Generative AI Training in Hyderabad
with
100% Placement Assistance
Table of Contents
ToggleGenerative AI Training in Hyderabad
Batch Details
Trainers Name: | Dr. Srinivas Rao, Madhuri |
Trainers Experience: | 13+ Years, 22+ Years |
Next Batch Date: | 31th July(offline),12th August(online) |
Training Modes: | Online and offline Training (Instructor-Led) |
Course Duration: | 3 months(offline & online) |
Call us at: | +91 81868 44555 |
Email Us at: | brollyacademy@gmail.com |
Demo Class Details: | ENROLL FOR FREE DEMO CLASS |
Generative AI Course Content
- Introduction to Generative AI.
- AI vs ML vs DL vs NLP vs Generative AI.
- Generative AI principles.
- What is the role of ML in Gen-AI.
- Different ML techniques (Supervised, Unsupervised, Semi-supervised & Reinforcement Learning).
- Applications in various domains.
- Ethical considerations.
- NLP essentials.
- Basic NLP tasks.
- Different text classification approaches.
- Frequency-based – Bag of words, TF-IDF, N-gram.
- Distribution Models – CBOW, Skipgram(Traditional approaches)and
word2vec, Glove. - Ensemble Methods (Random Forest, Gradient Boosting, AdaBoost) &
Traditional Machine Learning Models – Naïve Bayes, Support Vector
Machine (SVM), Decision Trees, Logistic Regression. - Deep learning techniques – CNNs, RNNs, LSTMs, GRU and
Transformers.
- Autoencoders.
- VAE’s and applications.
- GANs and it’s applications.
- Different types of GANs and applications.
- Different types of Language models
- Applications of Language models
- Transformers and its architecture
- BERT, RoBERTa, GPT variations
- Applications of transformer models
- What is Prompt Engineering
- What are the different principles of Prompt Engineering
- Types of Different Prompt Engineering Techniques
- How to Craft effective prompts to the LLMs
- Priming Prompt
- Prompt Decomposition
- Generative AI lifecycle
- What is RLHF
- LLM pre-training and scaling
- Different Fine-Tuning techniques
- What are word embeddings
- What is the use of word embeddings, where we can use it?
- Word Embeddings – Word2Vec, GloVe and FastText
- Contextual Embeddings – ELMo , BERT and GPT
- Sentence Embeddings – Doc2Vec, Infersent, Universal Sentence
Encoder - Subword Embeddings – BPE(Byte Pair Encoding), Sentence Piece
- Usecase of Embeddings.
- What is Chunking
- What is the use of chunking the document
- What are the traditional effective chunking techniques
- What are the problems and limitations with traditional chunking
techniques? - How to overcome the limitations of Traditional chunking
- Advanced Chunking Techniques:
1. Character Splitting
2. Recursive Character Splitting
3. Document based Chunking
4. Semantic Chunking
5. Agentic Chunking
- What is RAG
- What are the main components of RAG
- High level architecture of RAG
- How to Build RAG using external data sources
- Advanced RAG
- What is Langchain
- What are the core concepts of Langchain
- Components of Langchain
- How to use Langchain agents
● LlamaIndex
● What are Vector Databases
● Why do we prefer Vector Databases over Traditional Databases
● Different Types of Vector Databases: OpenSource and Close Source
● OpenSource: Chroma DB, Weaviate,Faiss,Qdrant
● Close-Source Vector Databases:Pinecone,ArangoDB,Cloud-Based
Solutions
- Supervised Finetuning
- Repurposing-Feature Extraction
- Advanced techniques in Supervised Finetuning -PEFT -LoRA, QLoRA
- Text based LLMs:
Automatic Evaluation: BULE Score, ROUGE Score, METEOR, BERT
Score.
Human Evaluation: Coherence, Factuality, Originality, Engagement - Image based LLMs:
Automatic Evaluation: Pixel-level metrics, FID (Frechet Inception
Distance), IS (Inception Score), Perceptual Quality Metrics,
Diversity Metrics.
