What does the Artificial Intelligence course cover?
Artificial Intelligence is covered here in 8 modules: Introduction to Artificial Intelligence: History of AI, Applications of AI, Machine Learning Fundamentals: Model Evaluation and Selection, Overfitting and Regularization, Deep Learning: Architectures: LeNet, AlexNet, and ResNet and 5 more. The outline lists 40 specific topics, opening with What is Artificial Intelligence?
How do you approach Artificial Intelligence step by step?
The work is sequenced in 8 stages. It starts with Introduction to Artificial Intelligence: History of AI, Applications of AI, moves through Machine Learning Fundamentals: Model Evaluation and Selection, Overfitting and Regularization and Deep Learning: Architectures: LeNet, AlexNet, and ResNet, and ends at AI Ethics and Fairness: AI Ethics: Bias, Fairness, and Transparency.
What is in Module 1 of the Artificial Intelligence course?
Module 1 is Introduction to Artificial Intelligence: History of AI, Applications of AI. It works through What is Artificial Intelligence?, history of AI, Types of AI: Narrow, General, and Superintelligence and 2 more. It sets the vocabulary the remaining 7 modules build on.
How is the Artificial Intelligence course delivered?
The Artificial Intelligence 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 Artificial Intelligence course cost?
The Artificial Intelligence 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.
Closely related courses: Artificial Intelligence Marketing Mastery, Unlocking Artificial General Intelligence, Unlocking Artificial Intelligence, AIOps.
More answers: what you get with every course, refund policy, all help answers.
Mastering Artificial Intelligence: A Step-by-Step Implementation Guide
Course Overview
This comprehensive course is designed to help you master the concepts of Artificial Intelligence (AI) and its implementation in real-world applications. With a focus on practical, hands-on learning, you'll gain the skills and knowledge needed to succeed in this exciting field.Course Features
- Interactive and Engaging: Learn through a variety of interactive elements, including videos, quizzes, and hands-on projects.
- Comprehensive Curriculum: Covering 80+ topics, our course provides a thorough understanding of AI concepts and techniques.
- Personalized Learning: Tailor your learning experience to your needs and goals with our flexible, self-paced format.
- Up-to-date Content: Stay current with the latest advancements and trends in AI.
- Practical Applications: Learn how to apply AI concepts to real-world problems and projects.
- High-quality Content: Developed by expert instructors with extensive experience in AI.
- Certification: Receive a certificate upon completion, issued by The Art of Service.
- Flexible Learning: Access course materials 24/7, from any device.
- User-friendly Platform: Easily navigate and track your progress through our intuitive learning platform.
- Mobile-accessible: Learn on-the-go, whenever and wherever you want.
- Community-driven: Connect with fellow learners and instructors through our online community.
- Actionable Insights: Gain practical knowledge and skills that can be applied immediately.
- Hands-on Projects: Apply your knowledge through real-world projects and case studies.
- Bite-sized Lessons: Learn in manageable chunks, with each lesson designed to be completed in under an hour.
- Lifetime Access: Enjoy ongoing access to course materials, even after completion.
- Gamification: Engage with our interactive elements, such as quizzes and challenges, to make learning fun.
- Progress Tracking: Monitor your progress and stay motivated with our tracking features.
Course Outline
Module 1. Introduction to Artificial Intelligence: History of AI, Applications of AI
- What is Artificial Intelligence?
- History of AI
- Types of AI: Narrow, General, and Superintelligence
- Applications of AI
- Benefits and Challenges of AI
Module 2. Machine Learning Fundamentals: Model Evaluation and Selection, Overfitting and Regularization
- Introduction to Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Machine Learning Algorithms: Linear Regression, Decision Trees, and Clustering
- Model Evaluation and Selection
- Overfitting and Regularization
Module 3. Deep Learning: Architectures: LeNet, AlexNet, and ResNet
- Introduction to Deep Learning
- Types of Deep Learning: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) Networks
- Deep Learning Architectures: LeNet, AlexNet, and ResNet
- Deep Learning Applications: Image Classification, Object Detection, and Natural Language Processing
- Deep Learning Frameworks: TensorFlow, Keras, and PyTorch
Module 4. Natural Language Processing (NLP): NLP Libraries: NLTK, spaCy, and Stanford CoreNLP
- Introduction to NLP
- Text Preprocessing: Tokenization, Stemming, and Lemmatization
- NLP Techniques: Sentiment Analysis, Named Entity Recognition, and Part-of-Speech Tagging
- NLP Applications: Text Classification, Sentiment Analysis, and Machine Translation
- NLP Libraries: NLTK, spaCy, and Stanford CoreNLP
Module 5. Computer Vision: Libraries: OpenCV, Pillow, and scikit-image
- Introduction to Computer Vision
- Image Processing: Filtering, Thresholding, and Edge Detection
- Computer Vision Techniques: Object Detection, Segmentation, and Tracking
- Computer Vision Applications: Image Classification, Object Recognition, and Scene Understanding
- Computer Vision Libraries: OpenCV, Pillow, and scikit-image
Module 6. Robotics and Autonomous Systems: Libraries: ROS, PyRobot, and DroneKit
- Introduction to Robotics and Autonomous Systems
- Robotics Fundamentals: Kinematics, Dynamics, and Control
- Autonomous Systems: Perception, Localization, and Navigation
- Robotics and Autonomous Systems Applications: Robotics Arm, Self-Driving Cars, and Drones
- Robotics and Autonomous Systems Libraries: ROS, PyRobot, and DroneKit
Module 7. Expert Systems and Knowledge Representation: Libraries: CLIPS, JESS, and Protege
- Introduction to Expert Systems and Knowledge Representation
- Expert Systems: Rule-Based Systems, Frame-Based Systems, and Ontologies
- Knowledge Representation: Propositional Logic, First-Order Logic, and Description Logics
- Expert Systems and Knowledge Representation Applications: Decision Support Systems, Expert Systems, and Knowledge Graphs
- Expert Systems and Knowledge Representation Libraries: CLIPS, JESS, and Protege
Module 8. AI Ethics and Fairness: AI Ethics: Bias, Fairness, and Transparency
- Introduction to AI Ethics and Fairness
- AI Ethics: Bias, Fairness, and Transparency
- AI Fairness: Fairness Metrics, Fairness Algorithms, and Fairness Frameworks
- AI Ethics and Fairness Applications: Fairness-Aware AI Systems, Explainable AI, and AI for Social Good
- AI Ethics and Fairness Libraries: AI Fairness 360, Fairlearn, and AIF360