Generative AI Program Details

At GoLogica students in Sacramento City, USA can participate in a complete Generative AI Training program that teaches essential skills for professional success in the expanding artificial intelligence industry.

The curriculum includes foundational generative model knowledge alongside advanced GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and methods for deep learning-based content generation. Through practical work with these models for generating different media the participants learn how effectively AI systems produce content. This course adjusts the content to serve people who want enhanced knowledge in AI along with workers in machine learning along with data science and software engineering domains.

Students will participate in hands-on activities and practical projects featured throughout the program to strengthen their expertise in AI models and data preprocessing and deployment and evaluation methods. Graduates of this program acquire the capability to produce their generative AI solutions for commercial use. The course trains students to build interview readiness while establishing their generative AI expertise through creating professional work portfolios. Upon finishing training students will obtain a certification for their professional resumes that enhances their trustworthiness in the competitive technological sector.

During the training learners can attend specialized sessions meant to help them develop their resumes with professionals who show them how to present their AI skills projects alongside their technical capabilities. Learners experience mock interview Q&A sessions within the program to learn expected generative AI questions during technical interviews and develop their presentation abilities.

The comprehensive method at GoLogica Generative AI Training provides trainees with all the necessary skills needed to find successful career opportunities in evolving AI domains. Generative AI learners of any experience level should consider this Sacramento City course because it builds essential skills required to succeed in powerful generative AI positions.

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Generative AI Syllabus

Python

Python was mainly developed for emphasis on code readability, and its syntax allows programmer to express concepts in fewer lines of code.Python is considered a scripting, language like Ruby or Perl and is often used for creating Web applications and dynamic Web content.

WEEK 5-6 20 Hours LIVE CLASS

History
Features
Setting up path
working with Python
Variable and Data Types
Operator

If
If- else
Nested if-else
For
While
Nested loops
Break
Continue
Pass

Accessing Strings
Basic Operations
String slices
Function and Methods

Accessing tuples
Operations
Working
Functions and Methods
Accessing list
Operations
Working with lists
Function and Methods

Defining a function
Calling a function
Types of functions
Function Arguments
Anonymous functions
Global and local variables
Importing module
Math module
Random module
Packages
Composition

Printing on screen
Reading data from keyboard
Opening and closing file
Reading and writing files
Functions
Exception
Exception handling
except clause
Try? Finally clause
User Defined Exceptions

Class and object
Attributes
Inheritance
Overloading
Overriding
Data hiding
Match function
Search function
Matching VS Searching
Modifiers
Patterns

Introduction
Architecture
CGI environment variable
GET and POST methods
Cookies
File upload
Connections
Executing queries
Transactions
Handling error

Socket
Socket Module
Methods
Client and server
Internet modules

Centers
Thread
Starting a thread
Threading module
Synchronizing threads
Multithreaded Priority Queue
Tkinter programming
Tkinter widgets

Data Science with Python

GoLogica provides Data Science with Python Training This course has been designed with a focus on quality and simplicity making it ideal for Beginners or for those looking for a refresher on Data Science with Python. It gives an engaging learning experience covering everything you need to know about Data Science.

WEEK 7-8 30 Hours LIVE CLASS

Overview
Life Cycle of Data Science
MapReduce Framework
Importance of Data Science
Tools in Data Science
Need of Python in Data Science

Introduction
Terminologies of statistics
Measures of Centers & Spread
Types of distributions

Overview
Data Types and Variables
Operators
if Statements and lops
Functions in R

Introduction
ML Fundamentals
Learning Technologies
Decision Tree
Random forest classifiers
Deep Learning
Time Series Analysis

ChatGpt

In this course, you will learn about the workings of OpenAI’s ChatGPT model, the model architecture, and the fine-tuning techniques. You will also discover in detail how to embed ChatGPT into various applications, control conversation flows, and fine-tune it for distinct tasks. Applied, you’ll learn about the operative utilization and development of AI chatbots as well as NLP, making ChatGPT your robust tool in AI capability.

