AI+ Developer (AIDEV)

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AI+ Developer (AIDEV)

Fast Lane Institute for Knowledge Transfer GmbH
Logo Fast Lane Institute for Knowledge Transfer GmbH
Provider rating: starstarstarstarstar_half 8.9 Fast Lane Institute for Knowledge Transfer GmbH has an average rating of 8.9 (out of 33 reviews)

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Description

Voraussetzungen

  • 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.

Detaillierter Kursinhalt

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 …

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Didn't find what you were looking for? See also: Neural Networks, Artificial Intelligence, Python, R Programming, and M&A (Mergers & Acquisitions).

Voraussetzungen

  • 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.

Detaillierter Kursinhalt

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 Convolutional Neural Networks (CNNs)
  • 5.3 Recurrent Neural Networks (RNNs)

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
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There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.