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 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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