Fundamentals of Artificial Intelligence Training Course

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 Phoenix Training Center 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@phoenixtrainingcenter.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@phoenixtrainingcenter.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@phoenixtrainingcenter.com or on Tel: +254720272325 / +254725012095 / +254724452588