Bootcamp_AIApps.pdf
- 1. AI and Applications Bootcamp With dedicated live sessions on the latest AI topics like generative AI, prompt engineering, ChatGPT and much more.
- 2. About the Program 3 Key Features of the Program 4 Eligibility Criteria 6 Application Process 7 Who is this Bootcamp Ideal for? 8 Program Outcomes 9 Learning Path 1 1 Tools Covered 26 Projects 27 Certificate s 29 Table of Contents
- 3. In the digital economy, machine learning (ML) and artificial intelligence (AI) are two of the most in-demand and highly paid skills. We now have self-driving cars, useful speech recognition, efficient web search, and a much better understanding of the human genome thanks to artificial intelligence (AI) in the last ten years. We use artificial intelligence (AI) hundreds of times a day without even realizing it. It is also regarded by many experts as the most effective path towards human-level AI.. With this highly regarded AI and Applications bootcamp, you can ride this wave and advance your career. This program includes a wide range of practical experience, projects, and theory. More significantly, you will acquire the practical knowledge necessary to swiftly and effectively apply these tools and techniques to novel problems in addition to learning about the theoretical foundations of AI and ML. A combination of self-paced online videos, live virtual classes, practical projects, labs, and masterclasses make up this bootcamp. You will gain practical applications and a highly engaging learning experience from mentoring sessions, which will assist you in mastering critical AI and ML skills. Comprehensive exposure to various AI concepts and workflows, including prompt engineering, explainable AI, machine learning, deep learning, computer vision, natural language processing, speech recognition, reinforcement learning, generative AI, and much more, will be provided. About the Program
- 4. UCSD program completion certificate Live interactive sessions by industry experts on latest AI trends, such as generative AI, prompt engineering, and more Live online masterclasses delivered by instructors Seamless access to integrated labs Industry projects and Several hands-on projects from various industry domains Career Assistance helps you get noticed by top hiring companies Gain exposure to ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools 8X higher interaction in live online classes by industry experts Key Features of the Program
- 5. For admission to this AI and Macine Learning bootcamp, candidates should have: At least 18 years and have a High School Diploma or equivalent. Prior knowledge or experience in programming and mathematics Preferably 2+ years of formal work experience Eligibility Criteria
- 6. Complete the application and include a brief statement of purpose. The latter informs our admissions counselors why you’re interested and qualified for the bootcamp. A panel of admissions counselors will review your application and statement of purpose to determine whether you qualify for acceptance. An offer of admission will be made to qualified candidates. You can accept this offer by paying the program fee. Candidates can apply to this bootcamp in 3 simple steps: Talk to an Admissions Counselor We have a team of dedicated admissions counselors here to help guide you in the application process and related matters. They are available to Address questions related to the application Assist with financial aid (if required) Help you better understand the program and answer your questions Submit an Application STE P 1 Application Review STE P 2 Admission STE P 3 Application Process
- 7. Who is this Bootcamp Ideal for? This bootcamp caters to professionals from a variety of industries and backgrounds. The diversity of our students adds richness to class discussions and interactions. Roles in this space require a combination of experience and an understanding of tools and technologies. This program is ideal for professionals looking for a career transition into the field of AI and ML, who have knowledge or prior experience in programming and mathematics, and an analytical frame of mind. Professionals eager to develop AI and ML expertise with the objective of: Enhancing effectiveness in their current role Transitioning to AI and ML roles in their organization Seeking to advance their career in the industry Giving shape to entrepreneurial aspirations
- 8. Program Outcomes Understand the meaning, purpose, scope, stages, applications, and effects of AI and ML Gain an in-depth understanding of data science processes, data exploration, data visualization, hypothesis building, and testing Learn about some of the latest AI trends like generative AI, prompt engineering, ChatGPT, and much more Apply effective prompt engineering techniques to improve the performance and control the behavior of generative AI models Perform scientific and technical computing using the SciPy package and its sub- packages, such as Integrate, Optimize, Statistics, IO, and Weave Gain expertise in mathematical computing using the NumPy and scikit-learn package Master the concepts of supervised and unsupervised learning, recommendation engines, and time series modeling Validate machine learning models and decode various accuracy metrics Understand and appreciate Deep Learning and its applications Gain knowhow of Neural Networks, and traverse the layers of data abstraction which will help you understand data like never before
