• Admission Date Announcing Soon
  • Program Duration 6 months
  • Learning Format Self-Paced Learning

Why Join this Program

UT Dallas Academic Excellence

 Receive a program certificate of completion

Industry-Relevant Experience

Gain experience through 25+ hands-on projects and 20+ tools with seamless access to integrated labs

Exposure to Latest AI trends

Live online classes for generative AI, prompt engineering, explainable AI, ChatGPT and more

Career Assistance Services

Build your professional profile with resume assistance, interview preparation and more


Looking to enroll your employees into this program ?

AI Engineer Bootcamp Overview

Learn, apply, and excel in your AI and machine learning career with this intensive bootcamp. Through a blend of theoretical and practical learning, you will engage in self-paced video lessons, live virtual classes led by industry experts, integrated labs, peer collaboration, and more. You will also get access to Simplilearn's Job Assistance services.

Key Features

  • Earn a certificate of completion from the University of Texas at Dallas.
  • Choose among 3 industry-oriented capstone projects.
  • Simplilearn Career Service helps you get noticed by top hiring companies
  • Attend live virtual classes led by industry experts
  • Generative AI and prompt engineering: Explore a dedicated course with live sessions
  • Work on 25+ hands-on industry-relevant projects.
  • Build expertise in 20+ tools and techniques with seamless access to integrated labs.
  • Collaborate with trainers and peers during office hours and project hours.
  • Learn about ChatGPT, G-Bard, Midjourney & other prominent tools

Machine Learning Certification Advantage

Our intensive curriculum equips you with the practical skills and knowledge to excel in your career. With structured learning, mentorship and industry projects, you will tackle complex problems and stay at the forefront of this groundbreaking field.

  • Bootcamp Certificate

    Collaborating with UT Dallas

    • Understand the meaning, purpose, scope, stages, applications and effects of AI and ML.
    • Gain an in-depth understanding of data wrangling, data exploration, data visualization, and hypothesis building.
    • Learn about the latest AI trends like generative AI, prompt engineering, ChatGPT and many more.

ML Engineer Bootcamp Details

Our AI Machine Learning Bootcamp covers a broad range of topics including Python programming, statistics, linear algebra, ANOVA, pandas, machine learning, supervised and unsupervised learning, machine learning frameworks, deep learning, CNNs, RNNs, generative AI, prompt engineering and more.

Learning Path

  • This introductory course provides a solid foundation in mathematical and statistical principles. The course aims to develop your critical thinking and problem-solving skills, enabling you to analyze data, make informed decisions and apply mathematical and statistical techniques to industry-relevant situations. This course serves as a stepping stone for further learning in this program.

  • Develop foundational Python skills that you will use throughout boot camp. Use Python to implement AI and ML algorithms, analyze data and build intelligent systems efficiently.

    • Essential data science skills like data preparation, model building, and evaluation
    • Python concepts including strings, Lambda functions, lists, NumPy, and pandas
    • Linear algebra and key statistical concepts like central tendency, dispersion, skewness, covariance, correlation, hypothesis testing, Ztest, Ttest, ANOVA
    • Data visualization using Matplotlib, Seaborn, Plotly, and Bokeh
    • Manipulating data with pandas for analysis and modeling
    • Core data science abilities in Python to prepare, build, evaluate, visualize, and analyze data
    • Types of machine learning (supervised, unsupervised, reinforcement)
    • Supervised learning - regression models and classification algorithms
    • Unsupervised learning - clustering techniques
    • Ensemble modeling
    • Machine learning frameworks - TensorFlow, Keras
    • Building a recommendation engine with PyTorch
    • Analyzing the machine learning pipeline
    • Applications of machine learning
    • Deep learning concepts and applications
    • Difference between deep learning and machine learning
    • Neural networks, forward and backward propagation
    • TensorFlow 2, Keras
    • Performance improvement techniques
    • Model interpretability
    • Convolutional neural networks (CNNs)
    • Transfer learning, object detection
    • Recurrent neural networks (RNNs)
    • Autoencoders
    • Creating neural networks in PyTorch
    • Generative AI models
    • Focus on ChatGPT
    • Build and deploy AI chatbot applications
    • Generative AI
    • Explainable AI
    • Prompt engineering
    • Fine-tuning
    • Ethical considerations
    • Deployment
    • Security
    • Monitoring
    • Debugging
    • Maintenance
  • This AI and Machine Learning bootcamp ends with a capstone project that will give you an opportunity to implement the skills you learned in Artificial Intelligence and Machine Learning. With dedicated mentoring sessions, you’ll solve an industry-relevant problem. The project is the final step in the learning path and will help you to showcase your expertise to employers.

