AI Development Certification Course That Transforms Your Career

TABLE OF CONTENTS
Introduction.................................................................................................................................1
Certification Goals and Learning Outcomes.............................................................2
The Impact of AI on Modern Development Practices..........................................2
What is Next for AI?................................................................................................................3
How AI Transforms the Roles and Responsibilities of Developers...............4
How AI Addresses Current Challenges for Developers.......................................4
How Industries are Adopting AI in Software Development.............................5
How to Integrate AI in Software Development Practices..................................5
Module 1: Foundations of AI..............................................................................................6
Module 2: Mathematical Concepts for AI..................................................................6
Module 3: Python for Developer........................................................................................6
Module 4: Mastering Machine Learning......................................................................7
Module 5: Deep Learning.....................................................................................................7
Module 6: Computer Vision................................................................................................7
Module 7: Natural Language Processing.....................................................................8
Module 8: Reinforcement Learning................................................................................8
Module 9: Cloud Computing in AI Development....................................................8
Module 10: Large Language Models...............................................................................8
Module 11: Cutting-Edge AI Research............................................................................9
Module 12: AI Communication and Documentation.............................................9
How Can AI CERTs Help Build an AI-Ready Culture?..............................................9
Copyright© 2024. All rights reserved
AI+
Developer

Introduction
The AI+ Developer™ Certification helps you master innovative technology and
advance your career in Artificial Intelligence (AI). Within this certification, you will
cover various topics such as python programming, advanced AI methods, linear
algebra, calculus, probability, statistics, data structures, and control flow to thrive in
this dynamic sector. Learners will master Deep Learning (DL), Neural Networks, and
GANs, applying them to exciting AI applications such as image processing, object
identification, text categorization, and question-answering systems will be
demonstrated.
Engage in hands-on exercises and real-world case studies in sectors like healthcare,
finance, and autonomous driving, which will help you to gain practical experience
and insights. In this certification, you will learn to use AWS, Azure, and Google
Cloud for AI development and deployment, and train and optimize GPT-4 and BERT
language models. You will also learn about AI ethics, ensuring fairness,
transparency, and accountability in AI systems. Completing this certification boosts
your professional credibility and will be recognized globally by employers as proof of
your AI expertise. We will also cover the following topics to help you understand the
incorporation of AI in the Developer role.
Foundations of Artificial Intelligence
Mathematical Concepts for AI
Python for Developer
Mastering Machine
Learning Deep Learning
Computer Vision
Natural Language Processing
Reinforcement Learning
Cloud Computing in AI Development
Large Language Models
Cutting-edge AI Research
AI Communication and Documentation
Certification Prerequisites
Basic Mathematics: High school algebra and basic statistics are necessary.
Computer Science Fundamentals: Variables, functions, loops, lists, and
dictionaries must be understood.
Python Programming: Hands-on exercises and project work require Python
proficiency.
Who Should Enroll?
Software Developers: Learn to integrate AI and Machine Learning (ML) into
applications for smarter solutions.
AI+
Developer

AI Engineers: Improve your ability to design, develop, and maintain practical AI
systems.
Data Scientists: Build and deploy AI models, advancing from data analysis to AI-
driven solutions.
IT Professionals: Upgrade technical skills by incorporating AI, enhancing system
operations and decision-making.
Certification Goals and Learning Outcomes
Comprehensive exploration of AI, focusing on fundamental and advanced
topics. In-depth instruction on Python programming essential for AI
development.
Study Natural Language Processing (NLP), Computer Vision, and Reinforcement
Learning (RL) to prepare for specialized AI tasks.
Understand Cloud platforms for AI development and deployment which provide
scalability, efficiency, and access to powerful computational resources.
Explore AI's ethical and social implications, emphasizing responsible AI practices
and the broader influence of AI technology on society.
The Impact of AI on Modern Business Practices
North America dominates
the market with 28.8% The European region has
a market share at
24.3%
The Asia-Pacific region share
stands at 25%
(MEA) region is growing
at
2.4%
Australia is growing at
14.1%
Middle East and Africa
Latin America
stands at

