Overview
Artificial Intelligence A-Z Training Course provides participants with a comprehensive understanding of artificial intelligence concepts, techniques, and applications. This course covers a wide range of topics, including machine learning, deep learning, natural language processing, computer vision, and AI ethics. Participants will gain hands-on experience through practical exercises and projects, enabling them to apply AI techniques effectively.
Objectives
- Understand the fundamentals of artificial intelligence and its applications.
- Apply machine learning algorithms for data analysis and prediction tasks.
- Implement deep learning models for computer vision and natural language processing.
- Utilize natural language processing techniques for text analysis and language generation.
- Develop computer vision applications for image recognition and object detection.
- Explore ethical considerations and responsible AI practices.
- Gain practical experience through hands-on projects and exercises.
Audience
- Professionals interested in gaining a solid foundation in artificial intelligence.
- Data scientists and machine learning practitioners looking to expand their AI skills.
- Software developers and engineers interested in implementing AI applications.
- AI enthusiasts and researchers seeking practical knowledge of AI techniques.
Pre- requisites
- Basic understanding of programming concepts and algorithms.
- Familiarity with a programming language such as Python is recommended but not mandatory.
- Prior knowledge of machine learning or deep learning is beneficial but not required.
Duration: 3 days – 21 hrs
Course Content
Day 1
Introduction to Artificial Intelligence
- Overview of artificial intelligence and its subfields
- Historical developments and key milestones
- Ethical considerations in AI
Machine Learning Fundamentals
- Introduction to supervised and unsupervised learning
- Regression and classification algorithms
- Evaluation metrics and model selection
Day 2
Deep Learning and Neural Networks
- Neural network architectures and activation functions
- Backpropagation and gradient descent
- Convolutional neural networks (CNNs) for computer vision
- Recurrent neural networks (RNNs) for natural language processing
Natural Language Processing (NLP)
- Text preprocessing and feature extraction
- Sentiment analysis and text classification
- Named Entity Recognition (NER) and language generation
Day 3
Computer Vision and Project Work
- Image processing techniques
- Object detection and image segmentation
- Convolutional Neural Networks (CNNs) for computer vision tasks
- Real-world project implementation using AI techniques
- Ethical considerations in AI and responsible AI practices