WEEK 3-4 12 Hrs LIVE CLASS

ChatGpt Complete Course: Beginers to Advanced

What is Chat GPT?
Understanding the capabilities and limitations
Use cases and applications

Setting up the environment
Choosing a development platform
Accessing the API or using the local runtime

Formatting your input
Sending requests to the model
Handling responses
Exploring different input formats

Prompts and instructions
System and user messages
Controlling response length
Adjusting temperature and top-k sampling

Maintaining context
Multi-turn conversations
Conversation history and context management
Resolving ambiguity and clarifying queries

Introduction to fine-tuning
Preparing your dataset
Training your fine-tuned model
Evaluating and deploying your model

Writing effective prompts and instructions
Managing bias and controversial topics
Handling sensitive information and user privacy
Ensuring safety and avoiding harmful outputs

Chat GPT API advanced features
Using system-level instructions for better control
Handling rare and out-of-distribution queries
Incorporating user feedback for model improvement

Prompt Engineering

The GoLogica Prompt Engineering Course is centered around effective prompts as a means to elicit the correct responses from AI models, together with ChatGPT. This course helps you learn when and how to write great prompts that obtain clear and relevant information. You will also learn how to better tune prompts to allow for an improved fit of the prompts to the task, thereby making ChatGPT and similar systems better suited for so many applications.

Week 4-5 15 Hours LIVE CLASS

What is Prompt Engineering, and why do we care? English as a new programming language

Getting to know our GPT-based AI tool The Naive Prompting Approach and the Persona Pattern The Interview Pattern

The Chain-of-Thought Approach in Prompt Engineering

The Tree-of-Thought Approach in Prompt Engineering Controlling Verbosity and the Nova System Getting to Know watsonx Prompt Lab

Generative AI

The Generative AI Industry Applications course is built for understanding how innovative forms of AI, like the GPT, are transforming industries such as technology, entertainment, and production. You will get to understand how you can incorporate AI in automating tasks, creating content, and improving decision-making. This course enables students to apply the concepts in the project and case-study scenarios where generative AI can be of immense value.

WEEK 5-6 20 Hrs LIVE CLASS

1. Introduction to Generative AI

• Overview of Generative AI
• Real-World Applications (Art, Text, Audio, Video, Code Generation)
• Ethical Considerations in Generative AI (Bias, Deepfakes, Intellectual Property)

2. Mathematical and Statistical Foundations

• Linear Algebra (Matrix Multiplications, Singular Value Decomposition)
• Probability and Information Theory (KL Divergence, Entropy)
• Optimization Techniques (Gradient Descent, L-BFGS)

3. Programming and Tools for Generative AI

• Python Basics for Generative AI
• Libraries: TensorFlow, PyTorch, Hugging Face Transformers

4. Hands-on Exercise 1

• Implement a simple random noise generator using NumPy.

5. Autoencoders (AE)

• Introduction to Encoder-Decoder Architecture
• Variational Autoencoders (VAEs)
• Applications: Image Reconstruction, Anomaly Detection

6. Generative Adversarial Networks (GANs)

• GAN Architecture (Generator, Discriminator)
• Loss Functions in GANs (Minimax, Wasserstein Loss)
• Challenges in Training GANs (Mode Collapse, Vanishing Gradients)

7. Hands-on Project 1

• Build a sim/x.ple GAN to generate handwritten digits (MNIST Dataset).

8. Transformers and Attention Mechanisms

• Attention is All You Need: Transformer Basics
• Pretrained Models (BERT, GPT, T5)
• Fine-tuning Generative Transformers for Tasks (e.g., Text Completion, Summarization)

9. Diffusion Models

• Denoising Diffusion Probabilistic Models (DDPMs)
• Applications in Image Generation

10. Flow-Based Models

• Normalizing Flows (RealNVP, Glow)
• Density Estimation and Sampling

11. Hands-on Project 2

• Implement a text generation model using GPT or similar transformer architecture.

12. Vision-Language Models

• CLIP: Connecting Text and Images
• DALL•E and Stable Diffusion

13. Audio and Speech Generation

• Text-to-Speech (TTS) Models (Tacotron, WaveNet)
• Music Generation with RNNs or Transformers

14. Video and Animation

• Video Synthesis Techniques
• Applications in Game Development and Media

15. Hands-on Project 3

• Create a multimodal model to generate images based on text prompts (e.g., leveraging Stable Diffusion).

15. Prompt Engineering for Generative Models

• Fundamentals of Prompt Design
• Crafting Effective Prompts for AI Models
• Advanced Prompting Techniques (Zero-Shot, Few-Shot, Chain-of-Thought)
• Practical Use Cases (Chatbots, Summarization, Knowledge Extraction)
• Understanding How Generative Models Interpret Prompts
• Techniques for Text-to-Image Prompts (e.g., DALL•E, Stable Diffusion)
• Experimenting with Text-to-Text Prompts (e.g., GPT Models)
• Crafting Prompts for Multimodal AI (e.g., CLIP, Audio Generation)

19. Hands-on Activity

• Experiment with prompts in GPT-based models to perform specific tasks (e.g., creative writing, data extraction).