- 9. Perform distributed and parallel computing using high-performance GPUs Learn about natural language understanding and natural language generation Understand the fundamentals of natural language processing (NLP) using the most popular library, Python’s Natural Language Toolkit (NLTK) Understand how to apply Machine Learning and Deep Learning with NLP Perform text-to-speech conversion with automated speech recognition Use Python and TensorFlow to understand reinforcement learning theory Learn how to solve reinforcement learning problems through a variety of strategies Explore tools, such as Keras, to build computer vision applications Become familiar with generative adversarial networks (GANs)
- 10. Learning Path Core Courses Industry Projects Core: Applied Data Science with Python Core: Deep Learning with Keras and TensorFlow Foundations: Mathematics & Statistics Essentials Foundations: Programming Refresher Core: Machine Learning Core: Essentials of Generative AI, Prompt Engineering & ChatGPT Electives Advanced: ADL and Computer Vision Advanced: Reinforcement Learning Advanced: Natural Language Processing and Speech Recognition Office Hours Project Hours
- 11. Foundations: Programming Refresher In this course, you will acquire essential Python skills that will serve as one of the building blocks for your journey throughout the program. Learning Outcomes: Gain knowledge of procedural and object-oriented programming Understand the benefits and advantages of using Python as a programming language Install Python and its integrated development environment Familiarize yourself with Jupyter Notebook and its usage Implement Python identifiers, indentations, and comments effectively Understand Python’s data types, operators, and string functions Learn about different types of loops in Python Explore variable scope within functions Explain the concepts of object-oriented programming and its characteristics Describe methods, attributes, and access modifiers in Python Gain an understanding of multi-threading Topics Covered Fundamentals of Programming Introduction to Python Programming Python Data Types and Operators Conditional Statements and Loops in Python Python Functions Object-Oriented Programming Concepts with Python Threading STE P 4 3 5 6 7 1 2
- 12. Core: Applied Data Science with Python This course provides a comprehensive understanding of data science essentials, including data preparation, model building, and evaluation. Participants will learn 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. The course also covers hypothesis testing, such as Z-test, T-test, and ANOVA, and data manipulation using pandas. Participants will develop data visualization skills using popular libraries like Matplotlib, Seaborn, Plotly, and Bokeh. Learning Outcomes: Explain the fundamentals of data science and its practical applications Explore the processes of data preparation, model building, and evaluation Apply Python concepts like strings and comprehensively understand Lambda functions and lists Develop a solid understanding of the fundamentals of NumPy Explore array indexing and slicing techniques Apply principles of linear algebra in data analysis Understand the application of calculus in linear algebra Calculate measures of central tendency and dispersion Gain a clear understanding of statistical concepts such as skewness, covariance, and correlation Describe the null hypothesis and alternative hypothesis Examine different hypothesis tests, including Z-test and T- test STE P 1 2 3 4 5 6 7
- 13. Understand the concept of ANOVA Work with pandas’ two primary data structures: Series and DataFrame Utilize pandas for tasks such as data loading, indexing, reindexing, and data merging Prepare, format, normalize, and standardize data using data binning techniques Create visualizations with Matplotlib, Seaborn, Plotly, and Bokeh Topics Covered Introduction to Data Science Essentials of Python Programming NumPy Linear Algebra Statistics Fundamentals Probability Distributions Advanced Statistics Working with pandas Data Analysis Data Wrangling Data Visualization End-to-End Statistics Applications in Python
- 14. Core: Machine Learning This course provides a comprehensive overview of various machine learning types 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. Evaluate popular machine learning frameworks such as TensorFlow and Keras, and build a recommendation engine using PyTorch. Learning Outcomes: Examine the different types of machine learning and their respective characteristics Analyze the machine learning pipeline and understand the key operations involved in machine learning operations (MLOps) Learn about supervised learning and its wide range of applications Understand the concepts of overfitting and underfitting and employ techniques to detect and prevent them Analyze various regression models and their suitability for different scenarios Identify linearity between variables and create correlation maps List different types of classification algorithms and understand their specific applications Master various types of unsupervised learning methods and when to use them Gain a deep understanding of different clustering techniques within unsupervised learning Examine different ensemble modeling techniques such as bagging, boosting, and stacking STE P 1 2 3 4 5 6 7
- 15. Evaluate and compare different machine learning frameworks, including Sklearn,TensorFlow and Keras Build a recommendation engine using PyTorch Topics Covered Machine Learning Supervised Learning Regression and its Applications Classification and its Applications Unsupervised Learning Clustering techniques and its applications Ensemble Learning Recommendation Systems
