Full Stack Generative AI Career Path- Beginners
Go from beginner to a Generative AI expert in just 4 months and create your own projects with this hands-on bootcamp.
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About This Course
Get started with Generative AI and gain the skills to build real-world applications from day one. This course will teach you how to create effective prompts, develop AI-powered tools using LangChain and LlamaIndex, and explore NLP with Hugging Face. Designed for beginners, it’s your first step toward a rewarding career in AI, with practical knowledge you can use across industries.
What You'll Learn
Core Generative AI Skills You Will Learn
- Learn how to develop, train, and fine-tune various generative models such as GANs, VAEs, and autoregressive models to generate realistic images, text, and other data.
- Gain a deep understanding of Transformer architectures, including how they work, their evolution, and their application in natural language processing tasks.
- Master the art of creating effective prompts to enhance the performance and relevance of AI-generated responses, including techniques for zero-shot, few-shot, and chain-of-thought prompting.
- Learn strategies for collecting, cleaning, and augmenting datasets to ensure high-quality input for training generative models, which is crucial for model accuracy and performance.
- Acquire skills in integrating generative AI models using APIs from platforms like OpenAI, Hugging Face, and Google Cloud AI, and deploying these models in real-world applications.
- Understand the ethical implications of generative AI, including how to identify and mitigate biases, ensure fairness, and address privacy concerns in AI applications.
Current Generative AI Tools and Libraries you will learn Learn
- TensorFlow: An open-source platform for machine learning, ideal for building and training generative models like GANs and VAEs.
- PyTorch: A flexible, open-source library for developing and experimenting with generative models using dynamic computational graphs.
- Hugging Face Transformers: Provides easy-to-use interfaces for state-of-the-art transformer models, facilitating the integration and fine-tuning of models like GPT-3, BERT, and T5.
- Keras: A high-level neural networks API that simplifies building and training deep learning models on top of TensorFlow, CNTK, or Theano.
- OpenAI API: Offers access to advanced generative models like GPT-3 through an easy-to-use API, enabling powerful AI capabilities without extensive machine learning expertise.
- LangChain: An open-source framework that simplifies the development of applications powered by large language models (LLMs) by chaining interoperable components.
- LlamaIndex: Provides a simple interface to connect LLMs with data, building indices for efficient and accurate information retrieval.
- Gemini API: A service that provides cutting-edge generative AI capabilities for various applications, offering powerful tools for AI-driven content creation and analysis.
Essential communication and business skills relevant to the generative AI domain:
- Stakeholder Communication: Clearly explain generative AI capabilities, limitations, and benefits to stakeholders, aligning project goals with business objectives.
- Comprehensive Documentation: Maintain detailed documentation of generative AI models, workflows, and results to ensure team members and stakeholders can easily understand and collaborate.
- Project Management: Effectively plan, coordinate, and monitor generative AI projects, ensuring timely completion and adherence to business goals.
- Insightful Data Presentation: Translate complex generative AI data and model outputs into understandable and actionable insights for non-technical audiences.
- Interdisciplinary Collaboration: Facilitate seamless cooperation between data scientists, engineers, and business teams to integrate generative AI solutions into business processes and strategies.
Problem Solving and Design Thinking Skills you will learn
- Learn to apply these models to solve real-world problems across different industries, especially BFSI.
- Utilize AI to develop innovative solutions for complex problems.Enhance your ability to think creatively about how AI can be used to address unique challenges.
- Apply design thinking principles to create user-centric AI solutions.Learn to empathize with end-users, define clear problem statements, ideate solutions, and iterate through prototyping and testing.
- Develop skills in creating and testing prototypes of AI applications, ensuring they meet user needs and solve the intended problems effectively.
- Understand the importance of data quality and relevance in developing effective AI solutions.
- Learn best practices for deploying AI solutions at scale, ensuring reliability and performance.
- Learn strategies to ensure fairness, transparency, and accountability in AI solutions.
- Enhance your ability to work collaboratively with interdisciplinary teams to develop AI solutions.
