• Real-world AI implementation strategies
• Latest trends in AI and machine learning
• AI’s impact on decision-making processes
• Ethical considerations in AI deployments
• Enhancing customer experience through AI

Cost: 4250 USD

Duration: 5 Days

Cost: 4250 USD

Duration: 5 Days

• Introduction to AI concepts and their applications in finance
• Using machine learning and predictive analytics for financial forecasting and trend analysis
• Automating financial data processing, anomaly detection, and reporting
• Enhancing risk assessment, credit analysis, and investment decision-making through AI models

• Implement various machine learning techniques, such as regression, classification, and clustering, to solve real-world financial problems.
• Understand the fundamentals of AI and its applications in financial analysis
• Learn how machine learning, deep learning, and predictive analytics can improve financial forecasting
• Apply AI techniques to automate financial data processing and enhance reporting accuracy
• Use AI-driven tools to detect anomalies, identify risks, and strengthen fraud prevention
• Analyse market trends and consumer behaviour through AI-powered insights

Cost: 4250 USD

Duration: 5 Days

• To understand the structure and purpose of ISO/IEC 42001
• To gain insight into the development and evolution of AI governance standards
• To explore the integration of ISO/IEC 42001 with existing management systems
• To learn how to implement and audit an Artificial Intelligence Management System (AIMS)
• To identify and mitigate risks associated with AI implementation
• To ensure ethical, safe, and compliant use of AI within organisational contexts
• To develop strategies that add value and enhance decision-making using ISO/IEC 42001

Cost: 4250 USD

Duration: 5 Days

• To understand the structure and purpose of ISO/IEC 42001
• To gain insight into the development and evolution of AI governance standards
• To explore the integration of ISO/IEC 42001 with existing management systems
• To learn how to implement and audit an Artificial Intelligence Management System (AIMS)
• To identify and mitigate risks associated with AI implementation
• To ensure ethical, safe, and compliant use of AI within organisational contexts
• To develop strategies that add value and enhance decision-making using ISO/IEC 42001

Cost: 4250 USD

Duration: 5 Days

• Understand the key principles and requirements of ISO/IEC 42001:2023.
• Develop strategies for establishing and implementing an AIMS within their organisations.
• Gain practical skills to identify, assess, and manage AI-related risks and opportunities.
• Learn how to ensure compliance with legal, regulatory, and ethical standards governing AI technologies.
• Explore best practices and case studies for successful AIMS implementation.

Cost: 4250 USD

Duration: 5 Days

Cost: 4250 USD

Duration: 5 Days

• Understand the architecture and principles of deep neural networks.
• Implement deep learning models using libraries such as TensorFlow and PyTorch.
• Apply deep learning techniques to solve business challenges in areas such as image classification and sentiment analysis.
• Fine-tune pre-trained models for specific business domains and use cases.
• Explore advanced topics in deep learning, including generative adversarial networks (GANs) and reinforcement learning.
• Develop strategies for deploying and scaling deep learning solutions in production environments.

• Fundamentals of Neural Networks
• Introduction to TensorFlow and Keras
• Hands-on: Building Your First Neural Network

• Understanding CNN Architecture
• Image Classification with CNNs
• Case Studies: CNN Applications in Business

• Introduction to RNNs and LSTM Networks
• Natural Language Processing (NLP) with RNNs
• Hands-on: Sentiment Analysis with RNNs

• Introduction to GANs and Reinforcement Learning
• Case Studies: Advanced Deep Learning Applications
• Deploying Deep Learning Models in Production Environments.

Cost: 5995 USD

Duration: 4 Days

• Understand the strategic importance of AI for business competitiveness and growth.
• Develop an AI strategy aligned with organisational goals, priorities, and capabilities.
• Assess organisational readiness for AI adoption, including data infrastructure, talent, and culture.
• Plan and execute AI projects using best practices and methodologies, managing risks and uncertainties effectively.
• Monitor and measure the impact of AI initiatives on business performance, iterating and refining strategies as needed.
• Foster a culture of innovation and continuous learning, driving organisational transformation and agility.

• AI Landscape: Trends and Opportunities
• Developing an AI Strategy Aligned with Business Goals
• Case Studies: Successful AI Transformation Stories

• Evaluating Data Infrastructure and Talent
• Identifying AI Use Cases and Priorities
• Overcoming Barriers to AI Adoption

• Developing AI Roadmaps and Implementation Plans
• Project Management Best Practices for AI Initiatives
• Building Cross-functional Teams for AI Projects

• Measuring AI Impact: KPIs and Metrics
• Continuous Improvement and Iteration
• Closing Remarks: Building a Culture of AI Innovation.

Cost: 5995 USD

Duration: 4 Days

• Identify key AI applications in various industries.
• Understand the core principles and limitations of machine learning.
• Define rational agents and their decision-making processes.
• Grasp the basics and applications of fuzzy logic.
• Gain an Overview of robotics and AI integration.
• Comprehend key concepts and challenges in NLP.
• Understand the fundamentals and applications of neural networks.