Human Evaluation: Photorealism, Style, Creativity, Cohesiveness - Audio generation LLMs:
Automatic Evaluation: FAD (Frechet Audio Distance), IS (Inception
Score), Perceptual Quality Metrics – PAQM, PAQM – SNR (Signal-to-Noise Ratio), PAQM – PESQ (Perceptual Evaluation of Speech
Quality)
Human Evaluation:Perceptual Quality – PQ, PQ- Naturalness, PQFidelity, PQ- Musicality, Task Specific Evaluation. - Video Generation LLMs:
Automatic Evaluation: FVD (Frechet Video Distance), Inception
Score(IS), Perceptual Quality Metrics, Motion Based Metrics –
Optical Flow Error, Content-Specific Metrics.
Human Evaluation: Visual Quality, Temporal Coherence, Content
Fidelit.
- Model Deployment and Management
- Scalability and Performance Optimization
- Security and Privacy
- Monitoring and Logging
- Cost Optimization
- Model Interpretability and Explainability.
- Amazon Bedrock, Azure OpenAI
- ChatGPT, Gemini, Copilot
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In-depth coverage of essential artificial intelligence concepts, algorithms, and applications.
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We have Trained and experienced faculty for over 12 years with a deep understanding of AI methodologies.
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Opportunities to work on real-world AI problems to reinforce theoretical knowledge.
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Well-equipped classrooms and laboratories with the latest technology infrastructure.
05
Dedicated placement support to connect participants with potential employers.
06
We offer Flexible Learning Options for both offline and online courses.
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Coding sessions and practical exercises to strengthen technical abilities.
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Recognized certification upon successful completion of the Generative AI training program.
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We offer the course at an affordable price without compromising the quality of the training.
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Get to utilize our free 3-day demo sessions before joining the main course.
About Generative AI Training in Hyderabad
The Generative AI course provides participants with a comprehensive understanding of the principles, techniques, and applications of Generative AI, a transformative field within artificial intelligence. Designed to cater to a broad audience, from beginners to seasoned professionals, the course offers a structured curriculum that encompasses both theoretical foundations and practical implementation aspects.
Participants will embark on a journey through the core concepts of Generative AI, delving into the workings of state-of-the-art models like Generative Pre-trained Transformers (GPT). The curriculum covers essential topics such as natural language processing, image synthesis using Generative Adversarial Networks (GANs), and the integration of Generative AI into real-world applications. Emphasis is placed on hands-on experience, enabling participants to develop proficiency in programming languages like Python and gain practical skills in utilizing popular frameworks such as TensorFlow and PyTorch.
What is Generative AI?
Generative AI is a cutting-edge technology that falls under the broader category of artificial intelligence (AI). It refers to systems and models designed to generate content, often in the form of text, images, or other media, using advanced machine-learning techniques. The primary goal of Generative AI is to create human-like outputs by learning patterns and structures from vast datasets. This technology has gained prominence in various fields, including natural language processing, computer vision, and creative arts.
One of the key aspects of Generative AI is its ability to understand and replicate human-like language. Natural language processing models, such as GPT (Generative Pre-trained Transformer), are examples of Generative AI systems that excel in understanding context, generating coherent text, and even engaging in meaningful conversations. These models are pre-trained on extensive datasets, allowing them to learn grammar, semantics, and contextual cues to produce contextually relevant and fluent content.
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Why is Generative AI Used?
Content Creation and Automation:
Generative AI, particularly in the form of language models like GPT (Generative Pre-trained Transformer), is widely employed for content creation. It can automate the generation of coherent and contextually relevant text, ranging from articles and blog posts to marketing copy and social media content. This streamlines the content creation process, saving time and resources for individuals and businesses.
Natural Language Processing (NLP):
In the realm of Natural Language Processing, Generative AI plays a crucial role. It can understand and generate human-like text, making it instrumental in applications such as chatbots, virtual assistants, and automated customer support systems. The ability to comprehend context and respond appropriately enhances user experience and efficiency in communication.
Image Synthesis and Manipulation:
Generative AI, especially through Generative Adversarial Networks (GANs), is employed for image synthesis and manipulation. This is valuable in creative fields, allowing for the generation of realistic images, style transfer, and even the creation of entirely new visual content. It finds applications in art, design, gaming, and virtual reality.