Figure 1: Global AI Market Share
Source: Forbes, IBM, PWC
2
AI+
Developer

AI is rapidly evolving and has significantly influenced technical and economic growth
over the past decade. This foundational technology is poised to revolutionize
corporate operations, boost idea generation, and enhance productivity.
The transformative power of AI holds great promise for the global economy. North
America leads the market with a 28.8% share, followed by the Asia-Pacific region at
25%. Europe holds 24.3% of the market, while Australia is growing at 14.1%. Latin
America and the Middle East and Africa (MEA) region each account for 5.4%,
illustrating the regional evolution of AI.
From a developer's perspective, the evolution of AI technologies has been marked
by rapid advancements and increasing accessibility. Initially, AI development
required specialized knowledge in complex algorithms and substantial computational
resources. However, with the advent of powerful frameworks like TensorFlow and
PyTorch, along with cloud-based AI services, developers can now easily implement
ML models and integrate AI capabilities into applications.
The focus has shifted from understanding intricate algorithms to leveraging pre-built
models, APIs, and tools that streamline the development process. This evolution has
empowered developers to create innovative AI-driven solutions more efficiently,
driving significant advancements in various industries.
What is Next for AI?
For developers, the future of AI holds exciting possibilities. Enhanced AI frameworks
and libraries will simplify complex tasks, making it easier to build and deploy AI
models. Integration with edge computing will allow AI to process data locally,
improving speed and reducing latency. Advances in NLP will enable more
sophisticated conversational agents and voice interfaces. Additionally, the focus on
ethical AI and responsible AI development will shape best practices, ensuring that AI
systems are fair, transparent, and secure.
How AI Transforms the Roles and Responsibilities of
Developers
AI is set to revolutionize the roles and responsibilities of developers, introducing
advanced tools and automation that will streamline coding, enhance efficiency, and
drive innovation. As AI becomes more integrated into the software development
process, developers will focus more on creative problem-solving, complex system
design, and leveraging AI-driven insights for better outcomes. So, let’s explore
some keys points represented in the below figure:
3
AI+
Developer

AI-driven tools automatically detect
bugs, code smells, and enforce
coding standards, which improves
overall quality.
Debugging is labor-intensive,
often taking hours to find root
AI-powered debugging tools
identify issues and suggest fixes
quickly, significantly reducing
Writing efficient, optimized code
for various conditions is
Development environments
equipped with AI offer real-time
optimization
suggestions, enhancing code
Ensuring application security
and identifying vulnerabilities
can be complex.
Security tools powered by AI scan
for vulnerabilities and provide
real-time recommendations to
secure applications.
Improved Efficiency
Development environments powered by AI optimize code for
software developers and suggest improvements, accelerating the
development process.
AI analyzes data for developers to inform decisions, predict trends, and
understand user behavior effectively.
Figure 2: Exploring How AI Transform Roles and Responsibilities of Developers
By utilizing AI, developers can become more efficient, produce higher quality
software, and tackle more innovative projects, reshaping the landscape of software
development.
How AI Addresses Current Challenges for Developers
AI is revolutionizing software development by addressing some of the biggest
challenges developers face. AI generates innovative solutions that enhance the
development process and drive growth. Here are some of the major challenges and
their corresponding AI-driven solutions shown in the below figure:
Figure 3: AI Addressing Current Challenges for Developers
Tools driven by AI detect and fix bugs automatically, ensuring higher
quality and reliability for developers.
Enhanced Code Quality
Tools used by developers can identify security vulnerabilities
and recommend solutions for creating secure applications.
Security Enhancements
Advanced Data Analysis
AI+
Developer
AI customizes environments to individual workflows, providing
tailored coding suggestions and resources.
Personalized
Development
AI Solution
Security
Vulnerabilities
AI Solution
Code Optimization and
Performance Issues
AI Solution
Time-Consuming
Debugging Processes
AI Solution
Bug Detection and
Code Quality
Ensuring high code quality is time-
consuming and prone to errors
during manual reviews.

