AI+ Developer™

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

Certificate Code: AT-310

Duration: 40 hours (5 Days)

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Course Metrics Prerequisites Curriculum Outcomes Opportunities Salary Comparison FAQs Enroll

14+

Modules

40 hours (5 Days)

Duration

50 MCQs, 90 Minutes

Examination

70% (35/50)

Passing Score

About This Certification

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

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.

Tools Covered

GitHub Copilot

Lobe

H2O.ai

Snorkel

Course Curriculum

14 Modules 40 hours (5 Days)

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

Exam Blueprint

Foundations of Artificial Intelligence (AI) - 5%

Mathematical Concepts for AI - 5%

Python for AI Development - 10%

Mastering Machine Learning - 15%

Deep Learning - 10%

Computer Vision - 10%

Natural Language Processing (NLP) - 15%

Reinforcement Learning - 5%

Cloud Computing in AI Development - 10%

Large Language Models (LLMs) - 5%

Cutting-Edge AI Research - 5%

AI Communication and Documentation - 5%

Learning Outcomes

Python Programming Proficiency

Students will gain a solid foundation in Python programming, a crucial skill for implementing AI algorithms, processing data, and building AI applications effectively.

Deep Learning Techniques

Learners will master machine learning and deep learning techniques to address challenges in classification, regression, image recognition, and natural language processing.

Cloud Computing in AI Development

Students will get hands-on experience in cloud-based AI application development and learn how to use AWS, Azure, and Google Cloud for scalable AI systems.

Project Management in AI

Participations will master the skills necessary to manage AI projects effectively, from initiation to completion, including planning, resource allocation, risk management, and stakeholder communication.

Career Opportunities

AI Machine Learning Developer

Design, implement, and optimize algorithms and models to enable systems to learn from data and make predictions or decisions.

AI Solutions Architect

Design and implement AI systems that integrate seamlessly with existing infrastructure to address business needs effectively and enhance system capabilities.

AI Application Developer

Build, design, and maintain AI-driven applications that solve real-world problems, integrating AI technologies for enhanced functionality.

AI System Programmers

Develop and maintain AI systems, including programming algorithms and software components that enable intelligent behavior in machines and applications.

Salary Potential

Without AI Skills

$77,311

With AI Skills

$125,439

62% Higher Earning Potential

Frequently Asked Questions

What will I gain from completing this certification?

Upon completion, you will receive an AI+ Developer™ certification, showcasing your proficiency in AI. You'll have the skills to tackle real-world AI challenges and implement advanced AI solutions in various domains.

Do I need any prior AI knowledge to join this course?

While prior AI knowledge is not mandatory, a fundamental understanding of Python programming and basic math and statistics will help you grasp the advanced concepts covered in this course.

Are there any hands-on projects in the course?

Yes, the course includes various hands-on projects and practical exercises to help you apply theoretical concepts to real-world scenarios, reinforcing your learning through practical experience.

Can I choose a specialization during the course?

You cannot choose a specialization in this course. However, you will be trained in areas such as Natural Language Processing (NLP), computer vision, and reinforcement learning.

How will my progress be evaluated?

Your progress will be evaluated through a combination of quizzes, hands-on exercises, and a final assessment. These evaluations are designed to test your understanding and application of the material.

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