This module provides a comprehensive overview of AI and ML principles, applications, and techniques before the students dive into the CertNexus CAIP program, whiich follows in the same module. CAIP as part of this module ensures that candidates are well-prepared to implement AI solutions in various professional environments. The CAIP-210 certification is designed for data professionals who can leverage AI and machine learning to solve business challenges. Certified AI Practitioners (CAIPs) are skilled in automating processes, reducing costs, and enhancing operational efficiency. They possess advanced knowledge in engineering datasets, selecting algorithms, training models, and ensuring ethical outcomes in AI applications.

Content

1. Understanding the Artificial Intelligence Problem
   • Describe AI and ML applications in business.
   • Analyze ML algorithm use cases.
   • Research learning systems.
   • Communicate with stakeholders.
   • Identify ethical concerns.
2. Engineering Features for Machine Learning
   • Impact of data quality and size on algorithms.
   • Data collection/transformation processes.
   • Work with various data formats.
   • Transform numerical and categorical data.
   • Address business risks and ethical concerns.
3. Training and Tuning ML Systems and Models
   • Design machine and deep learning models.
   • Optimize algorithms.
   • Train, validate, and test data subsets.
   • Evaluate models.
   • Address ethical concerns in training and tuning.
4. Operationalizing ML Models
   • Deploy models.
   • Secure pipelines.
   • Maintain models post-production.
   • Address business risks and ethical concerns.

Hands-on project module, where students will develop practical solutions using machine learning and deep learning techniques. This immersive experience blends theoretical knowledge with real-world applications, all under the mentorship of seasoned AI professionals.

This module aims to provide a comprehensive understanding of transformer models and their applications in Natural Language Processing (NLP) and Computer Vision. Students will explore the architecture, training, and fine-tuning of transformers, including BERT, GPT, RoBERTa, and T5. The module will cover practical implementations using Hugging Face, Google Trax, and OpenAI, focusing on tasks such as machine translation, text summarization, semantic role labeling, and image generation. By the end of the module, students will be equipped with the knowledge to select, train, and deploy transformer models for various AI projects, leveraging state-of-the-art techniques and tools.

Hands-on project module, where students will develop practical solutions using machine learning and deep learning techniques. This immersive experience blends theoretical knowledge with real-world applications, all under the mentorship of seasoned AI professionals.

The final project module is the module where students will apply their comprehensive knowledge based on the whole AI journey through the Academy, to develop innovative solutions using advanced machine learning and deep learning techniques. This capstone experience integrates theoretical insights with practical applications, guided by expert AI mentors, to prepare students for real-world challenges.