Artificial Intelligence for Business, Decoded
From understanding to implementation — a practical, application-level program for professionals

Course Overview

Module 1: Introduction to AI & AI Evolution

1.  Overview of AI & Industry Use Cases

• Definition of AI, ML, Deep Learning, and Generative AI
• AI applications in different industries (Healthcare, Finance, Manufacturing, etc.)
• The role of AI in modern enterprise operations

2.  Evolution of AI

• AI history and major breakthroughs
• Transition from rule-based AI to machine learning
• Deep learning and its impact on AI models

3.  Generative AI & Emerging Trends

• Introduction to Generative AI
• Use cases: Image generation, Chatbots, Music synthesis, Video creation
• Ethical considerations in AI-generated content

4.  Role of GPUs in AI Computing

• Why GPUs are preferred for AI workloads
• CUDA architecture and Tensor Cores
• Hardware accelerators vs. CPUs for AI

5.  AI Software Stack

• Overview of AI software stacks (TensorFlow, PyTorch, NVIDIA TensorRT)
• Importance of optimizing software and hardware together
• AI workloads in cloud and on-premises environments

6.  Hands-on Lab

• Setting up an AI development environment with GPU support
• Running a basic deep learning model using TensorFlow/PyTorch

Module 2: AI Infrastructure & Compute Platforms

1.  Hands-on Lab

• Introduction to NVIDIA DGX Systems and their role in AI training
• Cloud-based AI solutions (AWS, Azure, Google Cloud)

2. AI Storage & Data Management

• Types of AI storage solutions
• Data preprocessing and pipeline optimization

3. AI Networking & High-Speed Data Transfers

• Role of InfiniBand and RDMA in AI networking
• High-speed interconnects for distributed training

4. Energy-Efficient AI Computing

• Sustainable AI computing strategies
• Reducing carbon footprints in AI operations

5. Reference Architectures for AI Deployment

• Importance of Reference Architectures (RAs)
• Designing scalable AI solutions

6. Hands-on Lab

• Setting up AI infrastructure on cloud platforms
• Deploying AI models using Kubernetes and Docker

Module 3: AI Operations & Management 

1. Hands-on Lab

• AI workload monitoring tools (NVIDIA Nsight, Prometheus, Grafana)
• Detecting and resolving AI performance bottlenecks

2. AI Cluster Orchestration

• Kubernetes for AI workload orchestration
• Slurm for AI job scheduling

3. AI Job Scheduling & Workload Management

• Optimizing AI jobs across multiple GPUs
• Dynamic resource allocation for AI workloads

4. Hands-on Lab

• Monitoring AI workloads using Prometheus and Grafana
• Deploying AI workloads using Kubernetes

Module 4: Transition to Cloud AI Solutions

1. On-Prem vs. Cloud AI Deployment

• Comparing on-prem AI infrastructure with cloud-based AI solutions
• Cost-benefit analysis of cloud AI services

2. Hybrid Cloud AI Architectures

• Strategies for combining on-prem and cloud AI environments
• NVIDIA AI Enterprise solutions for hybrid AI workloads

3. Hands-on Lab

• Deploying an AI model on AWS SageMaker
• Managing AI workloads using NVIDIA AI Enterprise

Module 5: Certification Preparation & Final Assessment

1. Certification Exam Topics Review

• Key concepts and best practices from the course
• Sample questions and discussion

2. Mock Exams & Practical Assignments

• Hands-on problem-solving exercises
• Full-length mock exam

3. Final Q&A and Certification Readiness

• Review and clarification of key topics
• Exam-taking strategies

Course Pre-requisites

Labs: Open Source platform and Koenig DC will be provided

Pre-requisite:  Fundamentals of Python. Knowledge of machine learning will be an added advantage

Course Outline

Module 01: Introduction of GenAI 

• Introduction to Generative AI
• Architecture of Generative AI
• Applications of Generative AI using Transformer Library
• Introduction to Generative Adversarial Networks (GANs)
• Labs

Module 02: Introduction of Large language Model 

• Architecture of Large Language Models
• Types of Large Language Models (LLMs)
• Task based Text AI LLMs – Translation, Summarization, Sentence Similarity, Automatic Speech Recognition, Text to Speech, etc.
• Major Text AI LLMs – LLaMA, Qwen, Cohere, Falcon LLM
• Image AI Models & Services – Object Detection, Image Segmentation, Image Retrieval, Image, Image Captioning, Visual QnA, Zero-shot Image Classification, etc.
• Labs

Module 03: Learning Prompt Engineering using Open Source Models

• Introduction to Prompt Engineering
• Prompt Engineering Techniques
• Text Prompting using Llama (Meta)
• Image Prompting using Llama (Meta)
• Code Prompting using Llama (Meta)
• Labs

Module 04: Basic LLM Systems (RAG) using Open Source Models

• Introduction to Retrieval Augmented Generation (RAG)
• Introduction to LangChain
• Concept of Embedding, Retrieval, Chain and Agents using LangChain
• Las: Build a Simple LLM Application using LangChain
• Lab: Build a Chatbot LangChain
• Lab: Build vector stores and retriever using LangChain
• Lab: Build an Agent LangChain
• Lab: Build a Retrieval Augmented Generation (RAG) Application using LangChain
• Lab: Build a Conversational RAG Application using LangChain Module 05: Advanced LLM Systems (QnA) using Open Source Models
• Difference between RAG & Question Answering system
• Build a Question Answering system over Tabular Data using LangChain
• Build a Question/Answering system over SQL data using LangChain
• Labs

Module 06: Fine-tuning Techniques using Open Source Models

• Introduction to Quantization
• Optimization of model weights (data types)
• Modes of Quantization
• Fine tuning LLMs (Meta’s Llama / Alibaba’s Qwen / Google’s Gemma)
• Labs

Module 07: Evaluation of Open Source Models using MLflow

• Introduction to MLflow
• Build a machine learning model using MLflow
• MLflow Deployment Servers
• LLM Evaluation using MLflow
• Lab: Evaluate a Hugging Face LLM

Course Objective

• A comprehensive understanding of Generative AI, encompassing its historical context, key models, and ethical implications.
• Differentiate between search and reasoning engines, gaining practical skills in streamlining tasks with Microsoft Bing Chat.
• Overview of essential tools in artificial intelligence.

Pre-requisite

Chapter 01: Introduction to Generative AI

• Learn about the basics of generative AI, including its history, popular models, how it works, ethical implications, and much more
• Quiz

Chapter 02: The Evolution of Online Search

• Explore the distinctions between search engines and reasoning engines, with a focus on learning thoughtful search strategies in the world of generative AI
• Quiz

Chapter 03: Streamlining Your Work with Microsoft Bing Chat

• Learn how to leverage Microsoft Bing Chat to streamline and automate your work
• Quiz

Chapter 04: Ethics in the Age of Generative AI

• Learn why ethical considerations are a critical part of the generative AI creation and deployment process and explore ways to address these ethical challenges
• Quiz

Chapter 05: Introduction to Artificial Intelligence

• Get a simplified overview of the top tools in artificial intelligence
• Quiz

Course Outcome

Note: Client need to have their own ChatGPT Plus Subscription

Chapter 01: Introduction

1.1 What is AI in General

1.2 Generative AI Overview

1.3 Understanding Chat GPT

1.4 MS Bing: A Search Engine Powered by AI

1.5 Google BARD: Google Gen AI

1.6 Azure OpenAI: Microsoft Gen AI

1.6 DALL·E 2: The Artistic AI

1.7 Prompt Writing: Shaping AI Responses

Chapter 02: The Art of Text Prompting

2.1 Understanding Text Prompting

2.2 Iterative Techniques for Text Prompting

2.3 Using Summarization Techniques

2.4 Inference Techniques in Text Prompting

2.5 Transformation Techniques for Text

Chapter 03: Productivity Boost with ChatGPT Plus

3.1 Leveraging AI using Excel data

3.2 Automating Email using VBA in Excel

Chapter 04: Application Scenarios of Gen AI

4.1 Content Creation

4.2 Daily Task

4.3 Data Analysis and Reporting

4.4 Creative Projects

4.5 Decision Support

4.6 Research and Knowledge Gathering

Chapter 05: Best Practices for Using Gen AI

5.1 Importance of Reliability

5.2 Techniques for Debiasing in Gen AI

5.3 Ensuring Ethical Practices in Gen AI 

5.4 Addressing Transparency in Gen AI

5.5 Privacy Concerns using Gen AI

Course Overview

This comprehensive course equips business leaders with the knowledge and tools to harness the transformative power of artificial intelligence (AI) in a strategic, ethical, and impactful way. Covering everything from foundational concepts to cutting-edge applications, participants will explore key AI technologies, learn about generative AI tools, and discover how to align AI initiatives with business strategies. With a focus on real-world applications, ethical considerations, and practical skills like prompt engineering, this program empowers leaders to drive innovation, foster organizational readiness, and create sustainable AI-driven growth while building trust with stakeholders.

