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This comprehensive program covers foundational principles, advanced techniques, and practical applications, preparing participants to excel in the rapidly evolving field of AI.

Prerequisites

  • Programming Proficiency: Strong understanding of programming languages such as Python, Java, or C++.
  • Mathematical Foundations: Knowledge of calculus, linear algebra, and statistics.
  • Machine Learning Basics: Familiarity with core machine learning concepts and algorithms.
  • Problem-Solving Skills: Ability to approach complex problems analytically and develop innovative solutions.

 

Course Modules and Curriculum

Course Introduction Watch Demo
  • 1.1 Introduction to AI
  • 1.2 Core Concepts and Techniques in AI
  • 1.3 Ethical Considerations
  • 2.1 Overview of AI and its Various Applications
  • 2.2 Introduction to AI Architecture
  • 2.3 Understanding the AI Development Lifecycle
  • 2.4 Hands-on: Setting up a Basic AI Environment
  • 3.1 Basics of Neural Networks
  • 3.2 Activation Functions and Their Role
  • 3.3 Backpropagation and Optimization Algorithms
  • 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework
  • 4.1 Introduction to Neural Networks in Image Processing
  • 4.2 Neural Networks for Sequential Data
  • 4.3 Practical Implementation of Neural Networks
  • 5.1 Exploring Large Language Models
  • 5.2 Popular Large Language Models
  • 5.3 Practical Finetuning of Language Models
  • 5.4 Hands-on: Practical Finetuning for Text Classification
  • 6.1 Introduction to Generative Adversarial Networks (GANs)
  • 6.2 Applications of Variational Autoencoders (VAEs)
  • 6.3 Generating Realistic Data Using Generative Models
  • 6.4 Hands-on: Implementing Generative Models for Image Synthesis
  • 7.1 NLP in Real-world Scenarios
  • 7.2 Attention Mechanisms and Practical Use of Transformers
  • 7.3 In-depth Understanding of BERT for Practical NLP Tasks
  • 7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models
  • 8.1 Overview of Transfer Learning in AI
  • 8.2 Transfer Learning Strategies and Techniques
  • 8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks
  • 9.1 Overview of GUI-based AI Applications
  • 9.2 Web-based Framework
  • 9.3 Desktop Application Framework
  • 10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
  • 10.2 Building a Deployment Pipeline for AI Models
  • 10.3 Developing Prototypes Based on Client Requirements
  • 10.4 Hands-on: Deployment

Tools

TensorFlow

TensorFlow

Hugging Face Transformers

Hugging Face Transformers

Jenkins

Jenkins

TensorFlow Hub

TensorFlow Hub

Who Should Enroll?

This certification is ideal for:

  • Software Developers & Engineers
    - Learn how to integrate AI into applications and systems.
  • Data Scientists & AI Enthusiasts
    - Build and fine-tune AI models for better performance and accuracy.
  • Cloud & IT Professionals
    - Understand how AI works in cloud-based environments and automation workflows.
  • AI & ML Engineers
    - Advance your expertise in deep learning, AI deployment, and model optimization.
Key Learning Outcomes
  • Gain expertise in AI model development, optimization, and deployment.
  • Learn deep learning, natural language processing, and computer vision techniques.
  • Master AI performance tuning, automation, and cloud-based AI applications.
  • Understand best practices in AI security, governance, and ethical AI.

Enroll Now

Take the first step towards becoming an AI expert.
Enroll in our AI+ Engineer™ Course today and unlock the potential of coding to drive your career forward.

Exam Objectives

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Building AI-Driven User Interfaces

Learn to develop intuitive and user-friendly AI-powered GUIs, covering interface design, usability testing, and AI integration to enhance user experiences.

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Effective AI Communication & Deployment

Gain expertise in AI solution deployment and communication, including developing and managing AI deployment pipelines, ensuring smooth rollout, and articulating the value of AI solutions to stakeholders and end-users.

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AI Problem-Solving & Model Implementation

Apply AI principles to real-world challenges, enhance skills in identifying AI methodologies, constructing models, and interpreting results to tackle complex problems across industries.

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AI-Focused Project Management

Develop AI-specific project management skills, including planning, executing, and managing AI initiatives, handling resources, schedules, and stakeholder expectations for successful project outcomes.

Career Opportunities Post-Certification

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Median Salaries

$67,583

AI Salary Icon

With AI Skills

$134,143

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% Difference

98

Product Manager

AI Engineer

Design and develop AI solutions across industries.

Data Analyst

Machine Learning Engineer

Implement and optimize machine learning models for various applications.

AI Architect

Data Scientist

Analyze and interpret complex data to inform decision-making.

ML Engineer

Research Scientist

Conduct research to advance AI methodologies and technologies.

Join AI Course & Become Future-Ready!

Stay ahead in the AI-driven world. Enroll now to gain in-demand skills, hands-on experience, and industry-recognized certification for a future-ready career.

Get Certified | Check Matching Certification To Your Career

Frequently Asked Questions

This certification is ideal for professionals with a background in programming and mathematics who are looking to advance their careers in AI engineering.

Participants will acquire hands-on experience in building neural networks, applying large language models, implementing generative AI techniques, and utilizing transfer learning strategies.

Yes, a solid understanding of programming (preferably in Python), mathematics, and basic machine learning concepts is recommended.

The exam consists of 50 multiple-choice questions to be completed in 90 minutes, with a passing score of 70%.

Earning the AI+ Engineer™ Certification demonstrates advanced proficiency in AI engineering, enhancing your professional profile and opening opportunities in various tech-driven industries.