What does the A course cover?
A is covered here in 10 modules: Introduction to AI: History of AI and its evolution, Defining AI and its applications, Machine Learning Fundamentals: Model evaluation and selection, Deep Learning: Introduction to and neural networks and 7 more. The outline lists 40 specific topics, opening with defining AI and its applications and closing with future of AI: trends, challenges, and opportunities.
How do you approach A step by step?
The work is sequenced in 10 stages. It starts with Introduction to AI: History of AI and its evolution, Defining AI and its applications, moves through Machine Learning Fundamentals: Model evaluation and selection and Deep Learning: Introduction to and neural networks, and ends at AI Case Studies and Applications: AI in industry: retail, manufacturing, and more.
What is in Module 1 of the A course?
Module 1 is Introduction to AI: History of AI and its evolution, Defining AI and its applications. It works through defining AI and its applications, history of AI and its evolution, types of AI: narrow, general, and superintelligence and 1 more. It sets the vocabulary the remaining 9 modules build on.
How is the A course delivered?
The A course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the A course cost?
The A course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
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A Complete Guide to Building and Implementing AI Projects
Course Overview
This comprehensive course is designed to equip participants with the knowledge and skills needed to build and implement AI projects in real-world settings. With a focus on practical applications and hands-on learning, participants will gain a deep understanding of AI concepts, techniques, and tools.Course Features
- Interactive and Engaging: Learn through interactive lessons, quizzes, and hands-on projects
- Comprehensive and Personalized: Covering 80+ topics, tailored to your learning needs
- Up-to-date and Practical: Focusing on the latest AI trends and real-world applications
- High-quality Content and Expert Instructors: Learn from industry experts and thought leaders
- Certification: Receive a certificate upon completion, issued by The Art of Service
- Flexible Learning and User-friendly: Accessible on desktop, tablet, and mobile devices
- Community-driven and Lifetime Access: Join a community of learners and enjoy lifetime access to course materials
- Gamification and Progress Tracking: Track your progress and stay motivated with gamification elements
Course Outline
Module 1. Introduction to AI: History of AI and its evolution, Defining AI and its applications
- Defining AI and its applications
- History of AI and its evolution
- Types of AI: narrow, general, and superintelligence
- AI in industry: healthcare, finance, marketing, and more
Module 2. Machine Learning Fundamentals: Model evaluation and selection
- Introduction to machine learning and its types
- Supervised, unsupervised, and reinforcement learning
- Regression, classification, clustering, and dimensionality reduction
- Model evaluation and selection
Module 3. Deep Learning: Introduction to and neural networks
- Introduction to deep learning and neural networks
- Convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
- Long short-term memory (LSTM) networks and transformers
- Deep learning applications: computer vision, natural language processing, and more
Module 4. Natural Language Processing (NLP): Introduction to NLP and text processing
- Introduction to NLP and text processing
- Tokenization, stemming, and lemmatization
- Named entity recognition, sentiment analysis, and topic modeling
- NLP applications: chatbots, language translation, and text summarization
Module 5. Computer Vision: Introduction to and image processing
- Introduction to computer vision and image processing
- Image classification, object detection, and segmentation
- Convolutional neural networks (CNNs) for computer vision
- Computer vision applications: self-driving cars, facial recognition, and more
Module 6. Reinforcement Learning: Q-learning, SARSA, and deep Q-networks
- Introduction to reinforcement learning and Markov decision processes
- Q-learning, SARSA, and deep Q-networks
- Policy gradients and actor-critic methods
- Reinforcement learning applications: robotics, game playing, and more
Module 7. AI Ethics and Fairness: Bias and fairness in AI systems, Explainability and transparency in AI
- Introduction to AI ethics and fairness
- Bias and fairness in AI systems
- Explainability and transparency in AI
- AI ethics and fairness applications: facial recognition, predictive policing, and more
Module 8. AI Deployment and Integration: Cloud-based AI deployment: AWS, Azure, and Google Cloud
- Introduction to AI deployment and integration
- Cloud-based AI deployment: AWS, Azure, and Google Cloud
- Edge AI deployment: Raspberry Pi, Jetson Nano, and more
- AI integration with existing systems: APIs, microservices, and more
Module 9. AI Project Management: AI project team roles and responsibilities
- Introduction to AI project management
- AI project lifecycle: planning, development, deployment, and maintenance
- AI project team roles and responsibilities
- AI project management tools and techniques: Agile, Scrum, and more
Module 10. AI Case Studies and Applications: AI in industry: retail, manufacturing, and more
- Real-world AI case studies: healthcare, finance, marketing, and more
- AI applications: chatbots, virtual assistants, and more
- AI in industry: retail, manufacturing, and more
- Future of AI: trends, challenges, and opportunities