AI+ Developer™

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

1

Course Overview

  1. Course IntroductionPreview
2

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
3

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
4

Module 3: Python for Developer

  1. 3.1 Python Fundamentals Preview
  2. 3.2 Python Libraries
5

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
6

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
7

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)
8

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)
9

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
10

Module 9: Cloud Computing in AI Development

  1. 9.1 Cloud Computing for AI
  2. 9.2 Cloud-Based Machine Learning Services
11

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
12

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
13

Module 12: AI Communication and Documentation

  1. 12.1 Communicating AI Projects
  2. 12.2 Documenting AI Systems
  3. 12.3 Ethical Considerations
14

Optional Module: AI Agents for Developers

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

AI Tools You'll Learn

GitHub Copilot

GitHub Copilot

Lobe

Lobe

H2O.ai

H2O.ai

Snorkel

Snorkel