Course Outcomes

• Understand the Fundamentals of AI and Generative AI
• Evaluate and Strategize AI Opportunities for Business
• Lead Ethically and Responsibly in an AI-Driven World
• Apply Prompt Engineering and AI Tools to Enhance Productivity

Course Outline

Module 1: Demystifying Artificial Intelligence

• What is AI? Plain-language explanations
• History, evolution, and key milestones in AI
• Types of AI: Narrow, General, and Superintelligence
• Business Applications of AI
• Common misconceptions and what leaders really need to know
• Live Demo: ChatGPT + DALL·E for Business Tasks

Module 2: AI Technologies and Business Applications

• Overview of Machine Learning, Deep Learning, and Natural Language Processing (NLP)
• Real-world applications across industries: marketing, HR, operations, finance, healthcare
• Case studies: How organizations are winning (or failing) with AI
• What’s possible today vs what’s hype

Module 3: Introduction to Generative AI

• What is Generative AI and how does it work?
• Key tools: ChatGPT, DALL·E, Copilot, etc.
• Use cases: Content creation, automation, personalization, decision support
• Risks and ethical considerations in GenAI
• Hands-On Activity: Prompt Engineering Basics
• Live Demo: Copilot + ChatGPT in Workflow

Module 4: Strategic AI Leadership

• The role of leaders in AI-driven transformation
• Building an AI vision aligned with business strategy
• Organizational readiness and change management
• Collaborating with tech teams: What leaders need to ask and know

Module 5: Responsible and Ethical AI

• Core principles of responsible AI
• Bias, transparency, explainability
• Legal and regulatory considerations
• Building trust with stakeholders: customers, employees, partners

Module 6: Prompt Engineering for Business Leaders

• What is prompt engineering? Why it matters
• Designing effective prompts for business needs (summarization, insights, content generation)
• Hands-on examples using ChatGPT (no coding required)
• Guidelines for delegating prompt-based tasks in teams

Module 7: Driving Innovation and AI Adoption

• Identifying high-value AI opportunities
• Piloting and scaling AI projects
• Leadership traits for the AI era
• Capstone exercise: Drafting an AI roadmap or use case plan

Course Outline

Module 1: Foundations of AI and Cybersecurity

1. Introduction to AI in Cybersecurity:

• Key concepts: AI, ML, and DL in cybersecurity.
• Types of AI systems (rule-based, supervised, unsupervised, and reinforcement learning).
• Differences between traditional cybersecurity and AI-driven approaches.

2. Role of AI in Cybersecurity Domains:

• Threat detection and response.
• Predictive analytics for attack prevention.
• Behavioural analysis and anomaly detection.

3. AI in Network Traffic Analysis:

• Common attacks detectable via AI (DDoS, spoofing, port scans).
• Features extraction for network traffic using packet analysers (e.g., Wireshark).

4. Hands-On Lab:

• Setting up a network traffic dataset.
• Preprocessing the data for training AI models using Python and pandas.

Module 2: Machine Learning for Threat Detection

1. Supervised Learning in Cybersecurity:

• Training classifiers to detect malware, phishing, and fraud.
• Using decision trees, random forests, and support vector machines (SVM).

2. Unsupervised Learning:

• Clustering techniques for anomaly detection.
• Applications in insider threat detection and zero-day attack identification.

3. Data Preparation for ML Models:

• Handling imbalanced datasets (e.g., oversampling with SMOTE).
• Feature selection using mutual information or principal component analysis (PCA).

4. Hands-On Lab:

• Build a supervised ML model for intrusion detection using a public dataset (e.g., KDDCup99 or UNSW-NB15).
• Implement a clustering algorithm (e.g., K-means) for anomaly detection.

Module 3: Advanced Deep Learning Applications in Cybersecurity

1. Deep Learning Fundamentals for Cybersecurity:

• Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.
• How deep learning enhances malware detection and email classification.

2. AI for Endpoint Security and Fraud Detection:

• Endpoint vulnerability detection using DL models.
• Fraud detection techniques with recurrent models.

3. Phishing Email Detection with Natural Language Processing (NLP):

• Using pretrained NLP models (e.g., BERT, GPT) for phishing classification.
• Tokenization and text vectorization for cybersecurity datasets.

4. Hands-On Lab:

• Train and fine-tune a neural network for phishing detection.
• Use a malware dataset to train a CNN for malware classification.

Module 4: Securing AI Systems and Ethical Hacking with AI

1. AI Vulnerabilities and Adversarial Attacks:

• Types of attacks on AI models (e.g., poisoning, evasion).
• Adversarial examples and how attackers exploit AI systems.

2. Techniques to Secure AI Models:

• Defensive distillation and adversarial training.
• Role of explainable AI (XAI) in improving model robustness.

3. Using AI for Penetration Testing:

• AI-driven tools for vulnerability scanning and exploitation (e.g., Metasploit with ML extensions).
• Predicting attack vectors using AI models.

4. Hands-On Lab:

• Simulate adversarial attacks on a pre-trained AI model and implement mitigation strategies.
• Conduct AI-powered penetration testing on a virtual environment.

Module 5: Real-World AI Cybersecurity Solutions and Capstone Project

1. AI Cybersecurity Tools and Frameworks:

• Tools like IBM Watson for Cybersecurity, Darktrace, and Splunk with AI modules.
• Frameworks for building AI models (TensorFlow, PyTorch, Scikit-learn).

2. Emerging Trends in AI Cybersecurity:

• AI in quantum cryptography.
• The rise of generative AI in crafting and detecting cyber threats.

3. Capstone Project:

• Build an end-to-end AI-based cybersecurity solution:
• Dataset collection and preprocessing.
• Model training for anomaly detection.
• Deploy a proof-of-concept (PoC) in a simulated environment.

Course Overview

This course is designed to help data scientists, ML engineers, and AI developers establish a robust AI infrastructure using Azure services. Participants will work with Azure Machine Learning, OpenAI, Cognitive Search, DevOps, and containerized environments to develop and operationalize modern AI solutions, including RAG systems and large language model (LLM) deployments.

Duration: 40 hours

Course Objectives

By the end of this course, learners will:

• Understand Azure AI service offerings and architecture components.
• Set up AI-ready infrastructure using Azure ML, Storage, and Compute.
• Deploy and monitor AI models using Azure MLOps pipelines.
• Fine-tune and integrate OpenAI and custom LLMs.
• Build RAG-based applications with Azure Cognitive Search and Blob Storage.
• Apply governance, security, and cost optimization practices in AI projects.

Session Objective

Throughout this 16 hour program, participants will develop a deep understanding of Generative AI, with a particular focus on AI tools such as Midjourney, Invideo, Rephrase.ai, Hey Gen, ChatGPT, and its practical applications in marketing. They will gain the knowledge and skills required to leverage AI tools to enhance marketing strategy, video content creation, blogs, content marketing, perform SEO, build marketing strategies, perform data analysis and much more. Ultimately, becoming proficient in harnessing AI technologies to drive marketing success.

Course Outcomes

• Equip participants with a comprehensive understanding of Generative AI, particularly ChatGPT, HeyGen, Midjourney, Stablediffusion and more to understand its pivotal role in modern marketing
• Enable participants to apply ChatGPT to enhance content creation, marketing strategy development, and data analysis.
• Foster the ability to develop and execute AI-powered marketing plans, reinforcing marketing teams’ efficacy and innovation.
• Instil ethical and responsible AI practices within marketing strategies, ensuring alignment with company values and industry standards.
• Empower participants to present a comprehensive AI-driven marketing plan, demonstrating their proficiency in leveraging AI tools for marketing success.

Module outline

Using Gen AI for marketing

1. Introduction to AI in Marketing 

• Overview of the changing marketing landscape with AI.
• Key concepts and terminologies in AI relevant to marketing.
• Also understanding how Digital Marketing is changing drastically
• Case studies showcasing successful AI implementations in marketing.
• Introduction to NLP and its applications in marketing

2. Understanding Prompt Engineering from a marketing perspective 

• Explaining the concept of prompt engineering.
• Types of prompts to be used in Marketing domain
• How to build custom prompts for marketing specific tasks
• Practical exercises on creating effective prompts for marketing content.
• Strategies for guiding ChatGPT to produce desired outputs.

3. AI tools Use Cases for Marketing Teams 

• Exploring a range of applications for AI tools in marketing.

• Overview of Different AI tools to enhance marketing and create images, videos, content of different formats and more
• Real-world examples of LLM-driven marketing success stories.
• Identifying opportunities for marketing optimization through LLM

4. ChatGPT for content related activities 

• Leveraging ChatGPT for generating office presentations and strategy documents.
• Generating blog posts, articles, and social media content with ChatGPT
• Using ChatGPT for SEO content
• Building marketing images, videos using AI tools for marketing
• Applying ChatGPT to enhance email marketing campaigns.

5. Using AI tools for Marketing, PR and Comms 

• Exploring a range of applications for ChatGPT in marketing.
• Overview of Different AI tools to enhance marketing and create images, videos, content of different formats and more
• Using it for PR and Communication use case in Marketing
• Leveraging ChatGPT and other AI tools for enhancing marketing strategies.
• Real-world examples of ChatGPT-driven marketing success stories.
• Identifying opportunities for marketing optimization through ChatGPT
• Using Bing for market research

6. AI tools for Excel and Data Analysis 

• Using Ai tools for automating data analysis and report generation.
• Hands-on exercises in automating common marketing data tasks.
• Best practices for ensuring data accuracy and security with AI tools.

7: Recap & Ethical AI Principles 

• Fairness, transparency, and accountability

• Detecting and mitigating bias in AI responses

• Ethical decision-making in AI applications

• Handling sensitive customer information

• Ensuring compliance with data protection regulations

• Safeguarding data in AI-powered processes

• Recap and conclusion of the workshop

Session Objective

Help employees in manufacturing to leverage AI tools to perform better work and use ChatGPT to automate routine tasks

Course Outcomes

• Gain a solid understanding of AI and its practical application in manufacturing.
• Utilize ChatGPT to improve productivity and save time in manufacturing tasks.
• Foster creativity, innovation, and effective communication through ChatGPT integration.
• Expand AI skills to gain a competitive advantage in the manufacturing industry.
• Apply practical knowledge through hands-on exercises, successfully integrating ChatGPT in manufacturing workflows.
• Explore the potential of AI tools beyond ChatGPT for manufacturing processes.
• Gain confidence in applying AI solutions to optimize manufacturing operations.

Module outline

Session 1: Introduction to AI in Manufacturing 

• What is AI in Manufacturing: Overview of AI and its applications in the manufacturing industry.
• AI in Manufacturing Use Cases: Highlighting how AI is transforming manufacturing processes.
• Opportunities and Challenges: Exploring the potential benefits and considerations of AI adoption in manufacturing.

Session 2: How Generative AI is Changing the Company Landscape 

• Generative AI Overview: Introduction to Generative AI and its impact on various industries.
• Manufacturing Industry Trends: Recognizing how Generative AI is reshaping manufacturing practices.
• Case Studies: Analyzing successful Generative AI applications in manufacturing.

Session 3: Fundamentals of ChatGPT and Prompt Engineering 

• What is ChatGPT: Understanding the basics of ChatGPT and its capabilities.
• Exploring GPT -4.1: Key features and improvements in the latest version.
• The Role of Prompts: How prompts influence ChatGPT responses.
• How to prompt for various office tasks and take up Live use cases with participants
• Effective Prompting: Crafting prompts for specific marketing tasks and goals.
• Prompting Strategies: Exploring different approaches to achieve desired outcomes.

