x

AI+ Developer™

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
AVALIABLE AT COMPUNET LIMITED Enroll Now Enroll Now

Certificate Code

AT-310

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic math, computer science fundamentals, fundamental programming skills
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  1. Course IntroductionPreview

  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

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

  1. 3.1 Python Fundamentals Preview
  2. 3.2 Python Libraries

  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

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

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

  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)

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

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

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

  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

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

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

AI Tools Covered

GitHub Copilot
Lobe
H2O.ai
Snorkel
Enroll Now Enroll Now