4 Topics
5 Days
12 Sessions
Programme Overview
Training Description
Systems Engineers/Administrators
IT Operations (ITOps) Professionals
Network Engineers/Administrators
DevOps/SRE (Site Reliability Engineers)
Cloud Architects/Engineers
IT Managers and CIOs
Cybersecurity Professionals
Session Objectives
- Understand the fundamentals of AI and machine learning. Build and deploy practical AI/ML models for IT applications. Automate IT tasks using AI/ML techniques. Utilize AI/ML for predictive analytics and anomaly detection. Integrate AI/ML with existing IT systems and tools. Understand the ethical considerations of AI/ML in IT. Apply AI/ML for network monitoring and security. Utilize AI/ML for IT service management and automation. Develop strategies for implementing AI/ML in IT environments. Understand the importance of data management for AI/ML. Enhance their ability to leverage AI/ML for IT automation and innova
About the Course
Artificial Intelligence (AI) and Machine Learning (ML) are transforming IT operations, enabling automation, predictive analytics, and intelligent decision-making. This course on AI & Machine Learning for IT Professionals equips participants with the specialized knowledge and skills to leverage AI/ML for practical applications and automation within IT environments. Participants will learn how to build and deploy AI/ML models, integrate them with existing systems, and utilize them to enhance IT efficiency and security. This course bridges the gap between theoretical AI/ML concepts and real-world IT applications, empowering professionals to drive innovation and automation.
Curriculum & Topics
4 Topics | 12 Sessions
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Workshop 1.1:
Introduction to AI & ML: Define AI, ML, Deep Learning, and their real-world applications.
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Workshop 1.2:
Python Essentials for Data: Cover core Python, numpy, and Pandas for data manipulation.
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Workshop 1.3:
Data Preprocessing: Techniques for cleaning, transforming, and preparing data for model training.
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Workshop 2.1:
Supervised Learning (Regression & Classification): Understand algorithms like Linear Regression, Decision Trees, and Logistic Regression.
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Workshop 2.2:
Unsupervised Learning (Clustering): Explore K-Means and Hierarchical Clustering for pattern recognition in unlabeled data.
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Workshop 2.3:
Model Evaluation: Learn to use metrics (e.g., accuracy, precision, recall) to assess model performance.
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Workshop 3.1:
Neural Network Architecture: Grasp the concept of neurons, layers, and the backpropagation training process.
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Workshop 3.2:
Deep Learning Frameworks: Introduction to working with tensorflow and pytorch.
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Workshop 3.3:
Computer Vision (cnns): Overview of Convolutional Neural Networks for image recognition tasks.
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Workshop 4.1:
ML in the Cloud: Utilizing cloud platforms (like Azure or AWS) for scalable ML workflows.
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Workshop 4.2:
Model Deployment: Techniques for packaging and serving trained models in a production environment.
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Workshop 4.3:
Mlops and Monitoring: Introduction to best practices for managing, versioning, and monitoring models in production to prevent drift.