Session 4: Using AI for Office and Routine Tasks 

• Automating Routine Office Tasks: Applying AI to streamline administrative and office work.
• Using ChatGPT for Email, Reports, and More: Hands-on activities for office tasks automation.
• Time Savings and Productivity Gains: Measuring the impact of AI on daily work routines.
• Creating impressive PowerPoint presentations
• Using ChatGPT with Excel
• Using ChatGPT for measurement of critical excel reports and automating mundane tasks
• Using third party tools for financial analysis of public data
• Building macros using ChatGPT
• Case Study: A manufacturing use case demonstrating how AI enhances routine tasks.

Session 5: AI-Enhanced Product Design with Leonardo, Stable Diffusion, and Midjourney 

• Introduction to AI-Driven Product Design: Overview of AI tools such as Leonardo, Stable Diffusion, and Midjourney.
• Using AI for Product Design: Hands-on exercises for creating and enhancing product designs.
• Optimizing the Design Process: How AI accelerates innovation and reduces design time.

Session 6: Communication and Collaboration with AI 

• AI for Internal and External Communication: Utilizing AI for drafting emails, messages, and reports.
• ChatGPT in Teamwork: Enhancing collaboration and communication with AI.
• Case Studies: Successful integration of AI in communication and collaboration in a manufacturing context.

Session 7: AI-Powered Announcement in Regional Language (Using Clipchamp Tool) 

• Creating AI-Powered Regional Announcements: Using Clipchamp and AI to create announcements.
• Multilingual Communication: Expanding reach with announcements in regional languages.
• Measuring Effectiveness: Evaluating the impact of AI-powered regional announcements for various safety protocols, or AI driven regional transitions etc.

Session 8: Using AI help in increasing efficiency for operations task in Manufacturing set up

• Handling Employee Grievances with ChatGPT
• Crafting responses to common employee issues.
• Planning Company Events with AI
• Leveraging AI for efficient procurement and supply chain management
• Creating safety guidelines using ChatGPT.
• Ethical AI Practices in Manufacturing: Ensuring responsible and secure AI usage.
• Data Privacy and AI: Protecting sensitive manufacturing data.
• Compliance and Regulations: Navigating legal and ethical considerations in AI adoption.

Session 9: Q&A and Closing

• Open Forum: Participants can ask any remaining questions or seek clarification on AI in manufacturing.
• Workshop Recap: Summarizing key takeaways and actionable insights for successful AI integration.
• Closing Remarks: Encouraging participants to explore AI technologies for manufacturing optimization.

Course Outcome

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

• Understand the role of AI in identifying and mitigating security threats.
• Develop and deploy AI-driven threat detection systems using Python.
• Utilize machine learning and deep learning techniques for real-time intrusion detection.
• Implement natural language processing and reinforcement learning in security applications.
• Analyze and defend against adversarial attacks on AI security models.

Pre-requisite

To get the most out of this course, participants should have:

• Basic Programming Skills: Familiarity with Python syntax and data structures.
• Introduction to Cybersecurity: Basic knowledge of network security concepts, including malware, intrusion detection, and encryption.
• Mathematics for AI: Understanding of basic linear algebra, probability, and statistics.

Course Outline

Module 1: Introduction to AI in Security

• Overview of Artificial Intelligence in Cybersecurity
• Key Challenges in Cybersecurity
• AI Solutions for Security Threats

Module 2: Basics of Python for Security Applications

• Setting Up the Python Environment for Security Projects
• Essential Python Libraries for AI and Security

                o Libraries: NumPy, Pandas, Matplotlib, Scikit-Learn, Keras, PyTorch, Scapy, Requests

• Data Handling and Preprocessing for Security Datasets

Module 3: Machine Learning for Threat Detection

• Supervised Learning for Malware Classification

              o Building and Training Classification Models

              o Evaluating Model Performance

• Unsupervised Learning for Anomaly Detection

              o Clustering Techniques (K-Means, DBSCAN)

              o Dimensionality Reduction for Network Traffic Analysis

• Semi-Supervised Learning and Its Applications in Security

Module 4: Deep Learning Techniques for Security

• Introduction to Neural Networks for Security
• Convolutional Neural Networks for Intrusion Detection
• Recurrent Neural Networks for Log Analysis and Threat Detection
• Autoencoders for Anomaly Detection

Module 5: Natural Language Processing (NLP) in Security

• Text Classification for Phishing Email Detection
• Named Entity Recognition (NER) for Threat Intelligence
• Sentiment Analysis on Security News
• Text Summarization for Threat Reports

Module 6: Reinforcement Learning for Security Automation

• Basics of Reinforcement Learning (RL)
• RL for Intrusion Prevention Systems
• Adversarial Attacks and Defense Strategies with RL

Module 7: AI for Network Security and Intrusion Detection

• Intrusion Detection Systems (IDS) with Machine Learning
• Deep Packet Inspection with Deep Learning
• Network Traffic Analysis and Anomaly Detection
• Case Study: Building an AI-Driven Intrusion Detection System

Module 8: AI-Powered Malware Analysis and Detection

• Static Analysis with Machine Learning
• Dynamic Analysis Using Deep Learning
• Behavioral Analysis of Malware
• Case Study: Implementing a Malware Classifier

Module 9: AI for Threat Intelligence

• Data Sources for Threat Intelligence
• Knowledge Graphs for Threat Intelligence
• Automated Threat Hunting with AI
• Case Study: Creating a Threat Intelligence Pipeline

Module 10: Adversarial AI and Defense Mechanisms

• Understanding Adversarial Attacks on AI Models
• Defending Against Adversarial Attacks
• Securing AI Models in Production
• Case Study: Implementing Adversarial Defenses

Module 11: AI for Security Operations Center (SOC) Automation

• Incident Detection and Response Automation
• Log Analysis and Event Correlation with AI
• AI-Powered Incident Prioritization and Analysis
• Case Study: Automating SOC Workflows with AI

Module 12: AI-Driven Identity and Access Management (IAM)

• Machine Learning for Identity Verification
• Behavioral Biometrics and Anomaly Detection
• Facial Recognition and Authentication
• Case Study: Building an AI-Enhanced IAM System

Module 13: Implementing AI Models in Real-Time Security Applications

• Model Deployment in Security Environments
• Using Docker and Kubernetes for Model Deployment
• Monitoring and Maintenance of Deployed Models

Module 14: Ethical and Privacy Considerations in AI Security

• Ethical AI in Security Contexts
• Privacy Concerns and Compliance with GDPR
• Addressing Bias in AI Security Models
• Secure and Transparent Model Deployment

Module 15: Future of AI in Cybersecurity

• Emerging Trends in AI for Security
• Challenges and Limitations of AI in Cybersecurity
• Potential Advancements and the Road Ahead

Session Objective

Empower sales professionals working in retail environment with advanced AI tools, focusing on Generative AI and ChatGPT, to revolutionize sales strategies and optimize customer interactions.

Learning outcome

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

• Utilize AI tools effectively for sales strategies and customer interactions.
• Create compelling sales content through AI-driven refinements.
• Implement AI insights for store improvements and product placements.
• Train AI for objection handling and simulate scenarios for skill enhancement.
• Utilize AI algorithms for predictive analytics and data-driven strategies.
• Anticipate upcoming AI trends and consider ethical implications in AI-powered sales.

Module outline

1. Introduction to Generative AI in Sales

• Explanation of Generative AI’s impact on sales landscape
• Demonstrating how AI-driven solutions optimize sales processes
• Illustrating case studies showcasing successful AI integration in sales
• Discussing the future potential of AI in revolutionizing sales strategies
• Addressing concerns and misconceptions related to AI adoption in sales

2. Prompt Engineering for Effective AI Interactions

• Understanding the significance of prompts in AI interactions
• Analyzing the role of well-crafted prompts in driving desired outputs
• Designing prompts tailored for sales scenarios using Generative AI
• Crafting contextually relevant prompts to guide ChatGPT effectively
• Experimenting with various prompt styles for specific sales contexts

3. Using ChatGPT for Sales Scripts

• Crafting engaging and persuasive cold calling scripts with AI assistance
• Personalizing sales scripts for customer interactions using ChatGPT
• Designing scripts for upselling and cross-selling products/services
• Implementing ChatGPT-generated scripts for email communication with customers
• Analyzing AI-aided scripts’ effectiveness through A/B testing and feedback

4. Building Sales Collaterals & Documents using AI Tools

• Creating impactful sales presentations with AI-generated content suggestions
• Using AI tools for designing visually appealing sales collaterals
• Incorporating AI-generated insights for crafting persuasive sales emails
• Customizing proposals and documents using ChatGPT’s content refinement
• Streamlining document creation processes through AI-assisted templates

. Handling Objections with ChatGPT

• Training ChatGPT to provide effective responses to common objections
• Customizing objection-handling strategies based on AI-generated insights
• Role-playing objection scenarios to improve AI-guided responses
• Utilizing AI to adapt responses to different customer personas
• Evaluating and refining objection-handling techniques with AI feedback

6. Mock Sales Scenarios & Skill Practice

• Conducting simulated sales scenarios with AI-generated client interactions
• Receiving AI-provided feedback on sales techniques and communication skills
• Iteratively refining sales approaches based on ChatGPT-assisted role-plays
• Incorporating AI-driven insights for continuous sales skill enhancement

7. Data Analysis for Sales Forecast & Trends

• Implementing AI algorithms for predictive sales analytics and forecasting
• Utilizing AI tools to identify market trends and consumer behavior patterns
• Enhancing sales strategies through data-driven insights generated by AI
• Analyzing historical sales data with AI assistance to predict future trends
• Developing AI-driven predictive models for accurate sales projections

8. Future Trends in Sales & AI Integration

• Discussing upcoming AI advancements and their impact on sales methodologies
• Preparing sales teams for emerging AI technologies in the sales domain
• Examining the potential integration of AI with augmented reality (AR) for sales
• Predicting AI’s role in personalized and hyper-targeted sales approaches
• Considering ethical implications and regulations in the evolving AI-powered sales landscape

Why Take This Course?

