Post Graduate Program in
AI and Machine Learning
With dedicated live sessions on
the latest AI topics like
Generative AI,
Prompt Engineering,
ChatGPT and more.
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Table of Contents
About the Program 3
Key Features of the Program 4
About Caltech CTME 5
Eligibility Criteria 6
Application Process 7
Who is this Program Ideal for 8
Program Outcomes 9
Learning Path 11
Tools Covered 27
Projects 28
Certificates 30
Advisory Board Member 31
About the Program
Artificial Intelligence (AI) and Machine Learning (ML) are
among the most sought after and highly compensated
digital economy skills. In the past decade, AI has given us
self-driving cars, practical speech recognition, effective web
search, and a vastly improved understanding of the human
genome. AI is so pervasive that we use it dozens of times a
day without even realizing it. Many researchers also think it
is the best way to make progress towards human-level AI.
Ride this wave and accelerate your career with this
acclaimed AI and Machine Learning program. This program
features a thorough mix of theory, projects, and extensive
hands-on practice, while leveraging Caltech’s academic
excellence. More importantly, you’ll learn about not only the
theoretical underpinnings of AI and ML, but also gain the
practical know-how to quickly and successfully apply these
tools and techniques to new problems.
This program is a blend of self-paced online videos,
live virtual classes, hands-on projects, labs and
masterclasses. Mentorship sessions will provide you with
a high-engagement learning experience and real-world
applications, helping you master essential AI and ML skills.
This program covers essential mathematical and statistical
concepts, as well as Python programming, machine
learning, deep learning, generative AI, prompt engineering,
explainable AI, ChatGPT, computer vision, natural language
processing, and more.
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Key Features of the Program
Earn a program completion Curriculum delivered in live online
certificate from Caltech CTME sessions by industry experts
Earn a Caltech CTME Circle Earn up to 22 CEUs from Caltech
Membership CTME
Masterclasses delivered by Live sessions on the latest AI
distinguished Caltech CTME trends, such as generative AI,
instructors prompt engineering, and more
Online convocation by Caltech IBM certificates for IBM courses
CTME Executive Director
Access to hackathons and Ask Me Engage in 3 capstones and 25+
Anything sessions from IBM hands-on projects from various
industry domains
Simplilearn career assistance Exposure to ChatGPT, Dall-E,
services to help you get noticed by Midjourney, TensorFlow and other
top hiring companies prominent tools
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About Caltech CTME
Founded in 1891, Caltech is a world-renowned science and engineering institute that
marshals some of the world’s brightest minds and most innovative tools to address
fundamental scientific questions and pressing societal challenges. Caltech prizes
excellence and ambition. The contributions of Caltech’s faculty and alumni have
earned national and international recognition, including 38 Nobel Prizes and nearly
60 National Medals of Science. The Institute manages the Jet Propulsion Laboratory
(JPL) for NASA.
CTME is embedded in Caltech’s Division of Engineering and Applied Science.
Caltech CTME has a unique role to play in applying the capabilities of scientists
and engineers to the challenges of today’s technology-driven businesses. Caltech
CTME applies executive education and professional development directly to real-
world problems. Caltech CTME experts teach the tools and perspectives that elevate
careers and help companies achieve their goals.
About Simplilearn
Simplilearn is the world’s #1 online bootcamp provider, enabling learners around
the globe with rigorous and highly specialized training offered in partnership with
world-renowned universities and leading corporations. We focus on emerging
technologies and skills, such as artificial intelligence, data science, cloud computing,
programming, and more — transforming the global economy. Our hands-on and
immersive training includes live virtual classes, integrated labs and projects, 24x7
support, and a collaborative learning environment. Over two million professionals
and 2000 corporate training organizations across 150 countries have harnessed our
award-winning programs to achieve their career and business goals.
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Eligibility Criteria
For admission to this AI and Macine Learning program, candidates should have:
A bachelor’s degree with an average of 50% or higher marks
Prior knowledge or experience in programming and mathematics
Preferably 2+ years of formal work experience
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Application Process
Candidates can apply to this program in 3 simple steps:
STEP STEP STEP
1 2 3
Submit an Application Admission
Application Review
Complete the application and A panel of admissions counselors An offer of admission will be
include a brief statement of will review your application made to qualified candidates. You
purpose. The latter informs our and statement of purpose to can accept this offer by paying
admissions counselors why determine whether you qualify for the program fee.
you’re interested and qualified for acceptance.
the program
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
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Who is this Program Ideal for?
This program 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
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Program Outcomes
Gain insights into the latest AI trends Learn how to apply effective prompt
like generative AI, prompt engineering, engineering techniques to improve the
ChatGPT, and much more. performance and control the behavior of
generative AI models.
Master AI and ML comprehensively, Learn to navigate data science intricacies
understanding their meaning, purpose, with expertise, encompassing processes,
scope, stages, applications, and effects. wrangling, exploration, visualization,
hypothesis building, and testing.
Be able to conduct scientific and technical Excel in mathematical computing using the
computing seamlessly using the SciPy NumPy and scikit-learn package.
package, including sub packages like
Integrate, Optimize, Statistics, IO, and
Weave.
