AI Training

Byhoussem.ba

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KOSMOS Technologies

Process of Training And Development

Keys Of Training Principles

Progressive Learning

Structure the course content to build from basic concepts to advanced techniques, ensuring a gradual and comprehensive understanding.

Hands-On Practice

Incorporate practical exercises and real-world projects to reinforce theoretical knowledge and develop practical skills.

Relevance to industry

Use case studies and examples from the green technology sector to illustrate the application of AI and machine learning in solving real-world problems.

Continuous Feedback

Provide regular assessments, quizzes, and feedback sessions to monitor progress and address areas for improvement.

Ethical & Sustainable Practices

Emphasize the importance of ethical AI development and
sustainable practices, promoting responsible use of technology to
address environmental challenges.

Levels Of Training

Level 1: Introduction to ML & Deep learning

Have a foundational understanding of machine learning (ML) and deep learning (DL) by covering essential concepts, algorithms, and techniques, with an emphasis on Python in Data-science & ML. Learn data preprocessing, basic supervised and unsupervised learning methods, and the fundamentals of neural networks.

Level 2: Development of ML/DL models for industrial usage

Focus on the practical development and fine-tuning of ML and DL models for industrial applications by exploring advanced algorithms, hyperparameter tuning, and model optimization techniques. Go more further in applying hands-on projects & case studies for building advanced knowledge.

Level 3: Deploying & Scaling advanced AI solutions

Dedicate a deep understanding of deploying and scaling AI solutions in a production environment by using scalable AI architectures, cloud platforms, containerization, and continuous integration/continuous deployment (CI/CD) practices. Learn how to optimize performance, ensure security, and maintain AI models, with a focus on sustainable and ethical AI practices.

Level 1. Water Quality Prediction

Application

Build a basic machine learning model to predict water quality based on various environmental factors to verify if it's consumable by human or no.

Implementation

Have the essential background in EDA analysis by understanding the underlying structure of a dataset. Then test several regression algorithms to measure metrics and test their performances.

Level 2. Plant Diseases Detection

Application

Develop a machine learning model to identify and classify plant diseases from images of plant leaves.

Implementation

Start by preprocessing a dataset of labeled leaf images in Python. Build and train a Convolutional Neural Network (CNN) using PyTorch for classification. Evaluate the model’s accuracy, then do predictions on some examples.

Level 3. Enhanced Plant Disease Detection

Application

Learn how to deploy The plant disease prediction model in real-time applications by integrating cloud concepts for streaming and scalable solution.

Implementation

Implement the developed model inside a containerized applications that is streamed in cloud to serve as API use case for end-users.

Career Impact

Enhanced Skill Set

• Advanced Expertise: Gain proficiency in cutting-edge AI and machine learning techniques, from foundational models to complex deployment strategies.
• Versatile skills: Develop skills applicable across various domains, including data science, green technology, and industrial applications.

Increased Marketability

• Competitive edge: Improve your attractiveness by demonstrating expertise in both practical AI applications and advanced deployment strategies.
• Broader opportunities: Access a wider range of job roles, from AI research and development to implementation and management in diverse industries.

Career advancement

• Leadership: Positioning for higher-level roles such as AI architect, data scientist, or ML engineer, with an understanding of both technical & strategic aspects.
• Professional Growth: Enhance career prospects through the ability to lead complex projects and contribute to significant advancements in technology.

AI Training

Process of Training And Development

Keys Of Training Principles

Progressive Learning

Hands-On Practice

Relevance to industry

Continuous Feedback

Ethical & Sustainable Practices

Levels Of Training

Level 1: Introduction to ML & Deep learning

Level 2: Development of ML/DL models for industrial usage

Level 3: Deploying & Scaling advanced AI solutions

Level 1. Water Quality Prediction

Application

Implementation

Level 2. Plant Diseases Detection

Application

Implementation

Level 3. Enhanced Plant Disease Detection

Application

Implementation

Career Impact

Enhanced Skill Set

Increased Marketability

Career advancement

Explore

Useful Links

Contact Info

AI Training

AI Training

Process of Training And Development

Keys Of Training Principles

Progressive Learning

Hands-On Practice

Relevance to industry

Continuous Feedback

Ethical & Sustainable Practices

Levels Of Training

Level 1: Introduction to ML & Deep learning

Level 2: Development of ML/DL models for industrial usage

Level 3: Deploying & Scaling advanced AI solutions

Level 1. Water Quality Prediction

Application

Implementation

Level 2. Plant Diseases Detection

Application

Implementation

Level 3. Enhanced Plant Disease Detection

Application

Implementation

Career Impact

Enhanced Skill Set

Increased Marketability

Career advancement

Explore

Useful Links

Contact Info

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