Data Augmentation:
In fields where large datasets are essential for training machine learning models, Generative AI is used for data augmentation. It can generate additional training samples, helping improve the robustness and generalization of models. This is particularly beneficial in computer vision tasks and other domains where large labeled datasets are challenging to obtain.
Innovations in Research and Development:
Generative AI facilitates innovation in research and development by providing tools for exploring and experimenting with new ideas. Researchers use these models to generate hypotheses, simulate scenarios, and test various concepts in a more efficient and scalable manner.
Personalization and Recommendations:
In sectors like e-commerce and content streaming, Generative AI is employed to personalize user experiences. By analyzing user behavior and preferences, AI models can generate personalized recommendations, leading to improved user engagement and satisfaction.
Course outline
- Overview of generative AI concepts and applications.
- Basic programming skills using Python.
- Essential mathematical concepts for AI, including linear algebra and probability.
- Introduction to machine learning, covering supervised and unsupervised learning.
- Understanding key generative models like GPT and GANs.
- Applications of generative AI in NLP, including text generation and sentiment analysis.
- Practical application of learned concepts through coding projects.
- Addressing ethical implications and responsible AI deployment.
- Exploring how generative AI is used in industries like healthcare, finance, and arts.
- Presenting and discussing individual or group AI projects.
Generative AI Training Modes
Online Training
- Basic to advance level
- Daily recorded videos
- Live project included
- Course Material Dumps
- 100% Placement assistance
- Whatsapp Group Access
Video Course
- Lifetime Video Access
- Basic to advance level
- Doubt Clearing Session
- Course Material Dumps
- Certification Guidance
- Interview Guidance
Corporate Training
- Basic to advance level
- Flexible Batch Timings
- Live project included
- Daily recorded videos
- Doubt Clearing Sessions
- Whatsapp Group Access
Generative AI Course Job support program
Career Counseling
Individualized sessions with experienced career counselors to identify personalized career goals and strategies. Guidance on building a strong professional network within the AI industry.
Industry Connections
Access to networking events, webinars, and exclusive job fairs featuring prominent AI companies. Opportunities to connect with alumni who have successfully transitioned into AI roles.
Job Search Assistance
Guidance on navigating job boards, AI-specific job portals, and other relevant platforms. Support in identifying suitable job opportunities aligned with individual career goals.
Continuous Learning Resources
Access to updated AI resources, workshops, and webinars to stay current with industry trends. Discounts on advanced AI courses for ongoing skill development.
Mentorship Program
Matching participants with experienced mentors in the AI field for ongoing support and advice. Regular mentorship sessions to discuss career progression and overcome challenges.
Industry-Specific Workshops
Regular workshops on emerging trends, tools, and methodologies within the AI industry. Hands-on sessions with industry experts to deepen practical understanding of AI applications.
Market Trend in Generative AI
01.
The Generative AI market is anticipated to reach a substantial market size of US$66.62 billion by the year 2024.
02.
The market is expected to exhibit a robust annual growth rate, with a Compound Annual Growth Rate (CAGR) of 20.80% from 2024 to 2030.
03.
The continuous growth trend suggests that the market volume is projected to expand significantly, reaching an estimated US$207.00 billion by the year 2030.
04.
Generative AI is becoming an integral part of various sectors such as healthcare, finance, manufacturing, and more.
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contributing to the sustained growth rate as businesses recognize the transformative potential of generative models.
06.
As Generative AI becomes more pervasive, its integration into everyday applications, from virtual assistants to creative design tools.
Testimonials
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Generative AI Training In Hyderabad
Certification
- Generative AI Specialist Certification
- Certified GPT Developer
- Generative Adversarial Networks (GANs) Practitioner Certification
- Advanced Natural Language Processing (NLP) for Generative AI
- TensorFlow for Generative Models Certification
- PyTorch for Image Synthesis Certification
- AI Ethics and Responsible AI Deployment Certification
- Certified Generative Model Developer (CGMD)
- Generative AI Project Management Certification
- Certification in Creative Applications of Generative AI
Skills develop after the course
- Mastering Python and using tools like TensorFlow and PyTorch for AI implementation.
- Understanding how models like GPT and GANs work in generative AI.
- Proficiency in working with language data for tasks like sentiment analysis and text summarization.
- Creating and manipulating realistic images using algorithms like GANs.