Curriculum Designed For Career Success
Prompt Engineering for LLMs
6 Lectures
Learn to design, optimize, and evaluate prompts for effective AI interaction, including system workflows and ethical considerations.
- 1.1 Foundations of Prompt Engineering:
Understand key concepts, LLM behavior, and optimization techniques.
- 1.2 Intermediate Prompting Techniques:
Explore dynamic, chain-of-thought, and few-shot prompting through hands-on applications.
- 1.3 Advanced Prompting Strategies:
Master prompts for specific use cases, multimodal models, and advanced techniques.
- 1.4 Prompt Engineering in System Design:
Design workflows, manage context, and create meta-prompts for scalable systems.
- 1.5 Optimization and Evaluation:
Optimize performance, balance cost, and evaluate output accuracy using tools like RLHF.
- 1.6 Ethical Considerations:
Address bias, fairness, and transparency while crafting interpretable prompts.
Module 2: LangChain for LLM Application Development
3 Lectures
Learn to develop advanced LLM-powered applications using LangChain’s tools and workflows.
- 2.1 Introduction to LangChain & Chatbot Mechanics:
Understand LangChain components and build chatbots with memory strategies.
- 2.2 Chains and Agents:
Design sequential chains, integrate custom tools, and master LangChain Expression Language.
- 2.3 Retrieval Augmented Generation (RAG):
Create RAG applications with vector databases and context-rich retrieval prompts.
Module 3: LangGraph for LLM Application Development
5 Lectures
Explore LangGraph workflows to create intelligent systems with memory and user interaction.
- 3.1 Introduction to LangGraph:
Build simple workflows with agents, memory, and deployment basics.
- 3.2 State and Memory:
Master state management and memory-enabled chatbot development.
- 3.3 UX and Human-in-the-Loop:
Enhance user experience with dynamic feedback and real-time workflows.
- 3.4 Building Your Assistant:
Design complex workflows using parallelization and map-reduce techniques.
- 3.5 Long-Term Memory:
Integrate schemas and datasets for personalized long-term memory solutions.
Module 4: Mastering Retrieval-Augmented Generation (RAG) for AI Engineers
5 Lectures
Build and optimize RAG systems for knowledge-based AI applications.
- 4.1 Introduction to RAG:
Understand RAG systems, components, and real-world applications.
- 4.2 Data Preparation and Indexing:
Learn chunking strategies and indexing for optimal retrieval.
- 4.3 Query Optimization:
Enhance retrieval relevance with query compression and optimization pipelines.
- 4.4 Retriever Implementation:
Implement and refine dense, sparse, and hybrid retrieval methods.
- 4.5 The Generative Component:
Use transformer models and fine-tuning for effective generative pipelines.
Module 5: Mastering LlamaIndex for LLM-Driven Applications
7 Lectures
Develop intelligent workflows using LlamaIndex for data management and querying.
- 5.1 Introduction to LlamaIndex and LLM Basics:
Set up workflows and understand LlamaIndex architecture.
- 5.2 Core Concepts:
Explore privacy best practices and address security challenges in LLM applications.
- 5.3 LlamaIndex Fundamentals:
Integrate LLMs and compare popular models for performance.
- 5.4 Practical Implementation:
Build and optimize data indexes for efficiency.
- 5.5 Advanced Prompt Engineering:
Refine query performance and utilize role-based strategies.
- 5.6 Data Indexing:
Master embedding models, metadata handling, and vector storage.
- 5.7 Querying Data:
Explore natural language querying and external source integration.
Module 6: Mastering Hugging Face for NLP and Machine Learning
6 Lectures
Leverage Hugging Face tools to build and deploy NLP and ML applications.
- 6.1 Introduction to Hugging Face:
Navigate tools like transformers, datasets, and tokenizers.
- 6.2 Transformers and Pre-Trained Models:
Use and fine-tune pre-trained transformer models.
- 6.3 Tokenization and Data Preparation:
Master tokenization and preprocess datasets for NLP tasks.
- 6.4 Training and Fine-Tuning:
Build training pipelines and optimize model performance.
- 6.5 Deployment and Inference:
Deploy models using APIs and scalable solutions like Hugging Face Spaces.