  • This course provides in-depth knowledge and practical skills in computer vision and deep learning techniques. It covers various topics such as image formation and processing, convolutional neural networks (CNNs), object detection, image segmentation, generative models, optical character recognition, distributed and parallel computing, explainable AI (XAI), and deploying deep learning models. You will develop the expertise to successfully tackle complex computer vision challenges.

  • This course offers a detailed look at the science of applying machine learning algorithms to process large amounts of natural language data. It primarily focuses on natural language understanding, feature engineering, natural language generation, automated speech recognition, speech-to-text conversion, text-to-speech conversion, and voice assistance devices (including building Alexa skills).

  • This course will guide you through the core concepts of reinforcement learning (RL). You will learn how to solve reinforcement learning problems with various strategies using Python and TensorFlow to understand RL theory. Gain the skills to use reinforcement learning and algorithms as a problem-solving strategy.

  • Experts will respond to any questions or concerns you may have about the course material.

  • Clarify any questions or concerns you may have about course projects.

Skills Covered

  • Generative AI
  • Prompt Engineering
  • ChatGPT
  • Explainable AI
  • Conversational AI
  • Large Language Models
  • Deep Learning
  • Natural Language Processing NLP
  • Statistics
  • Computer Vision
  • Machine Learning Algorithms
  • Reinforcement Learning
  • Supervised and Unsupervised Learning
  • Speech Recognition
  • Model Training and Optimization
  • Model Evaluation and Validation
  • Ensemble Methods

Tools Covered

ChatGPTDalle.2Mid-journeypythontensorflowkerasNLKTScikitLearnMatPlotlibFlaskOpen CVDjango-n

Industry Projects

  • Project 1


    Use AI to categorize images of historical structures and conduct EDA to build a recommendation engine that improves marketing initiatives for historic locations.

  • Project 2


    Leverage deep learning algorithms to develop a facial recognition feature that helps diagnose patients for genetic disorders and their variations.

  • Project 3

    Human Resources

    Build a machine learning model that predicts employee attrition rate at a company by identifying patterns in their work habits and desires to stay with the company.

  • Project 4


    Use deep learning concepts, such as CNN to automate a system that detects and prevents faulty situations and identifies the type of ship entering the port.

  • Project 5

    Real Estate

    Use feature engineering to identify the top factors that influence price negotiations in the homebuying process.

  • Project 6


    Perform cluster analysis to create a recommended playlist of songs for users based on their user behavior.

  • Project 7


    Use exploratory data analysis and statistical techniques to understand the factors that contribute to customer acquisition for a retail firm.

  • Project 8


    Perform feature analysis to understand the features of water bottles using EDA and statistical techniques to understand their overall quality and sustainability.

  • Project 9


    Use deep learning to construct a model that predicts potential loan defaulters and ensures secure and trustworthy lending opportunities for a financial institution.

  • Project 10


    Use distributed training to construct a CNN model capable of detecting diabetic retinopathy and deploy it using TensorFlow Serving for an accurate diagnosis.

  • Project 11

    Food Service

    Use data science techniques, such as time series forecasting, to help a data analytics company forecast demand for different items across restaurants

  • Project 12


    Examine accident data involving Tesla’s auto-pilot feature to assess the correlation between road safety and the use of auto-pilot technology.

  • Project 13


    Develop a shopping app for an e-commerce company using Python.

Disclaimer - The projects have been built leveraging real publicly available data-sets of the mentioned organizations.


Program Advisors

  • Dinesh Bhatia

    Dinesh Bhatia

    Professor and Department Head at The University of Texas at Dallas

    Prof. Dinesh Bhatia is the Department Head of the Department of Electrical and Computer Engineering at UT Dallas. Throughout his career, he has engaged in research across various domains, including high-performance computing, healthcare and medical devices, and power and energy systems.

  • Benjamin Carrion Schaefer

    Benjamin Carrion Schaefer

    Associate Professor at the University of Texas at Dallas

    Prof. Benjamin Carrion Schaefer is an Associate Professor in the Department of Electrical and Computer Engineering at UT Dallas. He has experience in computer architecture, digital design, embedded systems, and reconfigurable computing and has published 120+ peer-reviewed publications.

