This AI and ML Bootcamp in Austin, in collaboration with IBM, will help you advance your career as an AI and ML specialist. The AI ML Bootcamp includes masterclasses by Caltech professors and IBM specialists, a hackathon, and IBM's Ask Me Anything sessions to learn about the latest processes and technologies.
Earn a Bootcamp certificate and up to 22 CEU’s from Caltech CTME
Access to Caltech CTME Circle Membership and Caltech’s campus visit initiative
Live online classes for generative AI, prompt engineering, explainable AI, ChatGPT and more
Gain experience through 25+ hands-on projects and 20+ tools with seamless access to integrated labs
The AI & Machine Learning Bootcamp in Austin combines Caltech CTME's academic prowess with IBM's industrial ability to help you accelerate your data science career. Statistics, Python, Machine Learning, Deep Learning, Natural Language Processing, and Supervised Learning are all included in this AI and Machine Learning Bootcamp.
This bootcamp leverages Caltech's academic excellence to help you pursue a successful AI and Machine Learning career.
This immersive AI Bootcamp thoroughly explains AI concepts and gives you practical experience working with deep learning, NLP, speech recognition, generative AI, prompt engineering, and more.
This introductory course establishes a fundamental base in mathematical and statistical principles. Its objective is to cultivate critical thinking and problem-solving skills, empowering learners to analyze data, make well-informed decisions, and employ mathematical and statistical techniques in practical scenarios relevant to various industries. This course is the initial step on the program journey.
In this course, you will acquire essential Python skills that will serve as the building blocks for your journey throughout the program.
This course provides a comprehensive understanding of data science essentials, encompassing various aspects such as data preparation, model building, and evaluation. Participants will learn Python concepts like strings, Lambda functions, and lists. Additionally, they will explore topics like NumPy, linear algebra, and statistical concepts, including measures of central tendency and dispersion, skewness, covariance, and correlation.
This course provides a comprehensive overview of various machine learning concepts and their practical applications. You will explore the machine learning pipeline and gain insights into supervised learning, regression models, and classification algorithms. Additionally, you will study unsupervised learning, clustering techniques, and ensemble modeling. The course will also evaluate popular machine learning frameworks such as TensorFlow and Keras.
This AI and Machine Learning bootcamp covers deep learning with TensorFlow and is aligned with the latest industry best practices. You’ll master deep learning concepts and models using Keras and TensorFlow frameworks and implement deep learning algorithms to help you prepare for a deep learning engineer career.
This course offers a comprehensive exploration of generative AI models, specifically focusing on ChatGPT. You will acquire practical skills to understand and explore ChatGPT effectively. The course covers various topics, including generative AI, explainable AI, prompt engineering, fine-tuning, ethical considerations and more.
The capstone project allows you to implement the skills you learned throughout this bootcamp. You will solve industry-specific challenges by leveraging various AI and ML techniques. The capstone project will help you showcase your expertise to employers.
Caltech Artificial Intelligence and Machine Learning Bootcamp Masterclass
The purpose of project hours is to help you clarify any questions or concerns you may have about projects you've completed this far.
Experts will respond to any questions or concerns you may have regarding the course material in this program.
This comprehensive course provides in-depth knowledge and practical skills in the field of computer vision and advanced deep-learning techniques. You will delve into a wide range of topics, including 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.
In this course, you will gain a detailed understanding of the science behind applying machine learning algorithms to process vast amounts of natural language data. The course focuses on natural language understanding, feature engineering, natural language generation, automated speech recognition, speech-to-text conversion, text-to-speech conversion, and voice assistant devices.
This course offers a comprehensive exploration of the core concepts of reinforcement learning. You will learn how to solve reinforcement learning problems using various strategies through practical examples and hands-on exercises using Python and TensorFlow. The course covers the theory behind RL algorithms and equips you with the skills to utilize reinforcement learning as a problem-solving strategy effectively.
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Develop a shopping app for an e-commerce company using Python.
Use data science techniques, like time series forecasting, to help a data analytics company forecast demand patterns for different restaurant items.
Use exploratory data analysis and statistical techniques to understand the factors contributing to a retail firm's customer acquisition.
Perform feature analysis to understand the features of water bottles using EDA and statistical techniques to understand their overall quality and sustainability.
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.
Build a machine learning model that predicts employee attrition rate at a company by identifying patterns in their work habits and desire to stay with the company.
Use deep learning concepts, such as CNN, to automate a system that detects and prevents faulty situations resulting from human error.
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.
Leverage deep learning algorithms to develop a facial recognition feature that helps diagnose patients for genetic disorders and their variations.
Examine accident data involving Tesla’s auto-pilot feature to assess the correlation between road safety and the use of auto-pilot technology.
Use AI to categorize images of historical structures and conduct EDA to build a recommendation engine that improves marketing initiatives for historic locations.
Disclaimer - The projects have been built leveraging real publicly available data-sets of the mentioned organizations.
Dr. Rick Hefner serves as the Program Director for Caltech’s CTME, where he develops customized training programs for technology-driven organizations. He has over 40 years of experience in systems development and has served in academic, industrial, and research positions.
AI, the revolutionary digital frontier, has far-reaching impacts on business and society. With the projected exponential growth of the AI market, this course is ideal for individuals striving to stay ahead of this transformative trend!
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
The diversity of our cohorts brings intensity to classroom conversations & interactions. This AI Bootcamp caters to learners from a wide range of sectors and experiences.
My learning experience with Simplilearn was outstanding. The course material is very thoughtfully designed. The way of explaining was simple and easy to understand. The instructors were good; the support was really helpful. I recommend this course to others who want to start something in the AI domain.
The application process consists of 3 simple steps. An offer of admission will be made to the selected candidates and can be accepted by the candidates by paying the admission fee.
Fill in basic details about yourself and your interest in this program
The admissions panel reviews and shortlisted individuals get offer letters
Confirm your seat by enrolling in the program by paying the program fee
Candidates applying for AI and ML Bootcamp should have the following:
The admission fee for this program is $ 9,999
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
Candidates for this Machine Learning Bootcamp in Austin Online should have the following qualifications:
Professionals who want to learn more about AI and machine learning with the goal of:
This AI and Machine Learning Bootcamp in Austin has a three-step admissions process:
You will receive the following as part of this Best Machine Learning Bootcamp in Austin:
This curriculum is for working professionals who want to learn advanced topics, including reinforcement learning, graphical models, natural language processing, deep learning, and a strong foundation in statistics.
The curriculum may be undertaken at your leisure; however, the overall duration of the AI and Machine Learning Bootcamp program is expected to take 6-8 months to finish.
Post Graduate Program are certification programs and do not include any transcripts for WES, this is reserved only for degree programs. We do not offer sealed transcripts and hence, our PG programs are not applicable for WES or similar services.
Yes, participants in the AI & Machine Learning Bootcamp can specialize in NLP by focusing on NLP courses and projects. Bootcamps often offer elective courses or allow participants to choose project topics related to NLP. This specialization allows participants to deepen their understanding of NLP techniques and apply them to real-world language processing challenges.