• No prior programming or computer science expertise required.
• Engage with real-world examples and a mini project to see AI in action.

 What You’ll Learn

• What is AI?
• AI Concepts and Terms
• Ethical Concerns and Bias
• Career Insights

Training Program

1. Introduction to AI for Managers

• Understanding the strategic importance of AI
• Key terminology and concepts
• Benefits and challenges of AI adoption
• Ethical considerations in AI

2. Foundations of Data Science

• Basics of data collection, cleaning, and pre-processing
• Descriptive and inferential statistics
• Introduction to machine learning techniques

3. Machine Learning Algorithms for Managers

• Supervised learning (regression, classification)
• Unsupervised learning (clustering, dimensionality reduction)
• Model evaluation and selection

4. AI Applications in Business

• Predictive analytics for demand forecasting
• Recommender systems for personalised recommendations
• Natural language processing (NLP) for customer interactions

5. AI Strategy and Implementation

• Developing an AI roadmap aligned with business goals
• Managing AI projects: scope, resources, and timelines
• Change management and organisational readiness

6. Ethics and Responsible AI

• Mitigating bias in AI models
• Ensuring transparency and fairness
• Addressing privacy and security concerns

7. Case Studies and Real-world Examples

• Learning from successful AI implementations
• Analysing challenges and lessons learned

8. Emerging Trends in AI

• Reinforcement learning and its applications
• Edge computing for decentralised AI
• Industry-specific AI trends (finance, healthcare, etc.)

9. Leadership Skills for AI Managers

• Communicating AI strategies to non-technical stakeholders
• Building cross-functional AI teams
• Balancing innovation with risk management

10. Capstone Project and Practical Application

Applying AI knowledge to a real-world business scenario- Developing an AI strategy for the organisation- Presenting findings and recommendations

11. Continuous Learning and Staying Updated

 Keeping abreast of AI advancements- Networking with industry experts- Leveraging online resources and communities

Session Objective (Overall)

Empower leaders to leverage Generative AI and ChatGPT effectively, enhancing productivity and integrating AI-driven strategies into various business functions. This 8-hour workshop is designed to provide practical insights, hands-on exercises, and relevant use cases for each department.

Learning Outcomes

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

• Understand the foundational concepts of AI and Generative AI in business contexts.
• Implement prompt engineering techniques to produce targeted results across functions.
• Identify practical applications of Gen AI for business functions such as HR, Admin, Production, and Technology.
• Formulate a department-specific AI strategy to streamline operations and boost efficiency.
• Address ethical considerations and anticipate future trends in Generative AI.

Module Outline

Module 1: Introduction to AI

• Definition and scope of Artificial Intelligence in modern organizations.
• Evolution of AI from Machine Learning (ML) to Deep Learning (DL) and Generative AI.
• Real-world examples of AI in business functions.
• Understanding AI’s role in increasing efficiency and decision-making.
• Hands-on Activity: Discussion on AI’s impact and potential in participants’ respective departments.

Module 2: Understanding Generative AI

• Explanation of Generative AI and how it differs from traditional AI.
• Key applications of Generative AI in business.
• Exploring the benefits and challenges of Gen AI.
• Real-world use cases across industries.
• Dive into Various Generative AI Applications
• Tool Selection for Different Applications
• Demonstration of Text-to-Text Applications
• Explore Text to image tools, text to video, text to voice
• Hands-on Activity: Experiment with ChatGPT to understand generative responses for business inquiries.

Module 3: Prompt Engineering

• Introduction to prompt engineering and its importance.
• Types of prompts for various business needs.
• Customizing prompts to achieve desired AI outputs.
• Practical tips for crafting effective prompts.
• Hands-on Activity: Participants practice creating prompts specific to their departments and compare results.

Understanding Fundamentals of ChatGPT

• Exploration of Different use cases of ChatGPT
• Key Features and Functionalities
• Use Cases and Applications in your industry
• Comparison Between Various LLMs
• Limitations and Workarounds
• Future Developments within ChatGPT

Hands on activity: Participants will explore various ChatGPT, Bing AI, comparing their features and applications. They’ll experiment with different tools to understand their functionalities within business contexts.

Module 4: Building an AI Strategy

• Steps to create a customized AI strategy aligned with company goals.
• Identifying department-specific use cases for AI.
• Aligning AI strategy with productivity and efficiency goals.
• Addressing challenges in AI adoption and integration.
• Hands-on Activity: Teams outline an AI roadmap focusing on strategic, operational, and efficiency objectives.

Module 5: Generative AI for Various Business Functions

• Exploring department-specific applications:

• HR and Admin: Using AI for recruitment automation, employee engagement insights, policy drafting, and document management.
• Inserts Production: Optimizing production processes with Gen AI, SOP creation, taking advisory with Gen AI tools for various tasks
• Quality Control: Leveraging AI for quality assessment, defect detection, and real-time monitoring to maintain high standards and reduce human error using Traditional AI and computer vision
• Production Planning & Control: Utilizing Gen AI to forecast demand, optimize production schedules, and manage resources efficiently.
• Business Controller: Employing AI for financial forecasting, budget analysis, and data-driven decision-making, aiding in accurate financial planning and cost control.
• Purchase and Logistics: Streamlining procurement processes, inventory management, and supply chain optimization. Using AI to compare proposals, vendors and agreements, review supplier contracts too
• Facility Management & Sustainability: Enhancing facility operations, automating documentation, manual data entry, making of incident reports using Generative AI, also writing and dealing with local authority and vernacular language letters using Gen AI
• Case studies of Gen AI in different functions.
• Hands-on Activity: Participants identify potential applications in their departments and discuss implementation.

Module 6: Integrating Gen AI into Work

• Using Gen AI to streamline day-to-day operations.
• Enhancing productivity through automation.
• Document creation, meeting summaries, and content generation.
• Leveraging AI for internal communications and team collaboration.
• Hands-on Activity: Participants use Gen AI tools to draft documents, emails, or reports for typical departmental tasks.

Module 7: Using Gen AI for Office Functions

• Applying Gen AI in routine office functions for time savings.
• Document processing, scheduling, and task management automation.
• Using ChatGPT and other tools for repetitive tasks.
• Visual aids and presentations with AI support.
• Hands-on Activity: Participants automate a common office task using Gen AI tools and reflect on efficiency gains.

Module 8: Data Analysis and Presentation with Gen AI

• Overview of data processing and visualization with AI.
• Analyzing trends and insights using Gen AI tools.
• Creating engaging presentations with AI-generated visuals.
• Using AI for report generation and summarization.
• Hands-on Activity: Teams create a data-driven presentation or report using Gen AI insights.

Module 9: Ethics in Generative AI

• Ethical considerations and responsible AI usage.
• Addressing bias and fairness in AI-driven decisions.
• Ensuring transparency and accountability.
• Regulatory landscape and industry guidelines.
• Discussion: Participants discuss ethical challenges in their respective departments and brainstorm mitigation strategies.

Session Objective: 

To equip participants with the knowledge and skills necessary to effectively integrate ChatGPT and AI tools into HR and L&D processes, optimizing efficiency, and fostering responsible AI usage.

Learning outcome

• Understand the basics of AI, NLP, and ChatGPT in the HR context to identify AI application opportunities.
• Craft effective prompts and use prompt engineering techniques to generate high-quality HR content with ChatGPT.
• Apply ChatGPT and AI tools to streamline talent acquisition, learning and development, and HR operations for increased efficiency.
• Address ethical considerations and ensure responsible AI usage in HR, focusing on data privacy and security.

Stay informed about AI’s impact on the future of HR work and the latest advancements in ChatGPT technology for HR practices

Module Outline

1: Introduction to AI and LLMs

• Overview of Artificial Intelligence (AI) and its applications in various industries.
• Understanding the basics of Natural Language Processing (NLP) and its significance.
• Introduction to LLMs and its capabilities.
• Key features and benefits of LLMs in HR processes.

2: Understanding How Prompt Works and Prompt Engineering

• Explaining the concept of prompts and how they guide ChatGPT’s responses.
• Guidelines for crafting effective prompts to get desired results.
• Hands-on practice with prompt engineering techniques for HR-specific tasks.
• Using ChatGPT for emails, presentations

3: Using AI tools & ChatGPT for HR Talent Acquisition

• The role of ChatGPT in streamlining talent acquisition processes.
• Benefits of integrating ChatGPT in candidate sourcing, screening, and selection.
• Leveraging ChatGPT to draft compelling job descriptions.
• Building a ChatGPT bot to screen CV’s
• Using People GPT
• Using ChatGPT to draft employment contracts for new hires.
• Building interview questionnaires and psychometric evaluation using ChatGPT.

4: Using AI tools for L&D

• Utilizing AI Tools for presentations.
• Building Videos for courses using AI tools.
• Building modules via Video, text, pdf all using AI.
• Understanding how AI powered LMS works.
• Utilizing ChatGPT for building assessments.
• Building videos for learning using AI.

5: Using AI tools & ChatGPT for HR Operations

• Overview of AI Tools’s role in HR operations and process optimization.
• Automating mundane tasks such as Emails, onboarding, offboarding employees using AI tools
• Building SOP and guidelines using AI tools & ChatGPT
• Automating HR policies drafting and updates using ChatGPT.

6: AI-Powered Change Management for HR

• Using AI tools to create communication plans, FAQs, and stakeholder updates.
• Crafting personalized messages for different employee segments.
  Designing campaigns and toolkits for change champions.
• Example prompts: “Draft a resistance-handling guide for managers.”
• Building pulse surveys and feedback tools with AI.
• Generating reinforcement content (nudges, microlearning, reminders).
• Hands-on creation of change roadmaps, talking points, training modules, and executive briefings.

7: Ethical Consideration

• Addressing ethical concerns related to AI and ChatGPT usage in HR.
• Responsible AI and how to ensure company is embracing for AI-driven environment
• Understanding potential biases and mitigating their impact.
• Ensuring data privacy and security in AI-powered HR processes.

