Overview of the Course
This foundational program provides a comprehensive introduction to the core pillars of Artificial Intelligence, offering participants a robust understanding of Machine Learning, Data Science, and Cognitive Computing. The curriculum is designed to demystify AI Algorithms, Predictive Analytics, and Automation, ensuring that learners can distinguish between Narrow AI and General AI. By exploring the intersection of Big Data and computational logic, this course establishes the essential technical and conceptual groundwork required to navigate the rapidly evolving Digital Transformation landscape.
The course covers a broad spectrum of topics, starting with the history and logic of intelligent systems. Participants will delve into various AI paradigms, including symbolic AI, statistical learning, and neural networks. Special attention is given to the practical application of AI in business, the lifecycle of an AI project, and the vital ethical considerations surrounding algorithmic bias and data privacy.
Who should attend the training
- Business Analysts and Strategists
- Project Managers and Team Leads
- Entry-level Data Professionals
- Non-technical Executives seeking AI literacy
- IT Professionals transitioning into AI roles
- Operations and Supply Chain Managers
Objectives of the training
- To define the core concepts and various sub-fields of Artificial Intelligence.
- To understand the role of data as the primary fuel for intelligent systems.
- To identify business problems that can be solved using AI and Machine Learning.
- To recognize the different types of learning: supervised, unsupervised, and reinforcement.
- To evaluate the ethical implications and societal impact of AI deployment.
Personal benefits
- Build a future-proof career by gaining literacy in the world's most disruptive technology.
- Develop the ability to communicate effectively with technical AI teams.
- Gain a conceptual framework to identify automation opportunities in your daily work.
- Acquire a recognized certification in foundational AI principles.
Organizational benefits
- Bridge the communication gap between business units and technical departments.
- Empower employees to identify high-value AI use cases across the value chain.
- Reduce risks associated with unethical or poorly planned AI implementations.
- Foster a culture of innovation and data-driven strategy.
Training methodology
- Conceptual lectures supported by real-world business case studies
- Interactive group discussions on AI ethics and policy
- Hands-on demonstrations of AI tools and low-code platforms
- Guided walkthroughs of the AI project development lifecycle
- Collaborative brainstorming sessions for industry-specific AI applications
Trainer Experience
Our trainers are expert consultants with extensive experience in AI strategy and implementation. They have guided numerous organizations through the complexities of adopting intelligent systems and possess a unique ability to translate complex technical concepts into actionable business insights.
Quality Statement
We are dedicated to providing clear, accurate, and high-impact training. Our content is meticulously curated to provide a balanced view of AI—highlighting both its immense potential and its practical limitations—to ensure learners walk away with a realistic and functional understanding of the field.
Tailor-made courses
We offer customized versions of this course designed to focus on specific industry verticals such as Finance, Healthcare, or Manufacturing. These tailored sessions ensure that the examples and practical exercises are directly relevant to your organization's specific operational environment.
