AI and Machine Learning Bootcamp - UT Dallas
Top program to Bridge the Skills Gap in a High-Demand Industry
Join our immersive bootcamp in partnership with UT Dallas Erik Jonsson School of Engineering and Computer Science to embark on an accelerated AI and machine learning journey. Acquire the expertise to unlock intelligent systems’ potential and make a significant contribution in this dynamic field.
- Admission Date Announcing Soon
- Program Duration 6 months
- Learning Format Self-Paced Learning
World’s #1 Online Bootcamp
Why Join 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
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.
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.
Electives:
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.
Program Advisors
Dinesh Bhatia
Professor and Department Head at The University of Texas at DallasProf. 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
Associate Professor at the University of Texas at DallasProf. 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
Associate Professor at the University of Texas at DallasKanad 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
Resume and LinkedIn profile optimization
Interview and assessment prep
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.
$267 billionExpected global AI market value by 2027
37.3%Projected CAGR of the global AI market from 2023-2030
$15.7 trillionExpected total contribution of AI to the global economy by 2030
Hiring Companies
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 industriesIndustryInformation Technology - 35%Software Product - 29%BFSI - 15%E-commerce - 9%Others - 12%Companies
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
Admission
An offer of admission will be made to qualified candidates.
Eligibility Criteria
For admission to this AI and Machine Learning Bootcamp, candidates
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
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.
- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.