8: Future Trends

• Exploring the evolving landscape of AI in HR and its potential impact on the industry.
• Understanding the role of AI in shaping the future of work in HR.
• Predictions and insights into upcoming advancements in ChatGPT technology.

Conclusion

• Recapitulation of key learnings from the curriculum.
• Emphasizing the importance of responsible AI usage in HR.
• Encouraging continuous learning and adaptation to emerging AI technologies in HR practices.

1-Hour Check in (Webinar) – Post session after 1 week

• Recap of session highlights.
• 1-hour checkpoint for Q&A, discussion, and reflection.
• Encouraging experimentation, ongoing learning, and cross-functional collaboration.

Module 1: What is AI?

• Introduction
• Machine Learning
• What is data?
• The terminology of AI
• What makes an AI company?
• What Machine Learning can and cannot do
• Intuitive explanation of deep learning

Module 2: Building AI Projects

• Workflow of a Machine Learning project
• Workflow of a Data Science project
• Every job function needs to learn to use data
• How to choose an AI project
• Working with an AI team
• Technical tools for AI teams

Module 3: Building AI In Your Company

• Case study: Smart speaker
• Case study: Self-driving car
• Example roles of an AI team
• AI Transformation Playbook
• AI pitfalls to avoid
• Taking your first step in AI
• Survey of major AI applications
• Survey of major AI techniques

Module 4: AI and Society

• A realistic view of AI
• Discrimination / Bias
• Adversarial attacks
• Adverse uses of AI
• AI and developing nations
• AI and jobs.

Module 01: Microsoft Azure AI Fundamentals: AI Overview

• Fundamental AI Concepts
• Fundamentals of machine learning
• Fundamentals of Azure AI services
• Exercise – Explore Automated Machine Learning in Azure Machine Learning
• Exercise – Explore Azure AI Services

Module 02: Microsoft Azure AI Fundamentals: Computer Vision

• Fundamentals of Computer Vision
• Fundamentals of Facial Recognition
• Fundamentals of optical character recognition
• Exercise – Analyze images in Vision Studio
• Exercise – Detect faces in Vision Studio
• Exercise – Read text in Vision Studio

Module 03: Microsoft Azure AI Fundamentals: Natural Language Processing

• Fundamentals of Text Analysis with the Language Service
• Fundamentals of question answering with the Language Service
• Fundamentals of conversational language understanding
• Fundamentals of Azure AI Speech
• Exercise – Analyze text with Language Studio
• Exercise – Use question answering with Language Studio
• Exercise – Use Conversational Language Understanding with Language Studio
• Exercise – Explore Speech Studio

Module 04: Document Intelligence and Knowledge Mining

• Fundamentals of Azure AI Document Intelligence
• Fundamentals of Knowledge Mining and Azure AI Search
• Exercise – Extract from data in Document Intelligence Studio
• Exercise – Explore an Azure AI Search index (UI)

Module 05: Microsoft Azure AI Fundamentals: Generative AI

• Fundamentals of Generative AI
• Fundamentals of Azure OpenAI Service
• Fundamentals of Responsible Generative AI
• Exercise – Explore generative AI with Bing Copilot
• Exercise – Explore Azure OpenAI Service
• Exercise – Explore content filters in Azure OpenAI

AI & Generative AI Bootcamp

Note: Exploring labs/exercises for Module 05 & Module 06 would require an OpenAI /Azure OpenAI subscription from the students. The subscription can be bought from here: https://platform.openai.com/account/billing/overview

DURATION: 15 Days

Module 1: Python – 3 Day 

• Introduction
• Data Types
• Variables
• Decision Control Files
• Operators
• List, Tuples, Sets, Dictionary
• Functions and Methods
• File Handling
• Module
• String
• Iterators and Generators
• Regular Expressions
• OO Programming Concepts
• Numpy
• Pandas
• Matplotlib

Module 2: Machine Learning Essentials – 3 Day 

• Machine Learning (ML) Lifecycle
• Statistics & Mathematics
• Feature Selection Technique
• Linear regression
• Logistic Regression
• Classification – Decision Trees & Random Forests
• Classification – Naive Bayes
• Clustering (K-Means)
• Classification – SVM (Support Vector Machines)
• Principal Component Analysis (PCA)
• Recommendation (Content based filtering & Collaborative filtering)

Module 3: AI-900 Microsoft Azure AI Fundamentals – 2 Day

• Cloud Concepts
• Describe Artificial Intelligence workloads and considerations
• Describe fundamental principles of machine learning on Azure
• Describe features of computer vision workloads on Azure
• Describe features of Natural Language Processing (NLP) workloads on Azure
• Describe features of generative AI workloads on Azure

Module 4: AI-102 Designing and Implementing a Microsoft Azure AI Solution – 4 Day 

• Plan and manage an Azure AI solution
• Implement decision support solutions
• Implement computer vision solutions
• Implement natural language processing solutions
• Implement knowledge mining and document intelligence solutions
• Implement generative AI solutions

Module 5: ChatGPT for End Users – 1 Day 

• Understand ChatGPT capabilities in various tasks
• Learn text prompting techniques for enhanced results
• Apply effective prompting techniques for different tasks
• Explore different OpenAI applications
• Learn image prompting techniques for enhanced results
• Understand prompt reliability and privacy concerns

Module 6: Azure OpenAI training for Developers– 2 Day

• Introduction to Azure OpenAI
• Azure OpenAI libraries and models
• Text Completion Model
• Code Completion Model
• Image Generation Model
• Fine-tuning of models

DURATION: 1 Day

Course Objective

1. Introduction to Artificial Intelligence

• Definition and Scope of AI
• Historical Evolution of AI
• Key Concepts and Terminologies
• Types of AI: Narrow, General, and Superintelligent AI

2. AI in Practice

• How AI Algorithms Work: An Overview
• Machine Learning vs. Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Use Cases and Applications Across Industries

3. Effective and Ethical Utilization of AI

• Understanding AI Ethics and Its Importance
• Bias in AI: Detection and Mitigation
• Transparency and Explain ability in AI
• Regulatory Compliance and Standards – Case Studies on Ethical AI Implementation

4. Managing AI Projects

• Project Life Cycle in AI Development
• Stakeholder Management and Communication
• Resource Allocation and Budgeting
• Risk Management in AI Projects
• Success Metrics and Performance Evaluation

5. AI Governance and Oversight

• Frameworks for AI Governance
• Role of AI Ethics Committees
• Data Governance and Privacy Concerns
• Continuous Monitoring and Auditing AI Systems
• Legal and Policy Implications

6. AI in Business Strategy

• Aligning AI Initiatives with Business Goals
• Building a Data-Driven Culture
• Integrating AI into Business Processes
• Change Management for AI Adoption
• Measuring Return on Investment (ROI) for AI

7. Launching and Scaling AI-based Start-ups

• Ideation and Validation of AI Start-ups
• Building an AI-Driven Business Model
• Funding and Investment Strategies
• Assembling the Right Team
• Challenges and Solutions in AI Start-ups

8. Future Trends in AI

• Emerging Technologies in AI
• The Impact of AI on Jobs and Society
• Future Ethical Considerations
• Predictions for AI Development
• Preparing for the Future of AI

Course Description

AI in Project Management is a 3-day, instructor-led program focused on the practical application of Artificial Intelligence within Project Management functions. The training emphasizes Project Management using the CPMAI (Cognitive Project Management for AI) approach for managing AI projects. It combines AI fundamentals, CPMAI-aligned project execution, real-world case studies, and hands-on exercises using practical templates. Participants apply the learning directly to their own projects to understand how AI-enabled initiatives are planned, governed, executed, and operationalized.

Learning Objectives

• Understand core AI concepts and how AI is applied within Project Management functions
• Apply Cognitive Project Management principles (CPMAI) to manage AI initiatives across the project lifecycle
• Identify and evaluate AI use cases relevant to project environments and organizational needs
• Design, assess, and simulate AI-enabled workflows, including automation and agentic AI use cases
• Make informed project decisions for AI initiatives considering business value, risk, governance, and ethics

Total hours of PDU

• 21 hours 

Lesson 1: Solving Business Problems Using AI and ML

Topic A: Identify AI and ML Solutions for Business Problems

• The Data Hierarchy—Making Data Useful
• Big Data
• Guidelines for Working with Big Data
• Data Mining
• Examples of Applied AI and ML in Business
• Guidelines to Select Appropriate Business Applications for AI and ML
• Identifying Appropriate Business Applications for AI and ML

Topic B: Follow a Machine Learning Workflow

• Machine Learning Model
• Machine Learning Workflow
• Data Science Skillset
• Traditional IT Skillsets
• Concept Drift
• Transfer Learning
• Guidelines for Following the Machine Learning Workflow
• Planning the Machine Learning Workflow

Topic C: Formulate a Machine Learning Problem

• Problem Formulation
• Framing a Machine Learning Problem
• Differences Between Traditional Programming and Machine Learning
• Differences Between Supervised and Unsupervised Learning
• Randomness in Machine Learning
• Uncertainty
• Random Number Generation
• Machine Learning Outcomes
• Guidelines for Formulating a Machine Learning Outcome
• Selecting a Machine Learning Outcome

Topic D: Select Appropriate Tools

• Open Source AI Tools
• Proprietary AI Tools
• New Tools and Technologies
• Hardware Requirements
• GPUs vs. CPUs
• GPU Platforms
• Cloud Platforms
• Guidelines for Configuring a Machine Learning Toolset
• How to Install Anaconda
• Selecting a Machine Learning Toolset

Lesson 2: Collecting and Refining the Dataset             

Topic A: Collect the Dataset – Machine Learning Datasets

• Structure of Data
• Terms Describing Portions of Data
• Data Quality Issues
• Data Sources
• Open Datasets
• Guidelines for Selecting a Machine Learning Dataset
• Examining the Structure of a Machine Learning Dataset
• Extract, Transform, and Load (ETL)
• Machine Learning Pipeline
• ML Software Environments
• Guidelines for Loading a Dataset
• Loading the Dataset