- 6.6 Advanced Topics:
Explore custom tokenizers, generative AI, and large dataset handling.
Assessments Involved
7 Assessment Categories
Evaluate your understanding and practical application of generative AI concepts, workflows, and tools through real-world scenarios and hands-on exercises.
- Module 1: Assess the ability to design and optimize prompts, develop workflows, and address ethical considerations.
- Module 2: Evaluate chatbot mechanics, chain workflows, and RAG integration via practical projects.
- Module 3: Test skills in building LangGraph workflows, managing state and memory, and creating long-term memory-enabled applications.
- Module 4: Assess proficiency in RAG systems, data preparation, retriever implementation, and generative pipelines.
- Module 5: Measure understanding of LlamaIndex fundamentals, data indexing, querying, and automation by building functional workflows.
- Module 6: Test expertise in Hugging Face tools, including model fine-tuning, tokenization, and deployment.
- Final Assessment: Integrate learnings across modules in a comprehensive evaluation project.
Capstone Projects Involved
6 Capstone Projects
Apply your knowledge to create impactful AI-driven applications tailored to solve real-world challenges in your domain.
- Module 1: Develop a domain-specific AI assistant with advanced prompts and workflows.
- Module 2: Create a LangChain-powered application integrating RAG workflows and memory-enabled chatbots.
- Module 3: Build and deploy a LangGraph application with memory, advanced workflows, and long-term personalization.
- Module 4: Design a RAG system addressing challenges with data retrieval, query optimization, and generative AI pipelines.
- Module 5: Build an intelligent chatbot using LlamaIndex, combining indexing, querying, and automation.
- Module 6: Develop and deploy an NLP application using Hugging Face tools for dataset preparation, model fine-tuning, and scalable deployment.
Case Studies
Industry Case Studies You Will Work On
Risk Assessment with Deep Learning
Predict the likelihood of credit default for loan applicants, helping risk analysts and credit officers improve loan approval processes and reduce default rates.
Skills learned:
Data Preprocessing
Feature Engineering
Model Evaluation
Customer Behavior Analysis
Analyze customer behavior patterns to predict future actions, such as account closure or product churn, enabling targeted strategies for customer retention.
Skills learned:
Sequential Data Processing
Time Series Analysis
Predictive Modeling
Fraud Detection Using Deep Learning
Detect fraudulent transactions in real time by analyzing transaction data patterns, improving fraud detection capabilities and reducing financial losses.
Skills learned:
Anomaly Detection Techniques
Feature Extraction
Model Evaluation
Customer Sentiment Analysis
Analyze customer sentiments from feedback data to gauge satisfaction levels, helping teams identify areas for improvement in banking services.
Skills learned:
Natural Language Processing (NLP)
Sentiment Analysis Techniques
Chatbot Development for Customer Service
Develop AI-powered chatbots to handle customer inquiries and provide instant assistance, improving customer experience and service efficiency.
Skills learned:
Natural Language Processing (NLP)
Conversational AI
Model Deployment
Investment Portfolio Optimization
Optimize investment portfolios by predicting asset performance and rebalancing strategies, helping wealth managers enhance returns and manage risks effectively.
Skills learned:
Time Series Analysis
Predictive Modeling
Portfolio Optimization Techniques
Loan Approval Automation
Develop an automated system to assess loan applications, reducing manual intervention and accelerating loan processing for improved efficiency.pre
Skills learned:
Predictive Modeling
Risk Assessment Techniques
Model Deployment
Predictive Pricing Models
Develop predictive models to forecast pricing trends and optimize strategies, enabling pricing teams to align product pricing with market dynamics.
Skills learned:
Time Series Analysis
Predictive Modeling
Pricing Analytics
Predictive Lending Models
Develop personalized lending models to offer tailored loan products, helping credit departments improve customer satisfaction and portfolio performance.
Skills learned:
Predictive Modeling
Customer Segmentation
Personalized Recommendation Systems
Network Intrusion Detection
Detect and prevent cyberattacks by analyzing network traffic patterns, strengthening cybersecurity measures and protecting sensitive banking data.