16. Hands-on Activity

• Create and refine prompts to generate high-quality outputs for text, image, and multimodal systems.

16. Optimization Techniques

• Fine-Tuning and Transfer Learning for Generative AI Models
• Model Compression for Deployment (Pruning, Quantization)

17. AI Deployment for Generative Models

• Deploying Models via APIs (FastAPI, Flask)
• Scaling with Cloud Platforms (AWS, Azure, GCP)

18. Hands-on Project 4

• Deploy a generative model as a web application with a user-friendly interface.

19. Ethics in Generative AI

• Risks of Generative Models (Deepfakes, Misuse of Content)
• Responsible Use and Fairness in AI

20. Regulation and Policies

• Current Regulations around Generative AI
• Understanding Copyright and Data Privacy Implications

21. Hands-on Discussion

• Group debate or case study analysis on the ethical challenges of generative AI.

22. Capstone Project

• Design, build, and deploy a comprehensive generative AI system (e.g., content generator, creative AI tool).

23. Portfolio and Resume Development

• Showcasing Projects and Contributions
• Publishing Work (GitHub, Kaggle, Personal Website)

24. Mock Interviews and Career Guidance

• Technical and Behavioral Interview Preparation
• Networking Strategies and Career Paths in Generative AI

To become a master in Generative AI?

Skills Covered

Tools Covered

Career Support

Personalized Industry Session

This will help you to better understand the Generative AI.

High-Performance Coaching

you will be able to grow your career by broadening your proficiency in Generative AI.

Career Mentorship Sessions

With this, the students will be able to decide their careers in the right way.

Interview Preparation

We Help with face-to-face interaction through mock interviews & Exams

Generative AI Course Masters Program career support

Program Fee

Program Fee: 98000 /-

88200 /-

Discount: 9800

Paypal

Debit/Credit

UPI

Generative AI Certification

GoLogica Generative AI Certification holds accreditation from major global companies worldwide. Upon completion of both theoretical and practical sessions, we offer certification to both freshers and corporate trainees. Our certification on Generative AI is recognized globally through GoLogica, significantly enhances the value of your resume, opening doors to prominent job positions within leading MNCs. Attainment of this certification is contingent upon the successful completion of our training program and practical projects.

Job Outlook

Personalized Industry Session

The U.S. Bureau of Labor Statistics (BLS) forecasts a 25% increase in employment for Generative AI from 2021 to 2031, significantly outpacing the average for all occupations. Additionally, Generative AI Ventures predicts 3.5 million unfilled cybersecurity jobs worldwide by 2025.

High-Performance Coaching

According to the BLS, Generative AI professionals are well-compensated. The median annual wage for Generative AI was $120,000 and $180,000 PA It’s depending on factors such as experience, location, and specific job responsibilities.

Job Titles

Are you preparing for a interview? If yes, our expert tutors will help you with this.

Machine Learning Engineer
AI Engineer
Data Scientist
NLP Engineer
Generative AI Researcher
Prompt Engineer

Generative AI Faq’s

The program generally takes anywhere from 6 to 7 months to complete, depending on the speed of the learner and category of learning, full-time or part-time.

The program offers a combination of real-time instructor-led and recorded modular and asynchronous designs.

Yes, you will receive a Generative AI Master’s Program certificate from GoLogica.

No, it is not necessary to have prior experience in using artificial intelligence technologies.

With this program, you will learn popular AI skills, receive individualized job search assistance, and find the most attractive positions for working in the AI sector.

You will apply AI in practice throughout the entire course, creating conversational agents, deploying AI models, and applying AI to selected industry issues.

You can apply for some job positions like AI Engineer, Machine Learning Engineer, NLP Specialist, Data Scientist, and Prompt Engineer.

The average salaries of AI engineers can cost around 12 lakhs per annum in India.

Prompt engineering refers to creating suitable prompts for attaining the right results from an AI model such as ChatGPT. This part is an important step when it comes to achieving the best performance levels of AI models.

No, it is not necessary. However, having a background in data science is an advantage.

