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AI+ Developer

AI+ Developer

$3,599.00

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
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Description

Why This Certification Matters

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.

What You’ll Learn

  1. Course Introduction

  1. 1.1 Introduction to AI
  2. 1.2 Types of Artificial Intelligence
  3. 1.3 Branches of Artificial Intelligence
  4. 1.4 Applications and Business Use Cases

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

  1. 3.1 Python Fundamentals
  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

Prerequisites

  • Basic math, including familiarity with high school-level algebra and basic statistics, is desirable.
  • Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential.
  • A fundamental knowledge of programming skills is required.

Course + Exam 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
Delivery: Online labs, projects, case studies
Outcome: Industry-recognized credential + hands-on experience

 

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