Gain expertise in supervised and Validate machine learning models
unsupervised learning, recommendation effectively, decoding various accuracy
engines, and time series modeling. metrics.
Appreciate and apply deep learning across Navigate the layers of data abstraction
various applications. with Neural Networks, gaining unparalleled
insights into data.
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Utilize tools like Keras for building computer Become well-versed with generative
vision applications. adversarial networks (GANs).
Execute distributed and parallel computing Comprehend natural language
efficiently, leveraging high-performance understanding and natural language
GPUs. generation.
Master natural language understanding and Know how to apply Machine Learning and
generation, delving into the fundamentals Deep Learning seamlessly with NLP.
of NLP using Python’s Natural Language
Toolkit (NLTK).
Conduct text-to-speech conversion with Learn how to apply reinforcement learning
automated speech recognition. theory using Python and TensorFlow.
Master ways to solve reinforcement learning
problems through various industry-standard
strategies.
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1
2
1
3
1
4
Grasp statistical concepts such as skewness, covariance, and
correlation
Describe the concepts of the null hypothesis and alternate
hypothesis
Examine different hypothesis tests, including the Z-test and T-test
Understand the concept of ANOVA (Analysis of Variance)
Work effectively with pandas’ primary data structures: Series and
DataFrame
Utilize pandas to load, index, reindex, and merge data
Prepare, format, normalize, and standardize data using techniques
like data binning
Construct visually appealing and informative graphs using
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 Application in Python
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1
6
Evaluate different machine learning frameworks, including
TensorFlow and Keras
Build a recommendation engine using the PyTorch library
Topics Covered
Machine Learning Fundamentals
Supervised Learning
Regression Models and Applications
Classification Models and Applications
Unsupervised Learning
Ensemble Learning
Recommendation Systems
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1
8
Topics Covered:
The course covers the following topics:
Introduction to Artificial Intelligence and Deep Learning
Artificial Neural Network
Deep Neural Network and Tools
Optimization, Tuning, and Interpretability of Deep Neural
Networks
Convolutional Neural Networks (CNN)
Recurrent Neural Networks
Autoencoders
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20
Grasp the fundamentals of Recurrent Neural Networks (RNNs)
Understand the basics of PyTorch and learn how to create a neural
network using PyTorch
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
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22
Identify and implement security measures to protect ChatGPT from
unauthorized access and ensure safe and non-offensive content
generation
Learn techniques to monitor ChatGPT for performance issues and
identify and debug incorrect or unexpected outputs
Understand how to maintain and update ChatGPT models with the
latest features and improvements
Gain insights into the future of generative AI, its challenges, and the
necessary steps to unlock its full potential
Topics Covered
The course covers the following topics:
Introduction to Generative AI Models
Explainable AI
Prompt Engineering
ChatGPT
Fine-tuning ChatGPT
Ethical Considerations in Generative AI Models & ChatGPT
The Future of Generative AI
Deploying and Scaling ChatGPT
Security and Privacy Considerations
Monitoring and Debugging ChatGPT
Maintaining ChatGPT
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Capstone Project
STEP The capstone project allows you to implement the skills you will learn
throughout this program. You will solve industry-specific challenges
1 by leveraging various AI and ML techniques. The capstone project is
the final step in the core learning path and will help you showcase your
expertise to employers.
2
Learning Outcomes:
The capstone project will enhance your understanding of
3 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.
4
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Electives Advanced Deep Learning and
Computer Vision
In this advanced course, you will gain in-depth knowledge and practical
skills in computer vision and deep learning techniques. The course
covers various 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. By the end of the course, you will
have the expertise to tackle complex computer vision challenges and
successfully deploy deep learning models.
Natural Language Processing
and Speech Recognition
This advanced course comprehensively explores applying machine
learning algorithms to process vast amounts of natural language
data. It focuses primarily on natural language understanding, feature
engineering, natural language generation, automated speech
recognition, speech-to-text conversion, text-to-speech conversion,
voice assistance devices, and building Alexa skills. By the end of the
course, you will have a deep understanding of the science behind
natural language processing and speech recognition, enabling you to
develop advanced applications in these areas.
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Reinforcement Learning
This course delves into the core concepts of Reinforcement Learning
(RL), providing you with the knowledge and skills to solve RL problems
using various strategies in Python and TensorFlow. You will learn the
theoretical foundations of RL and gain practical experience in applying
RL algorithms as a problem-solving strategy. By the end of the course,
you will be equipped with the skills to use reinforcement learning in
diverse applications and scenarios effectively.
Advanced Generative AI
Immerse yourself in cutting-edge generative AI concepts through
this advanced course. Throughout the program, you’ll extensively
investigate neural networks, LLMs, their structures, and various
generative model variations, including VAEs, GANs, autoencoders, and
transformer-based models. Explore well-known gen AI models like GPT,
BERT, and T5, learning how to evaluate their performance effectively.
