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AI and Applications
Bootcamp
With dedicated live sessions on the latest AI topics like generative AI, prompt engineering,
ChatGPT and much more.
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
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
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
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
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
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
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
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.
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.
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
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.
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

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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