  • Kanad Basu

    Kanad Basu

    Associate Professor at the University of Texas at Dallas

    Kanad Basu is an Assistant Professor of Electrical and Computer Engineering at UT Dallas. He earned his Ph.D. and MS degrees from the University of Florida. His interests encompass Hardware Security, Cybersecurity, Computer Architecture, VLSI systems, Cybersecurity, and Machine Learning.


Career Success

Career Success

This bootcamp readies you for career success from day one. Access live career workshops, office hours, and on-demand modules to build your job search toolkit, including resume, LinkedIn profile optimization, and much more
Grow your professional network

Grow your professional network

Resume and LinkedIn profile optimization

Resume and LinkedIn profile optimization

Interview and assessment prep

Interview and assessment prep

Salary negotiation workshops

Salary negotiation workshops

Industry Trends

The demand for AI Engineers is growing rapidly with an anticipated increase of 20% in new job opportunities. IBM predicts a surge in AI-related jobs, estimating that around 2.5 to 3 million specialized jobs will be available in the field of artificial intelligence.

Job Icon$267 billion

Expected global AI market value by 2027

Source: Fortune Business
Job Icon37.3%

Projected CAGR of the global AI market from 2023-2030

Source: Grandview Research
Job Icon$15.7 trillion

Expected total contribution of AI to the global economy by 2030

Source: PwC Global

Batch Profile

This bootcamp caters to working professionals from a variety of industries and backgrounds; the diversity of our students adds richness to class discussions and interactions.

  • The class consists of learners from excellent organizations and diverse industries
    Information Technology - 35%Software Product - 29%BFSI - 15%E-commerce - 9%Others - 12%
    Wells Fargo
    Ernst & Young

Admission Details

Application Process

The application process consists of three simple steps.


Submit Application

Complete the application and include a brief statement of purpose.


Application Review

An admission panel will shortlist candidates based on their application



An offer of admission will be made to qualified candidates.

Eligibility Criteria

For admission to this AI and Machine Learning Bootcamp, candidates

Should be 18 years old and a high school diploma or equivalent
Should have prior knowledge in programming and mathematics.
Should have 2+ years of formal work experience (preferred)

Admission Fee & Financing

The admission fee for this bootcamp is $ 8,000

Financing Options

We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.

Pay in Installments

You can pay monthly installments for Post Graduate Programs using Splitit, ClimbCredit or Klarna payment option with low APR and no hidden fees.


Other Payment Options

We provide the following options for one-time payment

  • Credit Card
  • Paypal

$ 8,000

Apply Now

Program Benefits

  • Certificate of Completion from UT Dallas.
  • Exposure to ChatGPT, generative AI, Explainable AI and more
  • Live & self-paced learning for better engagement.
  • Integrated sandboxed labs & 20+ practice tools.
  • Office & Project Hours for peer and trainer collaboration.

Program Cohorts

There are no cohorts available in your region currently

Got questions regarding upcoming cohort dates?

Machine Learning Engineer Bootcamp FAQs

  • What is covered in the AI and Machine Learning Certificate Bootcamp?

    Over six months, you’ll build a strong foundation in the fundamental principles and techniques of AI and Machine Learning. With our carefully curated curriculum, you'll explore advanced topics such as deep learning, natural language processing, computer vision and predictive analytics. An emphasis on practical training gives you the chance to apply your skills to real-world projects in integrated labs. This bootcamp is designed to equip you with the practical skills and expertise required for a successful career in AI.

  • Who should enroll in this AI Engineer Bootcamp?

    Professionals eager to develop AI and ML expertise with the objective of:

    • Improving performance in their current role.
    • Transitioning to AI and ML roles in their organization.
    • Seeking to advance their career in the industry.
    • Empowering entrepreneurial aspirations.

  • What are the requirements for enrolling in this AI And ML Engineer Bootcamp?

    Candidates for this Machine Learning Engineer Bootcamp online should have the following qualifications:

    • Be at least 18 years with a high school diploma or equivalent.
    • Have prior knowledge or experience in programming and mathematics.
    • Have 2+ years of formal work experience (preferred).

  • What is the application process for this AI Machine Learning Engineer Bootcamp?

    This AI and Machine Learning Certificate Bootcamp has a three-step admissions process:

    • All interested applicants must complete an online application.
    • Candidates will be shortlisted by an admissions panel based on their application.
    • Selected candidates will receive an offer of admission, which they can accept by paying the program fee.

  • Is there any financial assistance available?

