AT-310
AI+ Developer
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Price:
Duration:
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5 Days
Prerequisites
• Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.
• Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.
• Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.
• IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.
• Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.
What you’ll learn in this course
AI+ Developer™ certification program offers a tailored journey in key AI domains for developers. Master Python, advanced concepts, math, stats, optimization, and deep learning. The curriculum covers data processing, exploratory analysis, and allows specialization in NLP, computer vision, or reinforcement learning.
The program includes time series analysis, model explainability, and deployment intricacies. Upon completion, you'll receive a certification, showcasing your AI proficiency for real-world challenges.
Course Objectives
• Comprehensive exploration of AI, focusing on fundamental and advanced topics.
• In-depth instruction on Python programming essential for AI development.
• Study Natural Language Processing (NLP), Computer Vision, and Reinforcement Learning (RL) to prepare for specialized AI tasks.
• Understand Cloud platforms for AI development and deployment which provide scalability, efficiency, and access to powerful computational resources.
• Explore AI's ethical and social implications, emphasizing responsible AI practices and the broader influence of AI technology on society.
Course Outline
Module 1: Foundations of Artificial Intelligence
• 1.1 Introduction to AI
• 1.2 Types of Artificial Intelligence
• 1.3 Branches of Artificial Intelligence
• 1.4 Applications and Business Use Cases
Module 2: Mathematical Concepts for AI
• 2.1 Linear Algebra
• 2.2 Calculus
• 2.3 Probability and Statistics
• 2.4 Discrete Mathematics
Module 3: Python for Developer
• 3.1 Python Fundamentals
• 3.2 Python Libraries
Module 4: Mastering Machine Learning
• 4.1 Introduction to Machine Learning
• 4.2 Supervised Machine Learning Algorithms
• 4.3 Unsupervised Machine Learning Algorithms
• 4.4 Model Evaluation and Selection
Module 5: Deep Learning
• 5.1 Neural Networks
• 5.2 Improving Model Performance
• 5.3 Hands-on: Evaluating and Optimizing AI Models
Module 6: Computer Vision
• 6.1 Image Processing Basics
• 6.2 Object Detection
• 6.3 Image Segmentation
• 6.4 Generative Adversarial Networks (GANs)
Module 7: Natural Language Processing
• 7.1 Text Preprocessing and Representation
• 7.2 Text Classification
• 7.3 Named Entity Recognition (NER)
• 7.4 Question Answering (QA)
Module 8: Reinforcement Learning
• 8.1 Introduction to Reinforcement Learning
• 8.2 Q-Learning and Deep Q-Networks (DQNs)
• 8.3 Policy Gradient Methods
Module 9: Cloud Computing in AI Development
• 9.1 Cloud Computing for AI
• 9.2 Cloud-Based Machine Learning Services
Module 10: Large Language Models
• 10.1 Understanding LLMs
• 10.2 Text Generation and Translation
• 10.3 Question Answering and Knowledge Extraction
Module 11: Cutting-Edge AI Research
• 11.1 Neuro-Symbolic AI
• 11.2 Explainable AI (XAI)
• 11.3 Federated Learning
• 11.4 Meta-Learning and Few-Shot Learning
Module 12: AI Communication and Documentation
• 12.1 Communicating AI Projects
• 12.2 Documenting AI Systems
• 12.3 Ethical Considerations
Further information
If you would like to know more about this course please contact us
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