Topic B: Analyze the Dataset to Gain Insights

• Dataset Structure
• Guidelines for Exploring the Structure of a Dataset
• Exploring the General Structure of the Dataset
• Normal Distribution
• Non-Normal Distributions
• Descriptive Statistical Analysis
• Central Tendency
• When to Use Different Measures of Central Tendency
• Variability
• Range Measures
• Variance and Standard Deviation
• Calculation of Variance
• Variance in a Sample Set
• Calculation of Standard Deviation
• Skewness
• Calculation of Skewness Measures
• Kurtosis
• Calculation of Kurtosis
• Statistical Moments
• Correlation Coefficient
• Calculation of Pearson’s Correlation Coefficient
• Guidelines for Analyzing a Dataset
• Analyzing a Dataset Using Statistical Measures

Topic C: Use Visualizations to Analyze Data

• Visualizations
• Histogram
• Box Plot
• Scatterplot
• Geographical Maps
• Heat Maps
• Guidelines for Using Visualizations to Analyze Data
• Analyzing a Dataset Using Visualizations

Topic D: Prepare Data

• Data Preparation
• Data Types
• Operations You Can Perform on Different Types of Data
• Continuous vs. Discrete Variables
• Data Encoding
• Dimensionality Reduction
• Impute Missing Values
• Duplicates
• Normalization and Standardization
• Summarization
• Holdout Method
• Guidelines for Preparing Training and Testing Data
• Splitting the Training and Testing Datasets and Labels

Lesson 3: Setting Up and Training a Model

Topic A: Set Up a Machine Learning Model

• Design of Experiments
• Hypothesis
• Hypothesis Testing
• Hypothesis Testing Methods
• p-value
• Confidence Interval
• Machine Learning Algorithms
• Algorithm Selection
• Guidelines for Setting Up a Machine Learning Model
• Setting Up a Machine Learning Model

 Topic B: Train the Model

• Iterative Tuning
• Bias
• Compromises
• Model Generalization
• Cross-Validation
• k-Fold Cross-Validation
• Leave-p-Out Cross-Validation
• Dealing with Outliers
• Feature Transformation
• Transformation Functions
• Scaling and Normalizing Features
• The Bias–Variance Tradeoff
• Parameters
• Regularization
• Models in Combination
• Processing Efficiency
• Guidelines for Training and Tuning the Model
• Refitting and Testing the Model

Lesson 4: Finalizing a Model

Topic A: Translate Results into Business Actions

• Know Your Audience
• Visualization for Presentation
• Guidelines for Presenting Your Findings
• Translating Results into Business Actions

 Topic B: Incorporate a Model into a Long-Term Business Solution

• Put a Model into Production
• Production Algorithms
• Pipeline Automation
• Testing and Maintenance
• Consumer-Oriented Applications
• Guidelines for Incorporating Machine Learning into a Long-Term Solution
• Incorporating a Model into a Long-Term Solution

Lesson 5: Building Linear Regression Models                    

 Topic A: Build a Regression Model Using Linear Algebra

• Linear Regression
• Linear Equation
• Linear Equation Data Example
• Straight Line Fit to Example Data
• Linear Equation Shortcomings
• Linear Regression in Machine Learning
• Linear Regression in Machine Learning Example
• Matrices in Linear Regression
• Normal Equation
• Linear Model with Higher Order Fits
• Linear Model with Multiple Parameters
• Cost Function
• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)
• Coefficient of Determination
• Normal Equation Shortcomings
• Guidelines for Building a Regression Model Using Linear Algebra
• Building a Regression Model Using Linear Algebra

Topic B: Build a Regularized Regression Model Using Linear Algebra

• Regularization Techniques
• Ridge Regression
• Lasso Regression
• Elastic Net Regression
• Guidelines for Building a Regularized Linear Regression Model
• Building a Regularized Linear Regression Model

  Topic C: Build an Iterative Linear Regression Model

• Iterative Models.
• Gradient Descent.
• Global Minimum vs. Local Minima.
• Learning Rate.
• Gradient Descent Techniques.
• Guidelines for Building an Iterative Linear Regression Model.
• Building an Iterative Linear Regression Model.

DURATION: 16 Hours

Introduces the history, background, technologies, organizational challenges and strategies towards
applying artificial intelligence for IT Operations, or AIOps, a rapidly growing industry driven by the rapidly
evolving IT operational environments of cloud native applications. Tailored for those focused on
understanding basic concepts, implementations, use cases and benefits.
 
This AIOps Foundation course aims to cover the origins of AIOps including the history behind the term,
patterns that preceded it and the technology context in which it has evolved. Learners will gain an
understanding of the processes of combining big data analytics, machine learning algorithms, automation,
and optimization into a single platform.
 
This course introduces key principles and foundational concepts along with the core technologies of
AIOps: big data and machine learning. The course will provide students with an understanding of how and
why digital transformation, together with the evolution of machine learning, have brought about the rise
of AIOps as an indispensable tool in today’s IT Operational landscape.
 
Core technologies of machine learning and big data will be discussed, as well as the basic concepts of
artificial intelligence, different types of machine learning models that can be implemented, and the
relationship between AIOps and MLOps, DevOps and Site Reliability.
 
This foundation course will also provide the student with a solid understanding of the benefits of
implementing AIOps in the organization, including common challenges and key steps in ensuring valuable
and successful integration of artificial intelligence in the day-to-day operations of information technology
solutions.
 
Unique and exciting exercises will be used to apply the concepts covered in the course and sample
Documents, templates, tools, and techniques will be provided to use after the class. This course positions
learners to successfully complete the AIOps Foundation certification exam.

DURATION: 2 Day (16 hours).
Note: To complete the hands-on labs in this course, students require an Azure subscription that has been approved for access to the Azure OpenAI service. Azure OpenAI:
https://learn.microsoft.com/legal/cognitive-services/openai/limited-access

&nbsp:
Pre-requisites:

• Familiarity with Azure and the Azure portal.
• Experience programming with C# or Python.
• Python Check: https://learn.microsoft.com/en- us/training/paths/beginner-python/.
• C# Check: https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/.

Module 01: Introduction to Azure OpenAI

• Azure OpenAI’s language, code, and image capabilities.
• Azure OpenAI’s responsible AI practices and limited access policies.
• Types of Azure OpenAI’s base model and its deployment.
• Lab: Creation of Azure OpenAI resource/OpenAI & accessing Playground.

 
Module 02: Chat Copilot using Azure OpenAI Studio

• Brief of Azure Storage Account, Azure Cognitive Search, App Services & App Service Plans.
• Basic workflow for Copilot Creation.
• Lab: Build your own Chat Copilot using various Azure Services (Azure Portal).

 
Module 03: Art of Effective Prompting Techniques

• Understanding Text Prompting.
• Iterative Techniques for Text Prompting.
• Using Summarization Techniques.
• Inference Techniques in Text Prompting.
• Transformation Techniques for Text.
• Exercise/Documentation: Effective Prompting Techniques (Jupiter notebook).

 
Module 04: Prompt Flow Design using Azure Machine Learning Studio

• Introduction to Azure Machine Learning Studio.

Introduction to prompt flow.

Lab: Prompt flow design and implementation.

 
Module 05: Introduction to Semantic Kernel

• Introduction to Semantic Kernel.
• Working and Components of Semantic Kernel.
• Concept of Chat Plugin & its Integration into Applications.
• Integrating Semantic Kernel with Azure OpenAI models.
• Introduction to Autogen in Semantic Kernel.
• Native Functions.
• Chaining Functions using Azure OpenAI.
• Lab: Basic Labs on Semantic Functions.
• Lab: Adding skillsets to Semantic Kernel.
• Lab: Adding memories to Semantic Kernel.
• Lab: Using connectors in Semantic Kernel.
• Lab: Chaining concept in Semantic Kernel.
• Lab: Integrating Bing with Azure OpenAI using Semantic Kernel.

DURATION: 4 Day.
 
Time Division: – Break: 15 + 45 + 15 minutes
 

Course Outcomes:

• Design and prepare a machine learning solution.
• Explore data and train models.
• Prepare a model for deployment.
• Deploy and retrain a model.

 
Important Note:

• Courseware – Reference material/ppt along with lab files/exercises will be provided.
• Azurepass/Virtual Machine will be provided only during the training time to perform the labs.

Module 1: Explore and configure the Azure Machine Learning workspace

• Explore Azure Machine Learning workspace resources and assets.
• Lab: Explore Azure Machine Learning workspace resources and assets.
• Explore developer tools for workspace interaction.
• Lab: Explore developer tools for workspace interaction.
• Work with compute targets in Azure Machine Learning.
• Lab: Work with compute targets in Azure Machine Learning.
• Work with environments in Azure Machine Learning.
• Lab: Work with environments in Azure Machine Learning.

Module 2: Work with Data in Azure Machine Learning

• Make data available in Azure Machine Learning.
• Lab: Make data available in Azure Machine Learning.

 
Module 3: Experiment with Azure Machine Learning

• Find the best classification model with Automated Machine Learning.
• Lab: Train a model with the Azure Machine Learning Designer.
• Lab: Find the best classification model with Automated Machine Learning.
• Track model training in Jupiter notebooks with MLflow.
• Lab: Track model training in notebooks with MLflow.

 
Module 4: Train models with scripts in Azure Machine Learning

• Run a training script as a command job in Azure Machine Learning.
• Lab: Use MLflow to track training jobs.
• Track model training with MLflow in jobs.
• Lab: Track model training with MLflow in jobs.

 
Module 5: Optimize model training with Azure Machine Learning

• Run pipelines in Azure Machine Learning.
• Lab: Run pipelines in Azure Machine Learning.
• Perform hyper parameter tuning with Azure Machine Learning.
• Lab: Perform hyper parameter tuning with Azure Machine Learning.

 
Module 6: Deploy and consume models with Azure Machine Learning

• Deploy a model to a managed online endpoint.
• Lab: Log and register models with MLflow.
• Lab: Compare and evaluate models.
• Lab: Deploy a model to a managed online endpoint.
• Deploy a model to a batch endpoint.
• Lab: Deploy a model to a batch endpoint.

DURATION: 4 Day.