This program is concentrated mainly on Generative AI models as well as their practical usage, providing the best approaches to comprehending the newest trends in AI systems such as ChatGPT and Prompt Engineering.

Learners receive instant technical assistance, the possibility of learning directly from AI specialists, and project and assignment assistance.

Basic programming knowledge, preferably in Python, is recommended. Familiarity with data science concepts will be useful.

You will have access to live sessions, recorded tutorials, study materials, project workbooks, and a peer community for collaboration and networking.

Yes, you can attend pre-recorded sessions and complete assignments at your convenience. However, live sessions are scheduled and should be attended in real time.

Yes, you will be able to access all recorded sessions and course materials after the program.

Generative AI is widely used in industries such as finance, entertainment, marketing, and manufacturing for tasks such as content creation, automation, and decision-making.

Yes, GoLogica does offer guaranteed job placement assistance, which includes writing CVs, conducting interviews, and offering genuine job links.

Yes, the program is flexible. It offers part-time learning options with pre-recorded videos and weekend live sessions.

You will be given constant career advice, high-performance counseling, and multiple opportunities to get in touch with professionals who assist users with their job search and interviews.

You will follow how AI can be applied in the following fields: natural language processing (NLP), healthcare automation, finance, and writing.

You can register from the GoLogica official website and choose the Generative AI Master’s Program to register for the course.

The program is also available for international students. Both lectures and all the teaching materials are available online.

Yes, there are coding assignments, as integration with programming is a part of the program, mainly in Python and machine learning.

Your lab will include programming in Python, Tensorflow, or any model developed by OpenAI like ChatGPT, Natural language Processing frameworks like NLP, etc.

Yes, there are various quizzes and individual and group project check-ups that tell you if you’re on track in meeting the learning objectives.

Yes, you can slow down or speed up according to your preference, but the restrictions are well spelled out to switch between part-time and full time; you will have to contact the support team to discuss this.

Indeed, our program is crowned with a capstone project during which you will implement generative AI in practice to address a specific challenge.

In case you missed yesterday’s live, you can always watch the recorded version to get the necessary information.

The growth in demand for AI professionals is almost constant, and the successful completion of this program means one will be qualified to advance in any particular field in terms of Artificial Intelligence.

Generative AI Training in Sacramento, US

12th Mar 2025

6 Months

Online Bootcamp

Why Join this Program?

GoLogica Acadamic

Industry Experience

Latest AI Trends

Hands-on Experience

Learners Achievement

Maximum Salary Hike

150%

Average Salary Hike

75%

Haring Partners

2000+

Our Alumini

Generative AI Program Details

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Generative AI Syllabus

Python

WEEK 5-6 20 Hours LIVE CLASS

Course Content

Introduction and basic Syntax

Conditional & Control Statements and Looping

String Manipulation

Dictionaries

Functions and Modules

Input-Output and Exception Handling

OOPs concepts and Regular expressions

CGI and Database

Networking

Multithreading and GUI Programming

Data Science with Python

WEEK 7-8 30 Hours LIVE CLASS

Course Content

Introduction to Data Science

Statistics

R Programming

Data Visualization, Machine Learning with R & Python

ChatGpt

WEEK 3-4 12 Hrs LIVE CLASS

Course Content

Introduction to Chat GPT

Getting Started

Basic Usage

Customizing Model Behavior

Handling Complex Conversations

Fine-tuning Chat GPT

Best Practices and Tips

Advanced Topics

Prompt Engineering

Week 4-5 15 Hours LIVE CLASS

Course Content

Introduction to Prompt Engineering

Getting Started with Prompt Engineering

The Chain-of-Thought Approach

Advanced Techniques

Generative AI

WEEK 5-6 20 Hrs LIVE CLASS

Course Content

Module 1: Foundations of Generative AI

1. Introduction to Generative AI

2. Mathematical and Statistical Foundations

3. Programming and Tools for Generative AI

4. Hands-on Exercise 1

Module 2: Fundamentals of Deep Generative Models

5. Autoencoders (AE)

6. Generative Adversarial Networks (GANs)

7. Hands-on Project 1

Module 3: Advanced Generative Models

8. Transformers and Attention Mechanisms

9. Diffusion Models

10. Flow-Based Models

11. Hands-on Project 2

Module 4: Multimodal Generative AI

12. Vision-Language Models

13. Audio and Speech Generation

14. Video and Animation

15. Hands-on Project 3

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