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AI+ Developer Practitioner™

Course code: NPAT310

Formerly known as AI+ Developer™

Get hands-on with the tools and technologies that power the AI ecosystem.

  • Foundational AI Principles: Encompasses Python programming, deep learning methodologies, data processing techniques, and algorithmic design frameworks
  • Project-Based Practical Learning: Emphasizes projects in natural language processing, image recognition, and reinforcement techniques.
  • Specialized Advanced Curriculum: Features time series analysis, model interpretability techniques, and cloud-based deployment strategies.
  • Profession-Ready Competencies: Equips learners with the expertise to architect and deploy sophisticated AI systems confidently.
75 EUR

91 EUR including VAT

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Course dates

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 285 EUR

Register

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 75 EUR

Register

Starting
date		 Place
Type Course
duration		 Language Price without VAT
G Upon request Self-paced 40 hours en 285 EUR Register
G Upon request Self-paced 40 hours en 75 EUR Register
G Guaranteed course

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Course description

Python Programming Proficiency

Students will gain a solid foundation in Python programming, a crucial skill for implementing AI algorithms, processing data, and building AI applications effectively.

Deep Learning Techniques

Learners will master machine learning and deep learning techniques to address challenges in classification, regression, image recognition, and natural language processing.

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.

Course structure

Course Overview

Module 1: Foundations of Artificial Intelligence

  • 1.1 Introduction to AI Preview
  • 1.2 Types of Artificial Intelligence Preview
  • 1.3 Branches of Artificial Intelligence
  • 1.4 Applications and Business Use Cases

Module 2: Mathematical Concepts for AI

  • 2.1 Linear Algebra Preview
  • 2.2 Calculus Preview
  • 2.3 Probability and Statistics Preview
  • 2.4 Discrete Mathematics

Module 3: Python for Developer

  • 3.1 Python Fundamentals Preview
  • 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

Optional Module: AI Agents for Developers

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

Prerequisites

  • Mathematical fundamentals including familiarity with high school-level algebra and foundational statistics is recommended
  • Comprehension of core programming principles such as variables, functions, loops, and data structures including lists and dictionaries is necessary
  • A fundamental grounding in programming skills and practical coding experience is required for course participation

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