AI+ Developer™

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

Certificate Code: AT-310

About This Course

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

Certificate Overview

Included

Instructor-led OR Self-paced course + Official exam + Digital badge

Duration

Instructor-Led: 5 days (live or virtual)
Self-Paced: 40 hours of content

Prerequisites

Basic math, computer science fundamentals, fundamental programming skills

Exam Format

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

Course Modules

Course Overview

Course IntroductionPreview

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

Course