Course duration: 5 days
Training fee: USD 1500
Module 1: Introduction to the AI Ecosystem
- Definition of AI and the distinction between Strong AI and Weak AI
- The history of AI: From the Turing Test to modern Generative AI
- Overview of sub-fields: Machine Learning, Deep Learning, and NLP
- Real-world applications of AI in everyday consumer technology
- Current trends, market drivers, and the future outlook of AI
- Practical session: Conducting an AI audit of common workplace tools to identify embedded intelligence
Module 2: Logic and Problem Solving in AI
- Introduction to Search Algorithms and their role in pathfinding
- Understanding Knowledge Representation and Expert Systems
- The role of Heuristics in streamlining complex decision-making
- Introduction to Logic-based AI and Symbolic Reasoning
- Comparative analysis of Human Intelligence vs. Machine Logic
- Practical session: Designing a decision tree to automate a standard business approval process
Module 3: Data: The Foundation of Intelligent Systems
- The critical role of Big Data in training modern AI models
- Understanding data types: Structured, Unstructured, and Semi-structured
- The importance of Data Quality, Cleaning, and Pre-processing
- Overview of Data Governance and the lifecycle of data in AI
- Introduction to Feature Engineering and selecting relevant variables
- Practical session: Performing a data quality assessment on a sample business dataset
Module 4: Understanding Machine Learning Paradigms
- Supervised Learning: Understanding labels, features, and predictive outcomes
- Unsupervised Learning: Discovering hidden patterns through clustering
- Reinforcement Learning: The concept of agents, rewards, and environments
- Regression vs. Classification: Choosing the right model for the problem
- Introduction to popular algorithms like Linear Regression and K-Means
- Practical session: Mapping specific business problems to the appropriate Machine Learning paradigm
Module 5: Exploring Neural Networks and Deep Learning
- Introduction to the Perceptron: The fundamental building block of Neural Nets
- Understanding how hidden layers enable complex pattern recognition
- The concept of training models through weights, biases, and backpropagation
- Overview of Deep Learning architectures and why they require high compute power
- Distinguishing between traditional Machine Learning and Deep Learning
- Practical session: Using a "no-code" neural network simulator to visualize model learning
Module 6: Natural Language Processing and Communication
- Fundamentals of how machines process and understand human language
- Key tasks in NLP: Sentiment analysis, translation, and summarization
- The evolution of language models from simple chatbots to LLMs
- Understanding Speech-to-Text and Text-to-Speech technologies
- Challenges in NLP: Context, sarcasm, and linguistic nuances
- Practical session: Testing and refining a sentiment analysis tool on customer feedback data
Module 7: Computer Vision and Image Recognition
- Introduction to how machines "see" and interpret visual data
- Core concepts of Image Classification and Object Detection
- Applications of Computer Vision in medical imaging and security
- Understanding the basics of facial recognition and its technical hurdles
- The role of Convolutional Neural Networks in processing pixels
- Practical session: Using a pre-trained computer vision API to identify objects within a series of images
Module 8: Robotics and Autonomous Systems
- The intersection of Physical Robotics and Artificial Intelligence
- How AI powers perception and navigation in autonomous vehicles
- Understanding Industrial Automation vs. Collaborative Robots (Cobots)
- The role of sensors and IoT in providing real-time data to AI agents
- Challenges in edge computing and real-time decision making
- Practical session: Simulating a basic logic sequence for an autonomous warehouse robot
Module 9: AI Strategy and Project Management
- Identifying and prioritizing AI use cases based on ROI and feasibility
- Understanding the AI project lifecycle: From data collection to deployment
- Build vs. Buy: Strategic considerations for acquiring AI capabilities
- Key roles in an AI team: Data Scientists, Engineers, and Subject Matter Experts
- Measuring success: Key Performance Indicators (KPIs) for AI projects
- Practical session: Developing a "Lean AI Canvas" for a proposed project in your department
Module 10: Ethics, Governance, and Responsible AI
- Understanding Algorithmic Bias and the importance of diversity in data
- Transparency and Explainability: Why the "Black Box" problem matters
- Data Privacy and Security: Navigating regulations like GDPR and AI Acts
- The impact of AI on the future of work and labor markets
- Frameworks for implementing Ethical AI within an organization
- Practical session: Group debate and risk assessment of a controversial AI implementation scenario
Requirements:
- Participants should be reasonably proficient in English.
- Applicants must live up to Armstrong Global Institute admission criteria.
Terms and Conditions
1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free
2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.
3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.
4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.
5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.
Booking for Training
Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.
Or call us on +254720272325 / +254725012095 / +254724452588
Payment Options
We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:
1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.
2. Invoice: We can send a bill directly to you or your company.
3. Deposit directly into Bank Account (Account details provided upon request)
Cancellation Policy
1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.
2. Participants may cancel attendance 14 days or more prior to the training commencement date.
3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.
Tailor Made Courses
This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com
Accommodation and Airport Transfer
Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588
