Artificial Intelligence Course

The comprehensive Artificial Intelligence (AI) course is designed to provide a deep understanding of artificial intelligence, covering the core concepts, techniques, and applications.

The course is offered by leading training institute I-Tech System. Whether you’re a beginner or looking to advance your AI skills, this course offers a strong foundation and practical knowledge in the field.

Artificial Intelligence Course Details:

• Introduction to Artificial Intelligence
• What is Artificial Intelligence?
• Historical Overview
• The Role of AI in Modern Society
• AI Concepts and Terminology
• AI Tools and Frameworks
• Artificial Intelligence Course
• Machine Learning Fundamentals
• Introduction to Machine Learning
• Supervised, Unsupervised, and Reinforcement Learning
• Model Training and Evaluation
• Feature Engineering
• Practical Machine Learning Examples
• Data Preprocessing for AI
• Data Collection and Cleaning
• Feature Selection and Extraction
• Data Scaling and Transformation
• Handling Missing Data
• Data Privacy and Ethics in AI
• Supervised Learning Algorithms
• Linear Regression and Logistic Regression
• Decision Trees and Random Forest
• Support Vector Machines (SVM)
• k-Nearest Neighbors (k-NN)
• Model Selection and Hyperparameter Tuning
• Unsupervised Learning and Clustering
• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Anomaly Detection
• Real-world Applications
• Natural Language Processing (NLP)
• Text Preprocessing and Tokenization
• Sentiment Analysis
• Named Entity Recognition
• Language Modeling
• Real-world NLP Applications
• Deep Learning and Neural Networks
• Introduction to Neural Networks
• Feedforward Neural Networks (FNN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Training Deep Learning Models
• Deep Learning Frameworks
• TensorFlow and Keras
• PyTorch
• Deep Learning for Image Recognition
• Natural Language Processing with Deep Learning
• Reinforcement Learning
• Introduction to Reinforcement Learning
• Markov Decision Processes (MDPs)
• Q-Learning
• Deep Q-Networks (DQN)
• Applications in Gaming and Robotics
• AI Ethics and Responsible AI
• Ethical Considerations in AI
• Bias and Fairness in AI
• Transparency and Accountability
• AI Regulation and Guidelines
• Real-world AI Projects
• Implementing AI Solutions
• Collaborative AI Development
• Project Management and Deployment
• Presenting AI Findings
• AI in Various Industries
• AI in Healthcare, Finance, Retail, and more
• Case Studies and Success Stories
• Future Trends in AI

Course Benefits:

• Hands-on projects and practical exercises
• Access to real datasets and business case studies
• Quizzes and assessments for progress tracking
• Support from a community forum
• Course completion certificate