• AI in healthcare, finance, transportation, entertainment
• Real-world use cases and societal impact
• Strengths and Limitations of Machine Learning
• Core principles of machine learning
• Limitations: data dependency, interpretability

• Rational agents in AI
• Defining rationality in AI
• Implications for decision-making
• Fuzzy Logic
• Approximate reasoning with fuzzy logic
• Applications in uncertainty handling

• Introduction to robotics
• AI integration in robots
• Robot perception, motion planning, navigation

• Enabling machines to understand human language
• Key concepts: text analysis, language generation
• Challenges in conversational agents

• Neural networks as AIs backbone
• Brain-inspired models
• Uses: image recognition, speech processing, gaming.

Cost: 5995 USD

Duration: 5 Days

• Understand AI's capabilities and limitations within a business context.
• Develop strategies to leverage AI for competitive advantage.
• Identify the necessary skills and capabilities needed to support AI initiatives.
• Foster an AI-ready culture within organisations to facilitate seamless adoption.
• Measure and optimise the impact of AI through appropriate metrics and KPIs.
• Navigate ethical considerations and sustain responsible AI practices in business.

• Introduction to AI: Overview of AI technologies and their fundamental concepts
• AI in Operations: How AI can optimise operational efficiency, from manufacturing to services
• Interactive Session: Identifying AI opportunities in participants operations

• AI and Business Strategy: Integrating AI into business strategy for competitive advantage
• Case Studies: Real-world examples of strategic AI integration
• Workshop: Developing AI-aligned strategies for hypothetical businesses

• Skills Assessment: Identifying required AI skills and capabilities within a business
• Training Programs: Designing and implementing training for AI proficiency across roles
• Group Activity: Mapping out a skills development plan for an AI-driven business

• Cultural Change: Strategies for fostering an AI-ready culture in the workplace
• Leadership in AI: Role of top management in driving AI initiatives
• Discussion: Barriers to AI adoption and how to overcome them

• AI and ROI: Metrics and KPIs to measure AI impact
• Sustainability and Ethics: Ensuring responsible AI use in business practices
• Panel Discussion: Future of AI in business with industry experts

Cost: 5995 USD

Duration: 5 Days

• Understanding AI Technologies and Frameworks: Gain a comprehensive understanding of AI, Machine Learning (ML), and Deep Learning (DL) concepts, along with proficiency in using popular AI frameworks like TensorFlow, PyTorch, and Scikit-Learn.
• Application of Advanced AI Techniques: Acquire practical knowledge of advanced ML algorithms such as Neural Networks, Transfer Learning, and Natural Language Processing (NLP) to solve real-world problems across various industries.
• Deployment and Optimization Strategies: Learn techniques for deploying AI models in production environments and optimizing these models through strategies like hyperparameter tuning and model pruning for enhanced performance.
• Utilization of AI Services from Vendors: Explore and apply pre-built AI services from leading vendors (AWS, Azure, GCP) for practical implementations in domains like Computer Vision, Speech Recognition, and Recommendation Systems.
• Ethical Considerations in AI: Understand the ethical considerations associated with AI development and deployment, including addressing bias in algorithms, ensuring fairness, and leveraging open-source AI tools/platforms.
• Application of AI in Industry Contexts: Apply AI technologies specifically within industrial contexts, identifying challenges and opportunities across sectors like manufacturing, supply chain, logistics, and predictive maintenance.
• Future Trends and Continuous Learning: Explore emerging trends in AI, such as Explainable AI, AutoML, and AI in Edge Computing, while fostering an understanding of the impact of AI on industries and society. Additionally, access resources for continuous learning and staying updated in the evolving field of AI.

• Overview of AI, Machine Learning (ML), and Deep Learning (DL)
• Types of AI: Narrow vs. General AI
• Practical applications and industries benefiting from AI
• Overview of popular AI frameworks: TensorFlow, PyTorch, Scikit-Learn
• Hands-on: Setting up the environment and running basic ML/DL models using TensorFlow or PyTorch
• Case Study: Image Classification using TensorFlow or PyTorch

• Deep Dive into Neural Networks: CNNs, RNNs, and their applications
• Transfer Learning and its significance in various industries
• AI vendors and their specialised offerings (, IBM Watson, Google Cloud AI)
• Fundamentals of NLP and its applications (Sentiment Analysis, Named Entity Recognition)
• Hands-on: Text Generation or Sentiment Analysis using NLP tools (NLTK, spaCy)
• Case Study: Implementing NLP for Customer Sentiment Analysis using AWS Comprehend or Google Cloud Natural Language

• Techniques for deploying models in production environments
• Model optimisation approaches (Hyperparameter tuning, Model pruning)
• AI service providers and their deployment platforms (AWS, Azure, GCP)
• Hands-on: Utilizing pre-built AI services from vendors like AWS Rekognition, Azure Cognitive Services, or Google Cloud Vision API
• Case Study: Implementing a Recommendation System using AI services from AWS or Azure

• Ethical considerations in AI development and deployment
• Addressing bias in AI algorithms and ensuring fairness
• Open-source AI tools and platforms (TensorFlow Extended, Hugging Face Transformers)
• Emerging trends in AI (Explainable AI, AutoML, AI in Edge Computing)Emerging trends in AI (Explainable AI, AutoML, AI in Edge Computing)
• Discussion on the impact of AI on various industries and society
• Course Conclusion, Q&A, and Resources for Continuous Learning.