Leveraging these AI-driven solutions allows developers to overcome common
challenges, improve their productivity, and deliver higher quality software products.
How Industries are Adopting AI in Development
Industries are increasingly adopting AI in software development to enhance
workforce efficiency and productivity. By integrating AI tools, organizations are
automating repetitive tasks such as code reviews, debugging, and performance
optimization, allowing developers to focus on more complex and creative aspects of
their projects.
This shift not only accelerates development cycles but also reduces the potential for
human error, leading to higher-quality software. AI's role in streamlining these
processes helps employees by minimizing manual effort and providing them with
advanced tools that support real-time decision-making and problem-solving,
ultimately leading to a more innovative and agile development environment.
How to Integrate AI in Business
Integrating AI into software development practices can enhance various aspects of
the process, from code writing to testing and deployment. Here’s a step-by-step
approach mentioned in the below figure to effectively incorporate AI into your
software development practices.
Figure 4: Integrating AI in Software Development Practices
By following these steps, you can leverage AI to enhance software development
practices, improve efficiency, and deliver higher-quality software.
5
AI+
Developer
Identify Key Areas for AI Integration
Choose the Right AI Tools and Technologies
Integrate AI into Development Workflows
Train and Onboard Your Team
Monitor and Evaluate
Address Ethical and Security Considerations

A Brief Summary on AI+ DeveloperCertification
At AI CERTs, we empower organizations to unlock the potential of AI with our
industry-leading suite of role-based certifications.
To boost your skills and understanding, look at the AI+ Developer modules that
cover essential aspects of AI development.
Module 1: Foundations of AI
AI's roots go back to the mid-20th century when the concept arose. The
development of computational models and complicated algorithms replicating
human cognition have marked AI's evolution.
This module focuses on AI's evolution from early computational models to advanced
algorithms, covering types like Weak AI, General AI, and Super AI. It includes AI
categories such as Reactive Machines and Theory of Mind, and applications like
Computer Vision, NLP, and Robotics, demonstrating AI's broad potential across
various sectors.
Module 2: Mathematical Concepts for AI
Mathematical concepts are essential for AI as they enable efficient data
manipulation, model optimization, and handling of uncertainty. Linear algebra,
calculus, probability, and discrete mathematics provide the tools for developing
accurate and effective AI models.
The focus of this module is on the mathematical foundations for AI, including linear
algebra for data manipulation, calculus for function optimization, and probability
distributions for simulating events. It explores how eigenvalues, gradients, and
integration contribute to AI modeling and performance, with discrete mathematics
supporting algorithmic thinking and data modeling.
Module 3: Python for Developer
Python is valuable for developers due to its simplicity, readability, and extensive
libraries. It supports data science, ML, and automation tasks, with tools like NumPy
and Pandas for data manipulation and analysis. Its broad adoption and supportive
community enhance development efficiency.
Within this module, Python’s role in data science and ML is explored, focusing on
basic syntax, data types, and control structures. It covers essential libraries such as
NumPy for numerical operations, Pandas for data manipulation, and Matplotlib and
Seaborn for visualization. These tools enable efficient coding and data analysis.
AI+
Developer