Day 01: Get started and develop decision support solutions with Azure AI Services

• Prepare to develop AI solutions on Azure.
• Create and consume Azure AI services.
• Use a REST API and an SDK.
• Secure Azure AI services.
• Implement network security.
• Monitor Azure AI services.
• Deploy Azure AI services in containers.
• Classify and moderate text with Azure Content Moderator.
• Make recommendations with Azure AI Personalizer.
• Exercise – Use Azure AI services.
• Exercise – Manage Azure AI Services Security.
• Exercise – Monitor Azure AI services.
• Exercise – Use a container.
• Exercise – Test text moderation by using the API testing console.
• Exercise: Use AI Personalizer with Visual Studio Code Notebooks to simulate a loop.

Day 02: Create computer vision solutions with Azure AI Vision

• Analyse images.
• Classify images.
• Detect objects in images.
• Detect, analyse, and recognize faces.
• Read Text in images and documents with the Azure AI Vision Service.
• Analyze video.
• Exercise – Analyze images with Azure AI Vision.
• Exercise – Classify images with Azure AI Custom Vision.
• Exercise – Detect objects in images with Azure AI Custom Vision.
• Exercise – Detect, analyze, and identify faces.
• Exercise – Read text in images.
• Exercise – Analyze video.

 
Day 03: Develop natural language processing solutions with Azure AI Services

• Analyze text with Azure AI Language.
• Build a question answering solution.
• Build a conversational language understanding model.
• Create a custom text classification solution.
• Create a custom named entity extraction solution.
• Translate text with Azure AI Translator service.
• Create speech-enabled apps with Azure AI services.
• Translate speech with the Azure AI Speech service.
• Exercise – Analyze text.
• Exercise – Create a question answering solution.
• Exercise – Build an Azure AI services conversational language understanding model.
• Exercise – Classify text.
• Exercise – Extract custom entities.
• Exercise – Translate text with the Azure AI Translator service.
• Exercise – Create a speech-enabled app.
• Exercise – Translate speech.

 
Day 04: Develop Solutions with Azure Cognitive Search, Azure AI Document Intelligence and Azure OpenAI Service

• Create an Azure AI Search solution.
• Create a custom skill for Azure AI Search.
• Create a knowledge store with Azure AI Search.
• Extract data from forms with Azure Document Intelligence.
• Build natural language solutions with Azure OpenAI Service.
• Apply prompt engineering with Azure OpenAI Service.
• Use your own data with Azure OpenAI Service.
• Exercise – Create a search solution.
• Exercise – Implement a custom skill.
• Exercise – Create a knowledge store.
• Exercise – Extract data from custom forms.
• Exercise – Integrate Azure OpenAI into your app.
• Exercise – Utilize prompt engineering in your application.
• Exercise – Use your own data with Azure OpenAI Service.

DURATION: 3 Day.

Note: Labs for almost all modules can be performed on open source. So Koenig DC can be provided. Student can also install software’s on their own system.
 
Module 1: MLOps Fundamentals

• Introduction to MLOps and its significance.
• Challenges in traditional ML model management.
• Solutions offered by MLOps.

Module 2: MLOps Toolbox

• Applying MLOps tools for end-to-end projects.
• Integration of tools: DVC, Git, MLFlow, and DagsHub.

 
Module 3: Model Versioning with MLFlow

• Versioning and registering ML models with MLFlow.
• MLlow’s role in managing ML lifecycle.

 
Module 4: Data Versioning with DVC

• Capturing data and model versions with DVC.
• On-premises and cloud storage integration.

Module 5: Creating Shared ML Repository

• Utilizing DagsHub, DVC, Git, and MLFlow for versioning.
• Collaborative ML model management.

Module 6: Auto-ML and Low-Code MLOps

• Automation of ML model development with Auto-ML and Pycaret.
• Streamlining model versioning, training, evaluation, and deployment.

Module 7: Explain ability and Auditability

• Understanding model interpretability and explain ability.
• Monitoring model performance and data drift with SHAP and Evidently.

Module 8: Containerized ML Workflow with Docker

• Packaging code and dependencies using Docker.
• Efficient distribution of Machine Learning applications.

Module 9: Deploying ML via APIs

• Model deployment through API development with FastAPI and Flask.
• Deploying APIs on Azure Cloud using containers.

 
Module 10: Deploying ML in Web Applications

• Developing web apps with embedded ML models using Gradio and Flask.
• Deploying to production in Azure via Docker containers.

 
Module 11: Automated ML Services with BentoML

• Introduction to BentoML and its role in automated ML service development.
• Putting BentoML services into production using Docker.
• Integration of BentoML with MLFlow.

 
Module 12: CI/CD with GitHub Actions and CML

• Introduction to GitHub Actions and Continuous Machine Learning (CML).
• Practical lab: GitHub Actions for MLOps CI/CD.

 
Module 13: Model Monitoring with Evidently A

• Monitoring models and services using Evidently AI.
• Identifying data drift and evaluating model quality.

 
Module 14: Model Monitoring with Deep checks

• Components of Deep checks: checks, conditions, and suites.
• Hands-on experience with Data Integrity Suite, Train Test Validation Suite, Model.
• Evaluation Suite, and Custom Performance Suite.

 
Module 15: Complete MLOps Project

• Developing an ML model from scratch.
• Validating code and pre-processing data.
• Versioning with MLFlow and DVC.
• Sharing repository with DagsHub and MLFlow.
• Building an API with BentoML.
• Creating a Streamlet app.
• Implementing CI/CD with GitHub Actions.

DURATION: 3 Day (40 hours).

Important Note from Microsoft:

To complete the hands-on labs in this course, students require an Azure subscription that has been approved for access to the Azure OpenAI service. Access approval can take several days to be granted. See https://learn.microsoft.com/legal/cognitive-services/openai/limited-access for details.
 
Course Pre-requisites:

• Familiarity with Azure and the Azure portal..
• Experience programming with C# or Python..
• Python check: https://learn.microsoft.com/enus/training/paths/beginner-python/.
• C# Check: https://learn.microsoft.com/en-us/training/paths/getstarted-c-sharp-part-1/.

 
Module 01: Introduction to Azure OpenAI Service

• Describe Azure OpenAI workloads and access the Azure OpenAI Service.
• Understand generative AI models.
• Understand Azure OpenAI’s language, code, and image capabilities.
• Understand Azure OpenAI’s responsible AI practices and limited access policies.

 
Module 02: Get Started with Azure OpenAI Service

• Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models.
• Use the Azure OpenAI Studio, console, or REST API to deploy a base model and test it in the Studio’s
• playgrounds.
• Generate completions to prompts and begin to manage model parameters.

 
Module 03: Build natural language solutions with Azure OpenAI Service

• Integrate Azure OpenAI into your application.
• Differentiate between different endpoints available to your application.
• Generate completions to prompts using the REST API and language specific SDKs.

 
Module 04: Apply prompt engineering with Azure OpenAI Service

• Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models’ performance.
• Know how to design and optimize prompts to better utilize AI models.
• Include clear instructions, request output composition, and use contextual content to improve the quality of the model’s responses.

DURATION: 3 Day (40 hours).
Time Division (Break: 15 + 45 + 15 mines).
 
Course Outcomes:

• Understand the fundamentals of Data Science and Machine Learning.
• Analyse and pre-process data proficiently using Python.
• Apply Supervised Machine Learning techniques for regression and classification.
• Apply Unsupervised Machine Learning for clustering and natural language.
• Introduction to Deep learning concepts.

Chapter 1 – Introduction to Artificial Intelligence

• Artificial Intelligence (AI).

 
Definition of Artificial Intelligence (AI)

• Types of AI.
• Machine Learning (ML).

 
Definition of ML

• Supervised Learning – Classification and Regression.
• Unsupervised Learning – Clustering and Association.
• Reinforcement Learning.
• Deep Learning (DL).

 
Deep Learning and the Types of Neural Networks.

• Stages of the ML Process.

 
Chapter 2 – Overview of Testing AI Systems

• AI Testing Phases.

 
Offline and Online Testing of AI Systems

• AI vs. Non-AI Testing.

 
Testing of AI systems vs. Traditional (non-AI) Systems

• AI Quality Characteristics.

 
Quality Characteristics for Evaluating AI Systems

• Extended Quality Characteristics Specific to AI.

 
Chapter 3 – Offline Testing of AI Systems

• Data Preparation and Pre-processing.

 
Steps of Data Preparation and Pre-processing

• Data Preparation.
• Processing of Unstructured Data (Images).
• Processing of Unstructured Data (Text).
• Data Imputation.
• Data Visualization.
• Anomaly/Outliers Detection.
• Outliers Detection Techniques.
• Dimensionality Reduction.
• Metrics.

Role of Metrics

• Metrics for Supervised and Unsupervised Learning.
• Inertia and Adjusted Rand Score.
• Support, Confidence and Lift metrics.
• Confusion Matrix.
• Accuracy, Precision, Recall, Specificity and F1-Score.
• RMSE and R-Square.
• Model Evaluation.

 
Training, Validation and Testing Datasets

• Under fitting and Overfitting.
• Cross-validation methods.
• Analytics.

 
Types of Analytics

Chapter 4 – Online Testing of AI Systems

• Architecture of an AI application.

 
Components of an Intelligent Application and their Testing Needs

• Interaction of AI and Non-AI Parts.

 
Linguistic Analysis Test Design Method

• Linguistic Analysis Test Design Method.

 
Testing AI Systems.

• Test a Chatbot.

Chapter 5 – Explainable AI

• Explainable AI (XAI).

 
Explainable AI and its Need

• LIME.

CAM for Neural Networks.

 
Chapter 6 – Risks and Test Strategy for AI Systems

• Risks in testing AI.

 
Risks of Testing AI Systems

• Risk of Using Pre-Trained Models.
• Risk of Concept Drift (CD).
• Challenges of AI Test Environment.
• Test Strategy.

 
Test Strategy for Testing AI Applications
 
Chapter 7 – AI in Testing

• AI for Software Testing Life Cycle (STLC).

 
AI for STLC Methods

• AI for Reporting and Smart Dashboards.

 
AI Based Automation tools

• Tools.

DURATION: 5 Day (40 hours).
Time Division (Break: 15 + 45 + 15 mines).
 
