Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Language
English
Taught by
Peter Alkema, Irlon Terblanche
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Artificial Intelligence & Machine Learning: Practical Training for Real-World Applications & Skills Development
Unlock the Future: Dive into the World of AI and ML!
Welcome to an extraordinary journey into the realms of Artificial Intelligence and Machine Learning. Led by AI and Technology expert Irlon Terblanche, this course is not just an educational experience; it's an adventure into the technologies shaping our future. Whether you're a curious beginner, a business leader, or an aspiring tech guru, this course promises to transform your understanding of some of the most cutting-edge topics in tech.
Why This Course?
Designed for Curiosity and Career: Tailored for both personal and professional growth, this course demystifies AI and ML, making them accessible to everyone. It's perfect for busy professionals, entrepreneurs, and anyone with a thirst for knowledge.
No Math Fears: We've designed the course to be inclusive, requiring no prior expertise in math or coding. It's all about understanding concepts in a friendly, approachable manner.
Lifetime Access and Flexible Learning: Learn at your pace with full lifetime access to all resources, including videos, articles, and downloadable materials.
What You'll Achieve:
Grasp the Core Concepts: Understand the difference between AI, ML, and Deep Learning. Learn what sets them apart and how they're revolutionizing industries.
Debunk Myths: Discover why systems like ChatGPT aren't truly intelligent and explore the limitations of current AI technologies.
Practical Skills: Gain hands-on experience with tools like Microsoft's Model Builder and ML .Net. Understand the complete machine learning process, from data preparation to model evaluation.
Real-World Applications: See how AI and ML are being applied in various sectors. Discuss their impact on job markets and skill requirements.
Course Highlights:
Engaging Video Lectures: Over 4 hours of high-quality, engaging video content that breaks down complex ideas into digestible segments.
Comprehensive Topics: From the basics of neural networks to the intricacies of supervised and unsupervised learning.
Practical Demonstrations: Learn by doing with practical exercises and demonstrations.
Dynamic Learning Resources: An article and a downloadable resource to complement your learning journey.
Mobile and PC Access: Learn on the go or from the comfort of your living room.
Course Structure:
The course is divided into 9 comprehensive sections, each designed to build upon the last, ensuring a smooth learning curve. Starting with an introduction to AI and ML, it moves through various topics like function approximation, neural networks, and deep learning, concluding with practical demonstrations of machine learning in action.
Enroll Now and Transform Your Understanding of AI and ML!
Join us on this captivating journey into AI and ML. With Irlon Terblanche's expert guidance, engaging content, and practical insights, you're not just learning; you're preparing for the future. Enroll today and be part of the AI revolution!
Course Content
- 120 section(s)
- 605 lecture(s)
- Section 1 Introducing the first half of this course: AI and Machine Learning for Beginners
- Section 2 What is Artificial Intelligence?
- Section 3 What is Machine Learning?
- Section 4 Deep Learning and Neural Networks
- Section 5 Artificial Intelligence Insights and Fundamentals
- Section 6 AI Applied to Personalized Recommenders in eCommerce, Loyalty and other settings
- Section 7 Bonus: Ecosystem.ai & X-idian Webinar- Personalized Rewards & Loyalty Programmes
- Section 8 Test your knowledge now to achieve your goals!
- Section 9 Introducing the next part of this course: Practical AI with Model Builder.
- Section 10 Visual Studio and Model Builder
- Section 11 Model Builder and the Machine Learning Process
- Section 12 Machine Learning Demo with Model Builder
- Section 13 Course Summary: Let's recap the amazing work you've done in this course!!
- Section 14 Bonus: Live Gen AI Presentation to Institute of Risk Managers South Africa
- Section 15 Test your knowledge now to achieve your goals!