Engage in practical learning experiences, gaining first-hand proficiency
in constructing and launching a conversational chatbot capable of
meaningful dialogue interactions.
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Projects
Project 1 Project 5
Ecommerce Real Estate
Develop a shopping app for an ecommerce Use feature engineering to identify the top
company using Python factors that influence price negotiations in the
homebuying process.
Project 2
Food Service Project 6
Using data science techniques, such as time Entertainment
series forecasting, to help a data analytics Perform cluster analysis to create a
company forecast demand for different recommended playlist of songs for users
restaurant items. based on their user behavior.
Project 3 Project 7
Retail Human Resources
Use exploratory data analysis and statistical Build a machine learning model that predicts
techniques to understand the factors employee attrition rate at a company by
contributing to a retail firm’s customer identifying patterns in their work habits and
acquisition. desire to stay with the company.
Project 4 Project 8
Production Shipping
Perform feature analysis to understand the Use deep learning concepts, such as
features of water bottles using EDA and Convolutional Neural Networks (CNN), to
statistical techniques to understand their automate a system that detects and prevents
overall quality and sustainability. faulty situations resulting from human error
and identifies the type of ship entering the
port.
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Projects
Project 9 Project 12
BFSI Automobile
Use deep learning to construct a model that Examine accident data involving Tesla’s
predicts potential loan defaulters and ensures auto-pilot feature to assess the correlation
secure and trustworthy lending opportunities between road safety and the use of auto-pilot
for a financial institution. technology.
Project 10 Project 13
Healthcare Tourism
Use distributed training to construct a Use AI to categorize images of historical
CNN model capable of detecting diabetic structures and conduct exploratory data
retinopathy and deploy it using TensorFlow analysis (EDA) to build a recommendation
Serving for an accurate diagnosis. engine that improves marketing initiatives for
historic locations.
Project 11
Healthcare
Leverage deep learning algorithms to
develop a facial recognition feature that helps
diagnose patients for genetic disorders and
their variations.
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Certificates
This is to certify that
John Doe
has successfully completed the
POST GRADUATE PROGRAM IN
AI AND MACHINE LEARNING
on 1st January 2023
Program delivered by
Rick Hefner Krishna Kumar
Executive Director, CEO, Simplilearn
Caltech Center for Technology
and Management Education
This is to certify that
John Doe
successfully completed and received a passing grade in
Python for Data Science
(DS0101EN, provided by BigDataUniversity)
A course on skillsnet.simplilearn.com
Powered by IBM Developer Skills Network.
Issued by
Simplilearn
Jagadisha Bhat Anand Narayanan
Country Manager - Software Services Chief Product Officer
IBM India Pvt Ltd Simplilearn
December 1, 2021
Authenticity of this certificate can be validated by going to:
https://courses.skillsnet.simplilearn.com/certificates/351dd43ecaa94e92b3648e7826167f8a
Upon successful completion of this program, you will receive a certificate of
completion from Caltech CTME. You will also receive IBM certificates (sample IBM
certificate shown above) for all IBM courses, along with certificates from Simplilearn
for the courses completed in the learning path. These certificates will testify to your
skills as an AI and ML expert.
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Program Advisor
Rick Hefner
Program Director, Caltech Center for
Technology & Management Education
rhefner@caltech.edu
Rick Hefner, PhD, specializes in systems development and
maintenance; project management; Lean Six Sigma; process
improvement, technology transfer; and risk management. His
experience spans over 35 years. Dr. Hefner recently served
as Director of Process Management at Northrop Grumman
Corporation, where he managed corporate process initiatives
related to Lean Six Sigma and program management.
Previous positions at Northrop Grumman (formerly
TRW) included managing technology process initiatives
and helping to establish the corporate engineering and
program management processes. Previously, at Aerospace
Corporation, Dr. Hefner was the Director of their Software
Development department. He served as an engineer, technical
specialist, project manager, and section manager.
Dr. Hefner has also worked with companies in the
communications, electronics, and health sciences industries,
including Applied Physics Laboratory, Ares Management,
Boeing, DRS Technologies, Herbalife, Honeywell, Jet
Propulsion Laboratory, John Deere, L-3 WESCAM, Maytag,
Motorola, Pacific Bell, Raytheon, Schlumberger, Southern
California Edison, St. Jude Medical, Toshiba, U.S. Navy,
and Xerox. Dr. Hefner is credited with over 200 publications
and presentations. He earned his PhD from the University
of California, Los Angeles, in applied dynamic systems
control. He received his MS and BS from Purdue University in
interdisciplinary engineering.
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USA
Simplilearn Americas, Inc.
201 Spear Street, Suite 1100, San Francisco, CA 94105
United States
Phone No: +1-844-532-7688
INDIA
Simplilearn Solutions Pvt Ltd.
# 53/1 C, Manoj Arcade, 24th Main, Harlkunte
2nd Sector, HSR Layout
Bangalore - 560102
Call us at: 1800-212-7688
www.simplilearn.com
Disclaimer: All programs are offered on a non-credit basis and are not transferable to a degree.
SL-PGP-10-220-202308