Course Outcomes:

• Understand the fundamentals of Data Science and Machine Learning.
• Analyse and pre-process data proficiently using Python.
• Apply Supervised Machine Learning techniques for regression and classification.
• Apply Unsupervised Machine Learning for clustering and natural language.
• Introduction to Deep learning concepts.

 
Important Note:

• Courseware – Reference material/ppt along with lab files/exercises will be provided.

 
Module 1: Introduction to Data Science & Machine Learning:

• Need for Data Science and Machine Learning.
• Types of Analytics.
• Lifecycle of a Data Science project.
• Skills for a Data Scientist role.
• Types of Machine Learning.

 
Module 2: Python for Data Analysis & Pre-processing:

Introduction to Python

• Python Libraries – NumPy, Pandas, matplotlib, Seaborn scikit-learn, Tensor Flow, Keras, Pytorch.
• Exploratory Data Analysis (EDA).
• Data Cleaning Techniques, Handling Missing Data, Handling Categorical Data.
• Introduction to EDA, 2D Scatter-plot, 3D Scatter-plot, Pair plots.
• Univariate, Bivariate, and Multivariate Analysis, Box-plot.

 
Data Pre-Processing

• Need for Data Pre-Processing.
• Handling Missing Values.
• Label-Encoding for Categorical Data.
• Hot-Encoding for Categorical Data Explained.

 
Data Transformation

• Need for Data Transformation.
• Concept of Data Normalization.
• Data Normalization Techniques – Standard Scalar & Minmax.
• Train, Test & Validation of Data.

 
Module 3: Supersized Machine Learning – Regression

Simple Linear Regression

• Concept of Linear Regression.
• Ordinary Least Square and Regression Errors.
• Data Processing & Train and Test of Model.
• Model Evaluation Parameters like R-squared, Score, RMSE and their Interpretations.
• Prediction Plot & its Interpretation.
• Hands-on Problem.

Multiple Linear Regression

• Concept of Multiple Linear Regression.
• Degrees of Freedom.
• Adjusted R-Squared.
• Assumptions of Multiple Linear Regression – Linearity, Multicollinearity, Autocorrelation,.
• Indigeneity, Normality of Residuals, Homoscedasticity, etc..
• Concept of time-lag data in Autocorrelation.
• Concept of Dummy variable trap.
• Hands-on Problem.

 
Module 4: Supervised Machine Learning – Classification

Logistic Regression

• Concept of Logistic Regression.
• Concept of Stratification.
• Concept of Confusion Matrix.
• Hands-on Problem.

 
Support Vector Machine (SVM)

• Common Sensical Intuition of SVM.
• Mathematical Intuition of SVM.
• Different types of SVM Kernel Functions.
• Hands-on Problem (Preferred: IRIS Classification Problem).

 
Decision Tree Classifier

• Decision Tree Classifier.
• Optimal Model Selection Criterion in Decision Tree.
• Hands-on Problem.

 
Random Forest Classifier

• Ensemble Learning and Random Forests.
• Bagging and Boosting.
• Hands-on Problem.

 
Evaluation Metrics for Classification Models

• Need for Evaluation and Accuracy Paradox.
• Different Measures for Classification Models – Accuracy, Precision, Recall, F1 Score, etc.
• Threshold and Adjusting Thresholds.
• AUC ROC Curve.
• Hands-on Problem.

 
Module 5: Feature Selection and Dimensionality Reduction
 
Univariate Feature Selection

• Feature Selection Importance.
• Concept of Univariate Feature Selection.
• F-Test for Regression and Classification.
• Hands on F-test (p value analysis).
• Chi-Squared for Classification.
• Feature Selection Techniques – Select Best, Select Percentile & Generic Univariate Select.
• Hands-on Chi-squared (p value analysis).

 
Recursive Feature Elimination (RFE)

• Concept of Recursive Feature Elimination (RFE).
• Feature Importance Score/Feature Ranking.
• Hands-on RFE.

 
Principle Component Analysis (PCA)

• Need to reduce dimensions and Importance of PCA.
• Mathematical Intuition of PCA & Steps to calculate PCA.
• Hands-on PCA (Model Comparisons with PCA & without PCA recommended).

 
Module 6: Cross validation & Hyper parameter Tuning
 

• Cross Validation.
• Importance of Cross Validation.
• Parameter & Implementation of Cross Validation.
• Hands-on Problem (Drawing inference from results).

 
Hyper parameter Tuning

• Concept of Hyper parameter Tuning.
• Grid Search & Randomized Search.

• Hands-on GridSearchCV (analyse results).

 
Module 7: Supervized Machine Learning – Natural Language Processing

• Introduction to NLP.
• Basic Concepts of NLP: Tokenization, stop words, Stemming, Lemmatization, etc.

• Tfidf Vector and its mathematical intuition.

• Recommendation system example.

 
Module 8: Supervized Machine Learning – Clustering

• Introduction to Clustering.
• Mathematical intuition behind cluster formation.

• Elbow method & its mathematical intuition.

• K-means Clustering Implementation (numerical).

• K-means Clustering Implementation (natural language processing).

• Introduction to Clustering.

 
Module 9: Introduction to Deep Learning

• Need & Applications of Deep Learning.
• Working of Artificial Neural Network.
• Backend (Tensor Flow) & Frontend (Keras).
• Concept of Tensor.
• Keras Model Building Overview – Construct, Compile & Evaluate.
• Activation Function.
• Loss Functions.
• Optimization Techniques.
• Evaluation metrics for Deep Learning.

DURATION: 8 Day (40 hours)
 
Note: client need to have their own ChatGPT Plus subscription
 
Chapter 01: Generative AI & Prompting Techniques (20 minutes):

• Why is Generative AI imp?.
• Importance of Prompting & Techniques.
• Prompts for Excel/Power Point.
• Quiz 01: Prompting Techniques.

 
Chapter 02: Text Prompting for Construction Industry:

Chapter 03: Image Prompting for Construction Industry:

 
Chapter 04: Responsible Prompting for Construction Industry:

DURATION: 5 Day (40 hours)
 
Target Audience:
Data Analyst, Business Analysts, Data Scientist.
 
Course Outcomes:

• Master Python fundamentals for data manipulation and analysis.
• Explore data types, control flows, and operators in Python.
• Gain proficiency in data pre-processing and cleaning techniques.
• Perform exploratory data analysis using Pandas and NumPy.
• Develop skills in data visualization with Matplotlib.

 
Module: Introduction to Python and Basics:

• Definition & Applications.
• Features, Versions & Working.
• Anaconda & Different IDEs for Python.
• Introduction to IDE’s – Jupiter Notebook, Spider & Google Colab.

 
Module: Data Types and Control Flows:

• Literals, reserved words and input functions.
• Data Types: into, float, bool, star.
• Decision Control Flows: If / Nested If / If-else / If-elif-else.
• Control Flow Loops: While loop, For loop, While-else, For-else.
• Operators: Arithmetic, Relational or Comparison, Logical.

 
Module: Lists, Tuples, Sets, and Dictionaries:

• Bitwise Operators, Assignment Operators, Ternary Operator.
• List: Ways of Accessing Values, Traversing Elements, List Operations, List Methods, Membership Operator, List Comprehension.
• Tuples: Creating Tuples, Ways of Accessing Values, Tuple Vs Immutability, Tuple Comprehension.
• Sets: Creating Sets, Ways of Accessing Values, Manipulating and Accessing Sets, Set Operations.
• Dictionary: Why Dictionary, creating a Dictionary, Accessing Values, Updating Dictionaries, Functions of Dictionary.

 
Module: File Handling and Strings:

• File Handling: Types of Files, Opening and Closing Files, Writing, Appending, and Reading Files.
• Strings: String Literals, Single (”) & Double Quotes (“”), Triple Quotes (”’), Raw Strings (“r’…’ “) and Operations on strings.
• Dictionary: Accessing Values, Updating Dictionaries, Functions of Dictionary.

 
Module: Iterators & Generators:

• Iterator vs Inerrable, Containers, Generators in Python.

 
Module: Regular Expressions:

• Uses of Regular Expressions – Text Analytics, import re, Character Classes, Backslash, Alteration, Quantifiers.

 
Module: OOPS Concept:

• Class, Classes and Object, Creating Object, Accessing Objects, Need and Use of Self, Class Method, __in it__() constructor.

 
Module: Introduction to NumPy, Pandas & Matplotlib:

• Introduction to NumPy, Install NumPy.
• Array Creation, Array Reshaping, Indexing, Operations.
• Introduction to Pandas, Slicing Data, Slicing Data Frame.
• Data Visualization with Matplotlib.

 
Module: Introduction to Data Pre-processing:

• Filtering Data Frame, Transforming Data Frame, Advanced Indexing.

Data Cleaning & Data Pre-processing.

 
Module: Exploratory Data Analysis (EDA):

• Data Cleaning Techniques, Handling Missing Data, Handling Categorical Data.
• Introduction to EDA, 2D Scatter-plot, 3D Scatter-plot, Pair plots.
• Univariate, Bivariate, and Multivariate Analysis, Box-plot.
• Variance and Standard Deviation, Median, IQR (Interquartile Range).

 
Advanced Pandas and Data Visualization:

• Advanced Pandas, Advanced Indexing, Data Preparation.
• Handling Missing Data, handling Categorical Data, Data Cleaning.

 
Data Visualization:

• Introduction to Data Visualization, Plotting with Matplotlib.
• Scatter Plots, Line Plots, Bar Plots, Pie Charts, Heat maps.

 
Project Work:

• Problem Statement, Data Collection, Data pre-processing (Exploratory Data Analysis), Feature Engineering (optional), Data visualizations (Pandas, NumPy & Matplotlib), Project Final Outcome & findings.

Modul:CNN
Introduction to CNN, Relu layer, pooling, flattening, Full connections.

 
Modul:CNN
Building CNN models, accuracy of Models, Image classification using CNN.

 
Modul:RNN
Introduction to RNN, Vanishing Gradient Problem, LSTM.

 
Modul:RNN
Building RNN models, accuracy of Models, Forecasting using RNN.

 
Modul:RBM
Introduction to RBM, Energy based model, contrastive divergence.

 
Modul:RBM
Building Boltzmann machine, Machine evaluation, Case study.