    We offer a range of payment options to make this AI and Machine Learning Engineer Bootcamp financially feasible for all. For detailed information, please refer to our "Admissions Fee and Financing" section.

  • Why should I enroll in the AI and Machine Learning Certification?

    AI is a rapidly growing field with widespread applications across industries like health care, finance and manufacturing. Joining our AI and Machine Learning Certification will equip you with in-demand technical skills and expertise while unlocking new career opportunities in this dynamic industry.

  • What are the benefits of learning Artificial Intelligence in a Bootcamp?

    The benefits of enrolling in our AI and ML Engineer Bootcamp include:

    • Immersive, hands-on learning experience.
    • Instruction from industry experts.
    • Building a professional network.
    • Access to industry-standard tools and technologies.
    • Opportunities to work on real-world projects.
    • Fast-paced learning environment.

  • How do I become an AI Engineer online?

    Becoming an AI engineer requires a combination of technical engineering skills and a comprehensive knowledge of AI tools and technologies. You can kickstart your AI engineer journey by enrolling in our AI and Machine Learning Engineer Bootcamp. This program equips you with the necessary skills to design, develop and implement AI and ML algorithms in real-world applications. The flexibility of online learning allows you to learn at your own pace and from the comfort of your home, while still benefiting from expert instructors and peer support. 

  • What careers can I pursue with a background in artificial intelligence?

    In today's rapidly evolving landscape, companies are increasingly turning to AI-driven solutions to automate tasks and enhance decision-making processes. This trend has resulted in a growing demand for professionals with expertise in artificial intelligence. Pursuing a career in AI can open up a world of exciting opportunities, ranging from developing algorithms for autonomous vehicles to creating machine learning systems that detect fraud to enabling disease diagnosis

    Our AI Engineer Bootcamp will equip you with the skills and knowledge to create innovative solutions across various industries. Professionals with AI skills are in high demand for computer science jobs, including data scientists who use machine learning and deep learning to extract data-driven insights. NLP skills are needed to create chatbots and voice-activated assistants, while advanced AI skills can pave the way for cutting-edge technologies such as self-driving cars and autonomous robots. AI is also opening up new possibilities for professionals in finance, health care, creative arts, and more.

    Our AI and Machine Learning Engineer Bootcamp is a valuable opportunity for professionals seeking to advance their careers in this groundbreaking field. This program offers a structured learning environment, tailored specifically for professionals, to master the essential skills and concepts needed to excel in AI.

  • Can anyone enroll in the AI & Machine Learning Engineer Bootcamp?

    Learners must be at least 18 years old and have a high school diploma or equivalent to enroll in our AI and ML Engineer Bootcamp. A basic understanding of computer programming and mathematics and at least 2 years of work experience in a related field are preferred.

  • What are the key learning outcomes of the course “Essentials of Generative AI, Prompt Engineering and ChatGPT”?

    This course will give you a holistic understanding of the essentials of generative AI and its landscape, prompt engineering, explainable AI, conversational AI, ChatGPT and other LLMs.

    The key learning objectives of this course are: 

    • Understand the fundamentals of generative AI models, including the working principles and various generative AI models.

    • Comprehend the concept of explainable AI, recognize its significance and identify different approaches to achieve explainability in AI systems.

    • Apply effective prompt engineering techniques to improve the performance and control the behavior of generative AI models.

    • Understand ChatGPT, including its working mechanisms, notable features and limitations.

    • Identify and explore diverse applications and use cases where ChatGPT can be leveraged.

    • Gain exposure to fine-tuning techniques to customize and optimize ChatGPT models 

    • Recognize the ethical challenges of generative AI models and ChatGPT to ensure responsible data usage, mitigate bias and prevent misuse. 

    • Understand the potential of generative AI to revolutionize industries and explore prominent generative AI tools. 

    • Gain insights into the future of generative AI, its challenges and the steps needed to unlock its full potential.

  • What are the key topics covered in the course “Essentials of Generative AI, Prompt Engineering and ChatGPT”?

    Some of the key topics covered in the course “Essentials of Generative AI, Prompt Engineering and ChatGPT” are: 

    • Generative AI and its Landscape 

    • Explainable AI 

    • Conversational AI 

    • Prompt Engineering 

    • Designing and Generating Effective Prompts 

    • Large Language Models 

    • ChatGPT and its Applications

    • Fine-tuning ChatGPT 

    • Ethical Considerations in Generative AI Models 

    • Responsible Data Usage and Privacy 

    • The Future of Generative AI 

    • AI Technologies for Innovation

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.