Module 4: Mastering Machine Learning
Mastering ML is key for developing accurate predictive models, driving innovation,
and solving complex problems. It enhances problem-solving skills and supports
data- driven decision-making.
The module covers the basics of ML, including supervised, unsupervised, and RL. It
outlines the ML lifecycle—data preparation, model training, evaluation, and
deployment. Techniques for regression, classification, and clustering are discussed,
along with model evaluation metrics and practical exercises.
Module 5: Deep Learning
DL is important for analyzing complex data patterns that traditional methods find
challenging to address. It excels in image and speech recognition, NLP, and
autonomous systems, uncovering insights from large datasets and driving
innovations across various fields.
This module helps you understand DL through neural networks, including CNNs for
image recognition and RNNs for sequential data. It covers frameworks like
TensorFlow, PyTorch, and Keras. Hands-on tasks provide practical experience in
building and deploying AI models.
Module 6: Computer Vision
Computer vision, a crucial part of AI, lets computers comprehend and make
judgments from visual data. According to Market.us report, the global computer
vision market size is expected to expand around $58.9 billion by 2033 with a
progressive CAGR of 13.5% from 2024 to 2033.
Figure 5: Computer Vision Market Worldwide
Source: Market.us
7 Copyright© 2024. All rights reserved
AI+
Developer
2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033
10
0
$18.8
$16.6
20
$24.3
$21.4
$27.5
30
$35.5
$31.3
40
$40.3
$45.7
50
$51.9
$ 58 .
9
60
Software
Hardware
Global Computer Vision Market
Size, by Component, 2023-2033 (USD Billion)
70

This module aims to guide you through computer vision, covering image processing,
object detection with YOLO and SSD, and image segmentation techniques. It also
introduces Generative Adversarial Networks (GANs) for image generation and style
transfer, with practical hands-on exercises.
Module 7: Natural Language Processing
NLP allows computers to understand and interact with human language, supporting
tasks like text classification and sentiment analysis. It’s crucial for applications such
as chatbots, translation, and content recommendations.
In this module, you'll explore NLP and its key components, including tokenization,
stemming, lemmatization, and word embeddings for text preparation. It covers text
classification tasks such as sentiment analysis, topic modeling, and Named Entity
Recognition (NER), and introduces large language models like BERT and T5 for
question-answering systems. Hands-on projects will help you apply these concepts,
enhancing your practical skills in NLP.
Module 8: Reinforcement Learning
RL is a crucial aspect of AI that focuses on maximizing cumulative rewards. Agents,
environments, incentives, actions, and states are central elements to RL.
Within this module, you’ll learn about RL, including Q-learning, Deep Q-Networks
(DQNs), and Policy Gradient Methods. Hands-on projects, such as game
environments and robotics tasks, will help you apply RL concepts and understand
their practical use.
Module 9: Cloud Computing in AI Development
Cloud computing is essential for AI application development and deployment due to
its scalability, flexibility, and cost-efficiency. According to the Fortune Business
Insights report, the global cloud AI market is projected to grow around $397.8
billion by 2030, with a CAGR of 30.9% during the forecast period.
The module does a deep dive into cloud computing for AI, focusing on AWS, Azure,
and GCP. It highlights their scalability, flexibility, and cost-efficiency for AI
development. You'll explore high-performance computing, storage, and ML tools.
Practical experience with pre-trained models and AutoML will enhance your skills in
building and deploying scalable AI applications.
Module 10: Large Language Models
Large Language Models (LLMs) advance NLP by understanding and generating
human-like text. They improve tasks such as translation, text generation, and
information retrieval, leveraging vast datasets and deep neural networks.
AI+
Developer

