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From fundamental concepts to advanced algorithms, our
AI training programs equip you with the skills to harness the power of Artificial Intelligence.

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Artificial Intelligence

Training Programs

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Program Overview

This program is designed to provide trainees with a solid foundation in Machine Learning principles, mathematics, programming (Python), and practical experience, preparing them for further studies in Deep Learning & GenAI.

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Training Objectives

Upon completion of this training program, participants will be able to:

  • Understand the core concepts and principles of Machine Learning.

  • Apply fundamental mathematical concepts required for understanding ML algorithms. (Linear Algebra, Calculus, Probability, Statistics)

  • Proficiently use Python and relevant libraries (NumPy, Pandas, Scikit-learn) for implementing ML algorithms.

  • Implement, evaluate, and compare various supervised and unsupervised learning algorithms.

  • Apply best practices for data preprocessing, feature engineering, model selection, and hyperparameter tuning.

  • Develop practical Machine Learning models for real-world problems.

  • Evaluate the performance of ML models using appropriate metrics.

  • Understand the limitations of Machine Learning and its appropriate use cases.

  • Build a strong foundation for transitioning to Deep Learning concepts and applications.

Program Outcomes​​

By the end of the program, learners will:

  • Explain the different types of Machine Learning problems and their applications. (Regression, Classification, Clustering)

  • Describe the mathematical foundations underlying common ML algorithms.

  • Write Python code to implement and train ML models using Scikit-learn.

  • Prepare and preprocess data for Machine Learning tasks. (Cleaning, Transformation, Feature Scaling)

  • Select appropriate evaluation metrics for different ML problems.

  • Tune hyperparameters to optimize model performance.

  • Build and deploy basic ML models for practical applications.

  • Communicate the results of ML analysis effectively.

  • Demonstrate an understanding of the strengths and weaknesses of different ML algorithms.

  • Possess the necessary knowledge and skills to confidently progress to Deep Learning courses.

Program Overview

This program is designed to provide trainees with a comprehensive understanding of Deep Learning concepts, architectures, and applications, building on their existing Machine Learning foundation. This program will enable them to design, train, and deploy deep learning models for real-world problems.

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Training Objectives

Upon completion of this training program, participants will be able to:

  • Understand the fundamental concepts and principles of Deep Learning.

  • Explain the architecture and functionality of various deep learning models. (e.g., CNNs, RNNs, LSTMs, Transformers, Autoencoders)

  • Proficiently use deep learning frameworks (TensorFlow, Keras, or PyTorch).

  • Apply best practices for training deep learning models, including optimization, regularization, and hyperparameter tuning.

  • Build and train deep learning models for various tasks, including image recognition, natural language processing, and time series analysis.

  • Evaluate the performance of deep learning models using appropriate metrics.

  • Understand the challenges and limitations of deep learning and its ethical implications.

  • Deploy trained deep learning models for real-world applications.

  • Stay current with the latest advancements in deep learning research.

Program Outcomes​​

By the end of the program, learners will:

  • Describe the differences between Machine Learning and Deep Learning.

  • Explain the concept of neural networks and their building blocks (neurons, layers, activation functions).

  • Implement and train various deep learning models using TensorFlow, Keras, or PyTorch.

  • Preprocess data specifically for deep learning tasks.

  • Select appropriate deep-learning architectures for different problems.

  • Tune hyperparameters to optimize model performance.

  • Diagnose and address common problems encountered during deep learning training (e.g., overfitting, vanishing gradients).

  • Deploy trained deep learning models using frameworks like TensorFlow Serving or Flask.

  • Contribute to deep learning projects and research.

  • Critically evaluate the performance and limitations of deep learning models.

Program Overview

This program is designed to provide trainees with in-depth knowledge and practical skills in Generative AI, enabling them to understand, implement, and deploy generative models, including LLMs, LVMs, and SLMs, while adhering to MLOps principles and exploring the creation of AI agents.

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Training Objectives

Upon completion of this training program, participants will be able to:

  • Understand the fundamental concepts and principles of Generative AI.

  • Explain the architecture and functionality of various generative models. (GANs, VAEs, Diffusion Models, Transformers)

  • Describe the key components and architectures of Large Language Models (LLMs), Large Vision Models (LVMs), and Small Language Models (SLMs).

  • Apply best practices for fine-tuning, prompting, and evaluating generative models.

  • Use MLOps principles and tools to manage the lifecycle of generative AI models.

  • Design and implement AI agents that leverage generative models for various tasks.

  • Understand the ethical considerations and responsible development of generative AI.

  • Stay current with the latest advancements in generative AI research.

Program Outcomes​​

By the end of the program, learners will:

  • Differentiate between various generative modeling techniques.

  • Fine-tune pre-trained LLMs for specific tasks using techniques like parameter-efficient fine-tuning (PEFT).

  • Develop and evaluate prompts for LLMs to elicit desired outputs.

  • Integrate LLMs and LVMs to build multimodal AI systems.

  • Design and implement MLOps pipelines for generative AI models, including model versioning, deployment, and monitoring.

  • Build AI agents that can interact with users and perform tasks using generative models.

  • Critically evaluate the outputs of generative models and identify potential biases or limitations.

  • Contribute to generative AI projects and research.

  • Apply ethical principles to the development and deployment of generative AI systems.

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