AI Developer Training
| Commitment | 1 Day, 7-8 hours a day. |
| Language | English |
| User Ratings | Average User Rating 4.8 See what learners said |
| Price | REQUEST |
| Delivery Options | Instructor-Led Onsite, Online, and Classroom Live |
COURSE OVERVIEW
AI Developer Training 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.
WHAT'S INCLUDED?
- 1 day of AI Developer Training with an expert instructor
- AI Developer Training Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
RESOURCES
- AI Developer Training – https://www.wiley.com/
- AI Developer – https://www.packtpub.com/
- AI Developer – https://store.logicaloperations.com/
- AI Developer Training – https://us.artechhouse.com/
- AI Developer Training – https://www.amazon.com/
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ADDITIONAL INFORMATION
COURSE OBJECTIVES
Upon completion of this AI Developer Training course, participants can:
- Python Programming Proficiency
- Students will gain a solid foundation in Python programming. Implementing AI algorithms, processing data, and constructing AI applications require this competence
- Deep Learning Techniques
- Learners will master machine learning and deep learning techniques and methods to classification, regression, image recognition, and natural language processing challenges.
- Cloud Computing in AI Development
- Students will get hands-on experience in cloud-based AI application development and learn how to use AWS, Azure, and Google Cloud for scalable AI systems.
- Project Management in AI
- Participations will master the skills necessary to manage AI projects effectively, from initiation to completion, including planning, resource allocation, risk management, and stakeholder communication.
CUSTOMIZE IT
- We can adapt this AI Developer Training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this AI Developer course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the AI Developer course around the mix of technologies of interest to you (including technologies other than those included in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policymaker), and present the AI Developer Training course in a manner understandable to lay audiences.
CLASS PREREQUISITES
The knowledge and skills that a learner must have before attending this AI Developer Training course are:
- Basic Math: Familiarity with high school-level algebra and basic statistics is desirable.
- Computer Science Fundamentals: Understanding the basic programming concepts (variables, functions, and loops) and data structures (lists and dictionaries).
- Fundamental knowledge of programming skills.
AUDIENCE/TARGET GROUP
The target audience for this AI Developer Training course:
- All
COURSE SYLLABUS
Module 1: Foundations of Artificial Intelligence
- Introduction to AI
- Types of Artificial Intelligence
- Branches of Artificial Intelligence
- Applications and Business Use Cases
Module 2: Mathematical Concepts for AI
- Linear Algebra
- Calculus
- Probability and Statistics
- Discrete Mathematics
Module 3: Python for Developer
- Python Fundamentals
- 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
- Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
Module 6: Computer Vision
- 6.1 Image Processing Basics
- Object Detection
- mage Segmentation
- Generative Adversarial Networks (GANs)
Module 7: Natural Language Processing
- Text Preprocessing and Representation
- Text Classification
- Named Entity Recognition (NER)
- Question Answering (QA)
Module 8: Reinforcement Learning
- Introduction to Reinforcement Learning
- Q-Learning and Deep Q-Networks (DQNs)
- Policy Gradient Methods
Module 9: Cloud Computing in AI Development
- Cloud Computing for AI
- Cloud-Based Machine Learning Services
Module 10: Large Language Models
- Understanding LLMs
- Text Generation and Translation
- Question Answering and Knowledge Extraction
Module 11: Cutting-Edge AI Research
- Neuro-Symbolic AI
- Explainable AI (XAI)
- Federated Learning
- Meta-Learning and Few-Shot Learning
Module 12: AI Communication and Documentation
- Communicating AI Projects
- Documenting AI Systems
- Ethical Considerations