Cost: 6400 USD

Duration: 4 Days

Overview

Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.

Objective

• Solve business problems with AI and ML
• Prepare and process data for ML
• Train, evaluate, and tune ML models
• Build and deploy various ML models (regression, classification, clustering, neural networks)
• Operate and maintain ML models in production

Who Should Attend

This course is suitable for anyone looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decision-making products that bring value to the business

Outlines

Day 1: Solving Business Problems Using AI and ML including Preparing Data, Training, Evaluating, and Tuning a Machine Learning Model

• Identify AI and ML Solutions for Business Problems
• Formulate a Machine Learning Problem
• Select Approaches to Machine Learning
• Collect DataTransform DataEngineer Features
• Work with Unstructured Data
• Train a Machine Learning Model
• Evaluate and Tune a Machine Learning Model

Day 2: Building Linear Regression Models including, Building Forecasting Models

• Build Regression Models Using Linear Algebra
• Build Regularized Linear Regression Models
• Build Iterative Linear Regression Models
• Build Univariate Time Series Models
• Build Multivariate Time Series Models

Day 3: Building Classification Models Using Logistic Regression and k-Nearest Neighbor, including Building Clustering Models

• Train Binary Classification Models Using Logistic Regression
• Train Binary Classification Models Using k-Nearest Neighbor
• Train Multi-Class Classification Models
• Evaluate Classification ModelsTune Classification Models
• Build k-Means Clustering Models
• Build Hierarchical Clustering Models

Day 4: Building Decision Trees and Random Forests including Building Support-Vector Machines

• Build Decision Tree ModelsBuild Random Forest Models
• Build SVM Models for ClassificationBuild SVM Models for Regression

Day 5: Building Artificial Neural Networks including Operationalizing Machine Learning Models, Maintaining Machine Learning Operations

• Build Multi-Layer Perceptrons (MLP)
• Build Convolutional Neural Networks (CNN)
• Build Recurrent Neural Networks (RNN)
• Deploy Machine Learning Models
• Automate the Machine Learning Process with MLOps
• Integrate Models into Machine Learning Systems
• Secure Machine Learning Pipelines
• Maintain Models in Production

Cost: 5995 USD

Duration: 5 Days

Cost: 4900 USD

Duration: 5 Days

Cost: 4900 USD

Duration: 5 Days

Cost: 4900 USD

Duration: 5 Days

Cost: 4900 USD

Duration: 5 Days

Cost: 4900 USD

Duration: 5 Days

Cost: 4900 USD

Duration: 5 Days

• Perception
• Reasoning
• Learning
• Decision making
• Natural language processing.

• History of Artificial Intelligence
• What is Artificial Intelligence?
• Application of Artificial Intelligence
• Artificial Intelligence Intelligent Systems
• Problems in Artificial Intelligence
• Approaches used in Artificial Intelligence
• Tools used in Artificial Intelligence
• Different types of Artificial Intelligence Technology
• Agents and Environments in Artificial Intelligence
• Artificial Intelligence Expert Systems
• Artificial Intelligence Robotics
• What is strong and weak Artificial Intelligence?

• Business executives and managers who want to understand the potential of AI and how it can be leveraged to gain a competitive advantage in their industry.
• IT professionals who want to learn the fundamentals of AI and how it can be integrated into their projects.
• Data scientists and analysts who want to expand their knowledge and skills in AI and its subfields.
• Anyone who is interested in learning about AI and its impact on society. The course is designed for individuals with little or no prior knowledge of AI, but it can also serve as a refresher for those who have some familiarity with the topic.

• What is AI?
• Brief history of AI
• Different types of AI and their applications
• Key concepts and terminology

• Introduction to machine learning
• Supervised  unsupervised  and reinforcement learning.
• Classification and regression
• Overfitting and underfitting
• Model evaluation and selection

• Introduction to neural networks
• Structure and working of a neural network.
• Activation functions
• Convolutional neural networks (CNN) and their applications
• Recurrent neural networks (RNN) and their applications

• Introduction to NLP
• Tokenization  stemming  and lemmatisation.
• Part-of-speech tagging
• Named entity recognition.
• Sentiment analysis

• Emerging trends in AI
• AI and its impact on society
• Ethical considerations in AI development and use
• Bias and fairness in AI
• Explainable AI

Cost: 4900 USD

Duration: 5 Days