- Section 16 Introduction to Collaborative AI
- Section 17 Foundations of Human-AI Collaboration
- Section 18 AI Tools and Technologies for Teams
- Section 19 Leadership in the Age of AI
- Section 20 AI-Enhanced Decision Making
- Section 21 Enhancing Productivity with AI
- Section 22 Training and Upskilling for AI Integration
- Section 23 Managing AI Projects
- Section 24 Human-Centric AI Design
- Section 25 AI in Customer Service and Support
- Section 26 AI for Marketing and Sales Teams
- Section 27 AI and Human Resources
- Section 28 AI in Financial Management
- Section 29 AI in Operations and Supply Chain
- Section 30 Overcoming Challenges in AI Adoption
- Section 31 AI Ethics and Responsibility
- Section 32 Future Trends in AI and Team Collaboration
- Section 33 Building Resilient AI-Enhanced Teams
- Section 34 Measuring Success in AI Integration
- Section 35 Continuous Improvement and Innovation
- Section 36 Your Assignment: Write down goals to improve your life and achieve your goals!!
- Section 37 Introduction to Artificial Intelligence
- Section 38 Fundamentals of Machine Learning
- Section 39 Advanced Topics in Machine Learning
- Section 40 Introduction to Neural Networks
- Section 41 Training Neural Networks
- Section 42 Advanced Neural Network Architectures
- Section 43 Predictive Analytics and Forecasting
- Section 44 Practical Applications of Predictive AI
- Section 45 Natural Language Processing (NLP)
- Section 46 AI in Decision Making
- Section 47 AI in Customer Insights
- Section 48 AI in Automation
- Section 49 AI in Supply Chain Management
- Section 50 AI in Manufacturing
- Section 51 AI in Healthcare
- Section 52 AI in Marketing
- Section 53 17: AI in Sales
- Section 54 AI in Finance
- Section 55 AI in Cybersecurity (Part 1)
- Section 56 AI in Cybersecurity (Part 2)
- Section 57 Your Assignment: Write down goals to improve your life and achieve your goals!!
- Section 58 Introduction to Brand Management and Generative AI Concepts
- Section 59 Building Brand Identity with Generative AI Technologies
- Section 60 Developing Brand Positioning with the Aid of AI Tools
- Section 61 Generative AI in Creating Compelling Brand Narratives
- Section 62 AI in Understanding and Adapting to Consumer Preferences
- Section 63 Enhancing Visual Branding with Generative AI Creativit
- Section 64 Generative AI and Brand Engagement Strategies
- Section 65 Real-Life Case Studies: AI in Brand Management Success
- Section 66 Brand Consistency and AI-Generated Content Management
- Section 67 AI in Monitoring Brand Perception and Sentiment Analysis
- Section 68 Generative AI in Digital Marketing and Brand Amplification
- Section 69 Generative AI in Global Brand Management Strategies
- Section 70 Ethical Considerations of Using Generative AI in Branding
- Section 71 Generative AI and Personalization in Brand Messaging
- Section 72 Generative AI for Brand Loyalty Programs and Customer Experience
- Section 73 Generative AI in Competitive Brand Analysis and Differentiation
- Section 74 AI for Crafting Brand Crisis Management Strategies
- Section 75 Using AI for Evaluating Brand Campaign Performance
- Section 76 Adopting AI in Branding Teams and Creative Processes
- Section 77 The Future of Brand Management with Generative AI
- Section 78 Your Assignment: Write down goals to improve your life and achieve your goals!!
