AI+ Developer Practitioner™

$495.00
Formerly known as AI+ Developer™ 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
Learning Modules

Module 1: Foundations of Modern AI for Developers

  • 1.1 Understanding Artificial Intelligence
  • 1.2 Components of an AI Application
  • 1.3 Beginner AI Development Workflow
  • 1.4 AI Development Concepts and Limitations
  • 1.5 Case Study: A Chatbot Prototype That Produced Unreliable Answers
  • 1.6 Use Case: Selecting the Right AI Approach

Module 2: Python Programming for AI

  • 2.1 Python Foundations
  • 2.2 Python Data Structures and File Handling
  • 2.3 Beginner Software-Development Practices
  • 2.4 Case Study: An Unstructured Python Script Becomes Difficult to Maintain
  • 2.5 Use Case: Automated File Processing Utility

Module 3: Data Handling and Visualization

  • 3.1 Working with NumPy and Pandas
  • 3.2 Data Cleaning
  • 3.3 Exploratory Data Analysis
  • 3.4 Case Study: Dirty Customer Data Produces Incorrect Sales Insights
  • 3.5 Use Case: Retail Sales Data Preparation

Module 4: Practical Mathematics and Statistics for AI

  • 4.1 Essential Mathematical Concepts
  • 4.2 Essential Statistics
  • 4.3 Mathematical Reasoning for AI
  • 4.4 Case Study: Average Performance Hides a Major Customer Problem
  • 4.5 Use Case: Similarity-Based Product Recommendation

Module 5: Machine Learning Fundamentals

  • 5.1 Understanding Machine Learning
  • 5.2 Supervised Machine Learning
  • 5.3 Unsupervised and Other Beginner Methods
  • 5.4 Case Study: Customer Churn Prediction
  • 5.5 Use Case: Delivery-Time Prediction

Module 6: Model Evaluation and Improvement

  • 6.1 Model Evaluation Metrics
  • 6.2 Improving Model Performance
  • 6.3 Reliable Model Delivery
  • 6.4 Case Study: A High-Accuracy Model Misses the Important Cases
  • 6.5 Use Case: Spam Email Detection

Module 7: Deep Learning and Computer Vision Basics

  • 7.1 Neural Network Fundamentals
  • 7.2 Beginner Deep Learning with PyTorch
  • 7.3 Computer Vision Foundations
  • 7.4 Case Study: Manufacturing Defect Detection with Transfer Learning
  • 7.5 Use Case: Product Image Classification

Module 8: Natural Language Processing, Transformers, and LLM Fundamentals

  • 8.1 Text Processing Fundamentals
  • 8.2 Embeddings and Transformers
  • 8.3 Large Language Model Fundamentals
  • 8.4 Case Study: Choosing Between a Text Classifier and an LLM for Routing Support Tickets
  • 8.5 Use Case: Customer Review Analysis

Module 9: Generative and Multimodal AI Application Development

  • 9.1 Prompt Engineering Foundations
  • 9.2 Building Controlled Generative AI Applications
  • 9.3 Multimodal AI Foundations
  • 9.4 Case Study: Invoice Extraction Produces Incorrect Financial Fields
  • 9.5 Use Case: Multimodal Product Information Assistant

Module 10: Retrieval-Augmented Generation and Knowledge Assistants

  • 10.1 Retrieval Fundamentals
  • 10.2 Building a Basic RAG Workflow
  • 10.3 RAG Quality and Control
  • 10.4 Case Study: A Policy Assistant Returns an Outdated Rule
  • 10.5 Use Case: Employee Handbook Assistant

Module 11: Simple AI Agents, APIs, and Deployment

  • 11.1 API Development for AI
  • 11.2 Basic AI Agents and Tool Use
  • 11.3 Beginner Deployment and Operations
  • 11.4 Case Study: An Over-Privileged Agent Performs an Unapproved Action
  • 11.5 Use Case: IT Support Triage Assistant

Module 12: Responsible AI, Security, Monitoring, and Capstone

  • 12.1 Responsible AI Foundations
  • 12.2 AI Application Security
  • 12.3 Monitoring and Production Readiness
  • 12.4 Case Study: Prompt Injection Causes Confidential Data Exposure
  • 12.5 Use Case: Beginner AI Release Checklist

Optional Module: AI Agents for Developer

  • 1.1 What Are AI Agents?
  • 1.2 Significance of AI Agents for Developers
  • 1.3 Applications and Trends of AI Agents for Developers
  • 1.4 How Does an AI Agent Work?
  • 1.5 Core Characteristics of AI Agents
  • 1.6 Importance of AI Agents
  • 1.7 Types of AI Agents
  • 1.8 Comparison Table of AI Agents in Ethics
AI Enabled Coordinated Assurance Certification

Certificate Code:

AT-310

Duration:

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

 

Prerequisites:

Basic math, computer science fundamentals, fundamental programming skills

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.
Who Should Enroll

  • Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.

  • Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.

  • Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.

  • IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.

  • Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.

Related products

Powered by
►
Necessary cookies enable essential site features like secure log-ins and consent preference adjustments. They do not store personal data.
None
►
Functional cookies support features like content sharing on social media, collecting feedback, and enabling third-party tools.
None
►
Analytical cookies track visitor interactions, providing insights on metrics like visitor count, bounce rate, and traffic sources.
None
►
Advertisement cookies deliver personalized ads based on your previous visits and analyze the effectiveness of ad campaigns.
None
Powered by