AI+ Developer™

  • Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design
  • Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning
  • Advanced Modules: Includes time series, model explainability, and cloud deployment
  • Industry-Ready Skills: Prepares learners to design and deploy complex AI systems

¡Reserva ya!

  • Modalidad: Online

    Incluye:

    Candidatos ideales para este curso:

    AI Machine Learning Developer. Design, implement, and optimize algorithms and models to enable systems to learn from data and make predictions or decisions.
    AI Solutions Architect. Design and implement AI systems that integrate seamlessly with existing infrastructure to address business needs effectively and enhance system capabilities.
    AI Application Developer. Build, design, and maintain AI-driven applications that solve real-world problems, integrating AI technologies for enhanced functionality.
    AI System Programmers. Develop and maintain AI systems, including programming algorithms and software components that enable intelligent behavior in machines and applications.

    • Basic math, including familiarity with high school-level algebra and basic statistics, is desirable. 
    • Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential. 
    • A fundamental knowledge of programming skills is required. 

    Course Overview.

    1. Course IntroductionPreview

    Module 1: Foundations of Artificial Intelligence.

    1. 1.1 Introduction to AI Preview
    2. 1.2 Types of Artificial Intelligence Preview
    3. 1.3 Branches of Artificial Intelligence
    4. 1.4 Applications and Business Use Cases

    Module 2: Mathematical Concepts for AI.

    1. 2.1 Linear Algebra Preview
    2. 2.2 Calculus Preview
    3. 2.3 Probability and Statistics Preview
    4. 2.4 Discrete Mathematics

    Module 3: Python for Developer .

    1. 3.1 Python Fundamentals Preview
    2. 3.2 Python Libraries

    Module 4: Mastering Machine Learning.

    1. 4.1 Introduction to Machine Learning
    2. 4.2 Supervised Machine Learning Algorithms
    3. 4.3 Unsupervised Machine Learning Algorithms
    4. 4.4 Model Evaluation and Selection

    Module 5: Deep Learning.

    1. 5.1 Neural Networks
    2. 5.2 Improving Model Performance
    3. 5.3 Hands-on: Evaluating and Optimizing AI Models

    Module 6: Computer Vision.

    1. 6.1 Image Processing Basics
    2. 6.2 Object Detection
    3. 6.3 Image Segmentation
    4. 6.4 Generative Adversarial Networks (GANs)

    Module 7: Natural Language Processing.

    1. 7.1 Text Preprocessing and Representation
    2. 7.2 Text Classification
    3. 7.3 Named Entity Recognition (NER)
    4. 7.4 Question Answering (QA)

    Module 8: Reinforcement Learning.

    1. 8.1 Introduction to Reinforcement Learning
    2. 8.2 Q-Learning and Deep Q-Networks (DQNs)
    3. 8.3 Policy Gradient Methods

    Module 9: Cloud Computing in AI Development.

    1. 9.1 Cloud Computing for AI
    2. 9.2 Cloud-Based Machine Learning Services

    Module 10: Large Language Models.

    1. 10.1 Understanding LLMs
    2. 10.2 Text Generation and Translation
    3. 10.3 Question Answering and Knowledge Extraction

    Module 11: Cutting-Edge AI Research.

    1. 11.1 Neuro-Symbolic AI
    2. 11.2 Explainable AI (XAI)
    3. 11.3 Federated Learning
    4. 11.4 Meta-Learning and Few-Shot Learning

    Module 12: AI Communication and Documentation.

    1. 12.1 Communicating AI Projects
    2. 12.2 Documenting AI Systems
    3. 12.3 Ethical Considerations

    Optional Module: AI Agents for Developers.

    1. 1. Understanding AI Agents
    2. 2. Case Studies
    3. 3. Hands-On Practice with AI Agents

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