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
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 Duration
- 2 Months – Monday – Friday
- Daily 2 Hrs
- Week End Extra Practice
- Career Guidance sessions
- Week End Batches are also available.
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
Prerequisites: No specific prerequisites are required for this course. It is suitable for both beginners and individuals with some prior knowledge of programming and mathematics.
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