In this module, you'll explore Large Language Models (LLMs) like GPT-3, BERT, and
T5. You'll learn about their capabilities in text generation, translation, and question-
answering. Hands-on projects will involve creating diverse text styles, translating
languages, and building question-answering systems to demonstrate LLMs'
versatility.
Module 11: Cutting-Edge AI Research
Cutting-edge AI research advances technology improves model capabilities, and
addresses challenges. It drives innovation in reasoning, transparency, privacy, and
adaptability, leading to more effective and trustworthy AI systems.
Within this module, you'll explore advanced AI methods, including Neuro-symbolic
AI for combining symbolic reasoning with neural networks, Explainable AI (XAI) for
model transparency, and Federated Learning for privacy-preserving data analysis. It
also covers Meta-Learning and Few-Shot Learning for adapting quickly to new tasks
with limited data.
Module 12: AI Communication and Documentation
AI communication and documentation are crucial for ensuring transparency, clarity,
and effective collaboration. They help convey complex technical details to various
audiences, facilitate understanding and reproducibility, and support ethical AI
development by addressing biases and decision-making processes.
The focus of this module is on communication and documentation for AI projects,
including techniques for different audiences and best practices for clarity and
reproducibility. It also covers the ethical aspects of AI, such as addressing biases
and ensuring transparency, with practical exercises for improving these skills.
How Can AI CERTs Help Build an AI-Ready Culture?
Despite their benefits, businesses often face challenges with AI implementation,
including skill shortages, data complexity, and integration issues. At AI CERTs, we
address these challenges by providing top-tier certifications that assist organizations
in effectively navigating and overcoming these obstacles.
AI+
Developer

Figure 7: Factors determining the lack of adopting AI Technologies
Source: IBM, OECD
Bridging the AI Skill Gap
Challenge: A shortage of developers with advanced AI expertise limits the
ability to integrate AI into software development. Many developers lack the
specialized skills needed to implement and optimize AI technologies effectively.
Solution: AI CERTs offer in-depth training programs for developers focused on
AI technologies, including ML, neural networks, and data science techniques.
Benefit: This training equips developers with the advanced skills required to
successfully implement and leverage AI in their projects, addressing the critical
skills gap in software development.
Empowering Every Developer with AI Skills
Challenge: Many developers lack access to cutting-edge AI tools, platforms, and
training resources necessary for skill enhancement.
Solution: AI CERTs provide specialized training that includes access to the latest
AI tools, platforms, and methodologies, ensuring developers remain updated
with current advancements.
Benefit: By gaining proficiency with the latest AI tools and techniques, developers
can enhance software performance, innovate solutions, and deliver high-quality
results in their projects.
At AI CERTs, we offer a strategic solution, fostering a culture primed for AI
integration and innovation. AI certification provides thorough training and industry-
recognized credentials, equipping employees to guide your company towards a
future driven by AI.
AI+
Developer
UK
France
Canada
Ireland
Austria
Germany
USA
employers of the major
42% economies believe a shortage
of Al skills
%of Employer
21%
Cost Barrier
21%
Lack of Tools
22%
Integration
Challenges
33%
25%
Data Complexity
◄ 42%
37% 41 % 42
%
48 % 49
%
47
%
Skills Gap 33%
Share of employers saying lacking Al
skills is a barrier to adopt Al (2023)
%of Employer
Why do companies struggle to adopt Al
technologies? (2023)

AI CERTs Cultivate AI Culture in Several Ways:
Our comprehensive certification delves deeply into AI principles and applications,
making AI more accessible.
Ongoing education ensures your workforce remains up to date with AI trends,
providing you with a competitive edge.
AI CERTs certification promotes knowledge sharing and breaks down
departmental silos through collaborative and cross-functional projects, essential
for effective AI implementation.
AI CERTs: Your Pathway to Becoming AI-Ready
The future of business belongs to those who harness the power of AI.
Tailored for Success: Our programs aren't one-size-fits-all. We offer specialized
training designed by industry experts to equip your workforce with the specific skills
and knowledge needed for critical AI roles.
Actionable Expertise: We emphasize hands-on learning through practical projects
and case studies, ensuring your team acquires the skills and confidence to
effectively integrate and leverage AI technology, driving innovation and business
success.
Become an AI Leader: Drive the AI revolution with AI CERTs Store. Invest in your
workforce and join us in building an AI-focused culture where your team can
harness AI's transformative potential and lead your organization to success.
AI+
Developer

Get Started
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Developer
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Contact
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New York, NY 10018

AI Development Certification Course That Transforms Your Career

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