- Effectively managing and preprocessing large datasets for model training.
- Grasping essential machine learning concepts for generative AI applications.
- Understanding basic math and statistics for developing AI algorithms.
Tools covered
- TensorFlow
- PyTorch
- Jupyter Notebooks
- Hugging Face Transformers
- Keras
- NLTK (Natural Language Toolkit)
- OpenCV
- GANLib
Job opportunities after course
Natural Language Processing (NLP) Specialist
AI Product Manager
AI Consultant
Placement program Generative AI Training
Networking Opportunities
The program organizes networking events, meet-ups, and webinars with AI experts to help participants connect with potential employers. This aids in their career growth.
Internship Opportunities
The program seeks internship opportunities for participants to gain real-world experience, serving as a stepping stone for future employment.
Career Guidance and Counseling
The program conducts mock interviews and skill assessments to help participants improve their communication skills and demonstrate their understanding of AI concepts. This prepares them for job interviews.
Job Matching Algorithm
We use advanced AI to match job seekers’ skills and interests with employers’ needs, making job placement more personalized and efficient.
Resume Enhancement Workshops
The program conducts workshops to help participants create effective resumes for AI roles, with tips on showcasing relevant projects and achievements.
Mock Interviews and Skill Assessments
The program includes career guidance and counseling, with one-on-one sessions to help participants navigate their career paths, set goals, and make informed decisions.
Pre-requisites
Generative AI Training In Hyderabad
A solid grasp of basic programming concepts is a fundamental prerequisite. Participants should be comfortable with at least one programming language, such as Python.
Participants should have the ability to write and understand code.
The Generative AI program offers an immersive learning experience in artificial intelligence.
The course has prerequisites to ensure participants have a foundational understanding to derive maximum benefit.
While the program covers machine learning concepts in-depth, a basic understanding of machine learning principles, including supervised and unsupervised learning and common machine learning algorithms, is beneficial.
Frequently Asked Questions - Generative AI Training
FAQs
Generative AI refers to a subset of artificial intelligence focused on creating or generating new content, such as images, text, or other data. It often involves the use of advanced algorithms and models, such as Generative Adversarial Networks (GANs) and language models like GPT (Generative Pre-trained Transformer). Generate AI has applications in various domains, from creative arts to data synthesis.
While traditional AI focuses on tasks like classification and prediction, Generative AI specializes in content creation. It involves training models to generate new data rather than making predictions based on existing data. This makes Generative AI well-suited for creative applications and tasks that involve generating novel content.
Generative AI finds applications in diverse fields. Some key areas include:
- Image Synthesis and Manipulation
- Natural Language Generation
- Creative Arts and Design
- Data Augmentation
- Content Creation for Virtual Assistants and Chatbots
To delve into Generative AI, having a solid understanding of programming, especially in languages like Python, is beneficial. Additionally, a grasp of machine learning fundamentals, including concepts like neural networks, is advantageous. Familiarity with frameworks like TensorFlow or PyTorch is also helpful.
Yes, Generative AI often involves the use of tools such as TensorFlow, PyTorch, and specific libraries like Hugging Face Transformers for natural language processing tasks. Additionally, depending on the application, tools like GANLib for Generative Adversarial Networks may be utilized.
Ethical considerations in Generative AI include addressing bias in generated content, ensuring responsible deployment to avoid misinformation or malicious use, and maintaining transparency in the development process. Ethical AI practices are crucial to mitigate potential societal risks.
Certainly! Generative AI is actively used in:
- Creating realistic deep fake videos
- Generating human-like text in natural language processing tasks
- Synthesizing high-quality images for creative purposes
- Enhancing data sets through data augmentation
While a computer science background is advantageous, it’s not mandatory. Many online courses and resources cater to beginners, providing a gradual introduction to programming and machine learning concepts. A strong interest and commitment to learning are essential.
Upon completing a Generative AI course, individuals can pursue careers as:
- AI Researchers/Scientists
- Data Scientists
- Machine Learning Engineers
- Natural Language Processing Specialists
- Computer Vision Engineers
- AI Product Managers
- Creative Technologists/Artists
- AI Consultants
Generative AI training in Hyderabad fee will be provided by the concern team.