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.
Receive a program certificate of completion
Gain experience through 25+ hands-on projects and 20+ tools with seamless access to integrated labs
Live online classes for generative AI, prompt engineering, explainable AI, ChatGPT and more
Build your professional profile with resume assistance, interview preparation and more
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.
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.
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.
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.
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.
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Use AI to categorize images of historical structures and conduct EDA to build a recommendation engine that improves marketing initiatives for historic locations.
Leverage deep learning algorithms to develop a facial recognition feature that helps diagnose patients for genetic disorders and their variations.
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.
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.
Use feature engineering to identify the top factors that influence price negotiations in the homebuying process.
Perform cluster analysis to create a recommended playlist of songs for users based on their user behavior.
Use exploratory data analysis and statistical techniques to understand the factors that contribute to customer acquisition for a retail firm.
Perform feature analysis to understand the features of water bottles using EDA and statistical techniques to understand their overall quality and sustainability.
Use deep learning to construct a model that predicts potential loan defaulters and ensures secure and trustworthy lending opportunities for a financial institution.
Use distributed training to construct a CNN model capable of detecting diabetic retinopathy and deploy it using TensorFlow Serving for an accurate diagnosis.
Use data science techniques, such as time series forecasting, to help a data analytics company forecast demand for different items across restaurants
Examine accident data involving Tesla’s auto-pilot feature to assess the correlation between road safety and the use of auto-pilot technology.
Develop a shopping app for an e-commerce company using Python.
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.
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.
Expected global AI market value by 2027
Projected CAGR of the global AI market from 2023-2030
Expected total contribution of AI to the global economy by 2030
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 application process consists of three simple steps.
Complete the application and include a brief statement of purpose.
An admission panel will shortlist candidates based on their application
An offer of admission will be made to qualified candidates.
For admission to this AI and Machine Learning Bootcamp, candidates
The admission fee for this bootcamp is $ 8,000
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.
You can pay monthly installments for Post Graduate Programs using Splitit, Affirm, ClimbCredit or Klarna payment option with low APR and no hidden fees.
We provide the following options for one-time payment
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.
Professionals eager to develop AI and ML expertise with the objective of:
Candidates for this Machine Learning Engineer Bootcamp online should have the following qualifications:
This AI and Machine Learning Certificate Bootcamp has a three-step admissions process:
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.
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.
The benefits of enrolling in our AI and ML Engineer Bootcamp include:
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.
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.
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.
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.
Some of the key topics covered in the course “Essentials of Generative AI, Prompt Engineering and ChatGPT” are:
Generative AI and its Landscape
Designing and Generating Effective Prompts
Large Language Models
ChatGPT and its Applications
Ethical Considerations in Generative AI Models
Responsible Data Usage and Privacy
The Future of Generative AI
AI Technologies for Innovation