Data Science Training in Gaia

This instructor-led, live training in Gaia (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

By the end of this training, participants will be able to:

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in Gaia (online or onsite) targets intermediate-level data scientists and analysts who wish to use AWS Cloud9 to streamline their data science workflows.

Upon completing this training, participants will be able to:

• Establish a data science environment in AWS Cloud9.
• Conduct data analysis using Python, R, and Jupyter Notebook within Cloud9.
• Integrate AWS Cloud9 with AWS data services such as S3, RDS, and Redshift.
• Utilize AWS Cloud9 for developing and deploying machine learning models.
• Optimize cloud-based workflows for efficient data analysis and processing.

This instructor-led live training in Gaia (online or onsite) is designed for intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

Upon completion of this training, participants will be capable of:

• Configuring Apache Airflow to orchestrate machine learning workflows.
• Automating tasks related to data preprocessing, model training, and validation.
• Integrating Airflow with various machine learning frameworks and tools.
• Deploying machine learning models through automated pipelines.
• Monitoring and optimising machine learning workflows in production environments.

This instructor-led live training in Gaia (online or onsite) aims to support participants in their preparation for the Microsoft Certified: Data Scientist Associate certification exam (DP-100).

The program explores the core concepts of data science and machine learning, along with their application using Microsoft Azure Machine Learning. Participants will gain proficiency in preparing data, developing and training machine learning models, and deploying them to cloud environments. The course also examines methods for evaluating model performance and performing feature engineering.

This trainer-led, live training in Gaia (online or in-person) is designed for beginner-level data scientists and IT professionals who wish to learn the fundamentals of data science using Google Colab.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

This instructor-led, live training in Gaia (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.