AI+ Developer™

Course code: AT310

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

  • 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

Price of the certification exam is included in the price of the course.

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Course dates

Starting date: Upon request

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 445 EUR

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Course description

Master Key AI Development Skills:

Learn Python, deep learning, advanced concepts, and optimization techniques to build robust AI solutions.

Specialize in Cutting-Edge AI Domains:

Gain expertise in NLP, computer vision, or reinforcement learning, alongside data processing, exploratory analysis, and time series analysis.

Stay Ahead in AI Development:

AI is transforming industries, and organizations seek developers with strong proficiency in deploying AI models to solve real-world problems.

Advance Your Career in AI Development:

With growing demand across tech, finance, and healthcare sectors, this certification positions you as a leader in AI-driven development.

  • GitHub Copilot
  • Lobe
  • H2O.ai
  • Snorkel

Target group

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.

Course structure

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

Prerequisites

Basic math, computer science fundamentals, fundamental programming skills

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Certification

50 questions, 70% passing, 90 minutes, online proctored exam

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