- Section 79 Introduction to Customer Experience and Generative AI
- Section 80 Fundamentals of Generative AI and Its Applications
- Section 81 Enhancing Customer Experience with AI-Powered Content
- Section 82 AI Chatbots and Their Role in Customer Interaction
- Section 83 Personalization in Marketing Using Generative AI
- Section 84 Creating AI-Driven Customer Recommendation Systems
- Section 85 Real-Life Case Studies: AI Transforming Customer Experience
- Section 86 Using Generative AI to Analyze Customer Data
- Section 87 Building Emotional Connections with AI Customer Interactions
- Section 88 Generative AI in Omni-Channel Customer Experience
- Section 89 Personalizing E-Commerce Experiences with Generative AI
- Section 90 Leveraging AI for Dynamic Customer Segmentation
- Section 91 Overcoming Challenges in AI Customer Experience Solutions
- Section 92 Integrating AI Chatbots in Customer Support Ecosystems
- Section 93 Generative AI in Improving Customer Loyalty and Retention
- Section 94 Ethical Considerations in AI Customer Engagement
- Section 95 Enhancing Content Marketing Strategies with Generative AI
- Section 96 Measuring the Success of AI-Driven Customer Experience
- Section 97 Collaborating with AI for Better Customer Experience Outcomes
- Section 98 Preparing for the Future of Customer Experience with AI
- Section 99 Your Assignment: Write down goals to improve your life and achieve your goals!!
- Section 100 Understanding Ethics in Business AI
- Section 101 Data Governance and Ethics
- Section 102 Transparency and Explainability in AI
- Section 103 Mitigating Bias in AI Applications
- Section 104 Responsible AI Implementation
- Section 105 Ethical Challenges in AI Decision-Making
- Section 106 Ethical AI Governance Models
- Section 107 Addressing Ethical Concerns in AI Adoption
- Section 108 Ethical Leadership in AI-driven Organizations
- Section 109 Ethical AI Impact Assessment
- Section 110 Ensuring Ethical Considerations in AI Procurement
- Section 111 Ethical AI Use in Financial Decision-Making
- Section 112 Ethical Marketing Practices in AI
- Section 113 Ethical Implications of AI in Human Resources
- Section 114 Ethical AI in Supply Chain Management
- Section 115 Ethics in AI-enabled Customer Service
- Section 116 Legal and Ethical Aspects of Business AI
- Section 117 Ethical AI Policies and Guidelines
- Section 118 Ethical AI in Crisis Management
- Section 119 Ethical AI Leadership Reflections
- Section 120 Write down goals to improve your life and achieve your goals!
What You’ll Learn
- Demonstrate a solid understanding of the difference between AI, Machine Learning and Deep Learning.
- Clearly articulate why Large Language Models like ChatGPT and Bard are NOT intelligent.
- Articulate the difference between Supervised, Unsupervised, and Reinforcement Machine Learning.
- Explain the concept of machine learning and its relation to AI.
- Define artificial intelligence (AI) and differentiate it from human intelligence.
- Describe what Artificial Intelligence is, and what it is not.
- Explain what types of sophisticated software systems are not AI systems.
- Describe how Machine Learning is different to the classical software development approach.
- Compare and contrast supervised, unsupervised, and reinforcement learning.
- Explain Supervised and Unsupervised Machine Learning terms such as algorithms, models, labels and features.
- Explain Function Approximators and the role of Neural Networks as Universal Function Approximators.
- Explain Encoding and Decoding when using machine learning models to work with non-numeric, categorical type data.
- Demonstrate an intuitive understanding of Reinforcement Learning concepts such as agents, environments, rewards and goals.
- Identify examples of AI in everyday life and discuss their impact.
- Evaluate the effectiveness of different AI applications in real-world scenarios.
- Apply basic principles of neural networks to a hypothetical problem.
- Discuss the role of data in training AI models
- Construct a neural network model for a specified task
- Assess the impact of AI on job markets and skill requirements
- See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net.
- Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation.
- Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization.
- Understanding the impact of evaluation metrics on model performance, and how to check for overfitting.
- Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use.
- Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration.
- Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance.
- Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to demonstrate the machine learning process.
- Learn how to use Model Builder to train models without having to code.
Skills covered in this course
Reviews
-
SSomnath Banerjee
Quality of content is good, comprehensive.
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PPhilip Aberion
Very informative. I like the way it was being presented. It is very practical, and jargons are understandable.
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FFikreselam Kassa
interesting
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EElder Cama Arestegui
OK
