Introduction to Artificial Intelligence (AI)
The course on Introduction to Artificial Intelligence (AI) offers students a thorough understanding of the essential principles and methodologies within the AI domain.
Certificate :
After Completion
Start Date :
10-Jan-2025
Duration :
30 Days
Course fee :
$150
COURSE DESCRIPTION:
The course on Introduction to Artificial Intelligence (AI) offers students a thorough understanding of the essential principles and methodologies within the AI domain.
It covers foundational topics such as search algorithms, machine learning, neural networks, natural language processing, and various problem-solving strategies.
Additionally, students will engage in practical projects that allow them to implement these AI techniques in real-world scenarios.
AI is revolutionizing various sectors by allowing machines to replicate human cognitive functions, tackle intricate challenges, and make independent decisions. This course aims to present AI concepts in a straightforward way, equipping students for further academic pursuits or careers in AI-related areas.
Upon completion, students will possess a robust grasp of the fundamental ideas and tools that underpin AI systems.
CERTIFICATION:
Upon finishing the Introduction to Artificial Intelligence (AI) course, students will be awarded a Certificate in Artificial Intelligence Fundamentals.
LEARNING OUTCOMES:
By the conclusion of the course, participants will possess the skills to:
By the conclusion of the Introduction to Artificial Intelligence (AI) course, students will acquire a comprehensive understanding of AI fundamentals, including its definition and various subfields such as Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Robotics, and Expert Systems.
They will gain insights into the historical development of AI and its contemporary applications, while also engaging in discussions about ethical implications and societal effects, including issues related to privacy, bias, and employment. Students will explore problem-solving strategies utilized in AI, learning about search algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS), as well as heuristic techniques such as A* and greedy algorithms, applying these methods to basic challenges in puzzle-solving, pathfinding, and gaming scenarios.
Furthermore, students will delve into the basics of machine learning, distinguishing between its various types—supervised, unsupervised, and reinforcement learning—and familiarizing themselves with essential algorithms like linear regression, decision trees, and k-nearest neighbors (KNN).
They will also learn to assess and validate machine learning models through metrics such as accuracy, precision, recall, and F1-score. Intermediate knowledge will encompass neural networks and deep learning, where students will understand the structure and function of neural networks, including artificial neurons, layers, and activation functions, and apply deep learning techniques to tasks like image recognition and classification.
In the realm of Natural Language Processing (NLP), they will grasp fundamental concepts such as tokenization, stemming, and lemmatization, and engage in text classification, sentiment analysis, and named entity recognition (NER) using libraries like NLTK or spaCy.
Lastly, students will explore reinforcement learning, focusing on how agents learn through interactions with their environment.
Course Curriculum
- What is Artificial Intelligence?
- Definition and key concepts.
- AI vs. Machine Learning vs. Deep Learning.
- Types of AI
- Narrow AI, General AI, and Super AI.
- History and Evolution of AI
- Milestones: Early AI systems to modern advancements.
- Applications of AI
- Real-world examples in healthcare, finance, transportation, and entertainment.
- Components of AI Systems
- Knowledge representation, reasoning, learning, and perception.
- Subfields of AI
- Natural Language Processing (NLP), Computer Vision, Robotics, and Expert Systems.
- AI Techniques
- Search algorithms, logic programming, and optimization techniques.
- Linear Algebra
- Vectors, matrices, and transformations.
- Probability and Statistics
- Bayesian inference, Markov processes.
- Logic and Reasoning
- Propositional logic and predicate logic.
- Overview of Machine Learning
- Supervised, unsupervised, and reinforcement learning.
- Core Algorithms
- Regression, classification, clustering.
- Model Evaluation
- Accuracy, precision, recall, F1 score.
- Programming for AI
- Introduction to Python.
- Libraries: NumPy, pandas, Matplotlib.
- AI Frameworks
- TensorFlow, PyTorch, OpenAI Gym.
- Development Environments
- Jupyter Notebook, Google Colab.
- AI Ethics
- Fairness, accountability, and transparency.
- Impact of AI on Society
- Job displacement, decision-making, and privacy concerns.
- AI Safety
- Avoiding unintended consequences and biases.
- Natural Language Processing
- Sentiment analysis, chatbots, and language translation.
- Computer Vision
- Image classification, facial recognition, and object detection.
- AI in Robotics
- Autonomous vehicles, drones, and industrial robots.
- Recommendation Systems
- Content filtering and collaborative filtering.
- Trends and Innovations
- Edge AI, quantum computing, and AI for sustainability.
- Challenges in AI
- Scalability, interpretability, and ethical dilemmas.
- Careers in AI
- Roles and opportunities in AI-related fields.
Training Features
Interactive Learning
Hands-on activities, quizzes, and live coding sessions.
Industry-Relevant Skills
Focused training on foundational AI concepts and tools.
Real-World Projects
Implement basic AI applications like chatbots and image classifiers.
Ethical Awareness
In-depth discussions on ethical AI practices.
Career Preparation
Resume building, interview preparation, and AI career guidance.
Certification
A certificate of completion to validate your understanding of AI fundamentals.