- 16. Core: Deep Learning with Keras and TensorFlow This comprehensive course provides you with the necessary knowledge and skills to effectively deploy deep learning tools using AI/ML frameworks. You will explore the fundamental concepts and practical applications of deep learning while gaining a clear understanding of the distinctions between deep learning and machine learning. The course covers a wide range of topics, including neural networks, forward and backward propagation, TensorFlow 2, Keras, performance optimization techniques, model interpretability, convolutional neural networks (CNNs), transfer learning, recurrent neural networks (RNNs), autoencoders, and creating neural networks. By the end of the course, you will have a solid foundation in deep learning principles and the ability to build and optimize deep learning models effectively using Keras and TensorFlow. Learning Outcomes: Differentiate between deep learning and machine learning and understand their respective applications Gain a thorough understanding of various types of neural networks Master the concepts of forward propagation and backward propagation in deep neural networks (DNNs) Gain insight into modeling techniques and performance improvement in deep learning Understand the principles of hyperparameter tuning and model interpretability Learn about important techniques such as dropout and early stopping, and implement them effectively STE P 1 2 3 5 6 7 4
- 17. Develop expertise in convolutional neural networks (CNNs) and object detection Acquire a solid understanding of recurrent neural networks (RNNs) Gain familiarity with PyTorch and learn how to create neural networks using this framework Topics Covered: Introduction to Deep Learning Artificial Neural Networks Deep Neural Networks TensorFlow Model Optimization and Performance Improvement Convolutional Neural Networks (CNNs) Transfer Learning Object Detection Recurrent Neural Networks (RNNs) Transformer Models for Natural Language Processing (NLP) Getting Started with Autoencoders PyTorch
- 18. Core:Essentials of Generative AI, Prompt Engineering & ChatGPT This course offers a thorough exploration of generative AI models, specifically emphasizing ChatGPT. Participants will acquire a comprehensive grasp of the fundamentals of generative AI and its scope, prompt engineering, explainable AI, conversational AI, ChatGPT, other large language models and much more. Learning Outcomes: Acquire a solid foundation in generative AI models, encompassing their core principles and various types of generative AI models. Grasp the concept of explainable AI, understand its importance, and distinguish between different approaches for achieving explainability in AI systems. Utilize effective prompt engineering techniques to enhance performance and regulate the behavior of generative AI models. Develop a comprehensive understanding of ChatGPT, including its operational mechanisms, notable features, and limitations. Explore a range of applications and scenarios where ChatGPT can be effectively utilized. Familiarize yourself with fine-tuning techniques to personalize and optimize ChatGPT models. STE P 1 2 3 6 4 5 7
- 19. Recognize the ethical challenges of generative AI models to ensure responsible data usage, mitigate bias and prevent misuse. Comprehend the transformative potential of generative AI across industries and explore prominent generative AI tools. Gain insights into the future of generative AI, its challenges, and the necessary steps to unlock its full potential. Topics Covered: 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
- 20. Capstone/Industry Project The capstone project allows you to implement the skills you will learn 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. Learning Outcomes: The capstone project will enhance your understanding of the Artificial Intelligence decision cycle, including performing exploratory data analysis, building and fine-tuning a model with cutting-edge AI-based algorithms, and representing results. STE P 1 2 3 4 5 6 7
- 21. Advanced: Natural Language Processing and Speech Recognition Advanced: Advanced Deep Learning and Computer Vision 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 and deploying deep learning models. By the end of the course, you will have the expertise to tackle complex computer vision challenges and successfully deploy deep learning models in various applications. 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 assistance devices. Electives STE P 1 2 3 4 5 6 7
- 22. Advanced: Reinforcement Learning Office Hours Project Hours 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. By the end of the course, you will be proficient in using RL algorithms to tackle a wide range of real-world challenges. 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.
- 23. Tools Covered Academic Master Classes Attend online interactive Master Classes conducted by the instructor from Object Automation and get insights about advancements in the AI domain.
- 24. Projects AI in Health Care AI in Banking AI in Logistics Chat bots Recommendation System Emotion Detection Object Detection Sentiment Analysis Face Recognition Sales Predictor Diseases Detection Industrial Application AI in Agriculture AI in Ecommerce
- 25. Certificates Upon completing the AI and Machine Learning Bootcamp, you will receive a certificate of completion from Object Automation. These certificates will testify to your skills as an AI and ML expert.
- 26. USA Object Automation System Solutions Inc 11864, Bernardo Terrace, Unit D, San Diego, CA – 92128. Phone: 858 603 3472 INDIA No 1, Nehru Street, Co-Operative Nagar, Adambakkam, Chennai – 600088. Phone : +91 7397784815 Email : hr@object-automation.com Contact Us