshop.
Topic covered at the workshop-
https://uditha.wordpress.com/2017/11/15/big-data-and-machine-learning-workshop-sri-lanka/
For Training Requirement Contact-
Recently we had conducted online Python training for Bank of America staff at Singapore.
Following topics covered at the training.
http://www.bluechiptraining.biz/python-programming-course-sri-lanka/
For Training Requirement Contact-
Sri Lanka
+94 0716092918
Singapore-
+65 86738158
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
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
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.
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
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
Data Science Academy [ Singapore , Hong Kong , Philippines ] 