Skills learned:
Intrusion Detection Techniques
Anomaly Detection
Cybersecurity Fundamentals
Advanced Fraud Detection Techniques in Banking
Learn the skills and knowledge to detect fraudulent activities in credit card transactions, ensuring financial security and reducing losses from fraud.
Skills learned:
Data Preprocessing
Feature Engineering
Model Evaluation
Anomaly Detection Techniques
Customer Segmentation Strategies
Learn how to segment customers based on their behavior and demographics to optimize marketing campaigns for targeted engagement and retention.
Skills learned:
Data Visualization
Clustering Algorithms
Customer Profiling techniques
Operational Efficiency Optimization
Develop tools and techniques to forecast call volumes in customer service centers, improving resource allocation and enhancing service efficiency.
Skills learned:
Time Series Analysis
Model Evaluation
Operational Optimization Techniques
Sales Prediction and Optimization
Learn how to predict sales performance and optimize strategies for products and services to drive revenue growth and customer acquisition.
Skills learned:
Predictive Modeling
Sales forecasting
Model Interpretation Techniques
Real-Time Fraud Detection
Equip yourself with the skills to detect fraudulent activities in real-time transactions, safeguarding organizations from financial losses.
Skills learned:
Data Preprocessing
Pattern Recognition
Anomaly Detection Techniques
Predictive Modeling for Risk Assessment
Predict default probabilities to assess risk and make informed decisions, helping organizations manage portfolios more effectively.
Skills learned:
Feature Engineering
Risk Assessment
Model Evaluation Techniques
Predictive Analytics for Customer Lifetime Value Estimation
Learn how to estimate the lifetime value of customers, enabling personalized marketing strategies and enhancing customer experiences.
Skills learned:
Customer Segmentation
Predictive Modeling
Customer Lifetime value calculation techniques
Personalized Cross-Selling Recommendations
Gain the skills to build recommendation systems for cross-selling products and services based on customer behavior and preferences.
Skills learned:
Collaborative Filtering
Recommendation Algorithms
Customer Profiling techniques
Machine Learning for Risk Assessment
Learn the tools to assess risks in decision-making processes, enabling better strategies and management for large-scale operations.
Skills learned:
Feature Selection
Risk Modeling
Credit Scoring Techniques
Automated CI/CD Pipeline for ML Models
Develop an automated CI/CD pipeline to streamline the deployment of machine learning models, ensuring quick and reliable integration into production environments.
Skills learned:
Pipeline Design
Automated Testing
Continuous Deployment
Real-Time Model Monitoring and Drift Detection
Set up real-time monitoring systems to track ML model performance and detect model drift, ensuring accuracy and reliability in changing environments.
Skills learned:
Performance Monitoring
Drift Detection
Metric Logging
Scalable Model Deployment with Docker and Kubernetes
Learn to containerize ML models using Docker and deploy them at scale with Kubernetes for reliable, efficient, and reproducible production environments.
Skills learned:
Containerization
Orchestration
Scalable Deployment
Data Versioning and Governance Framework
Design a robust data versioning and governance framework to ensure reproducibility, data integrity, and compliance with industry regulations.
Skills learned:
Data Version Control
Compliance
Governance Frameworks
Hyperparameter Tuning for Model Optimization
Implement hyperparameter tuning techniques to improve the performance of ML models, ensuring they meet production requirements effectively.
Skills learned:
Hyperparameter Tuning
Experiment Tracking
Model Optimization
End-to-End Workflow with TensorFlow Extended (TFX)
Build an end-to-end ML pipeline using TensorFlow Extended (TFX), including data ingestion, preprocessing, model training, and deployment.
Skills learned:
Pipeline Construction
Data Preprocessing
Model Deployment
Implementing Monitoring for Ethical AI Compliance
Design monitoring systems to ensure ML models align with ethical AI practices, addressing issues such as bias detection and fairness.
Skills learned:
Bias Detection
Fairness Metrics
Ethical AI Monitoring
Multi-Model Deployment and Load Balancing
Deploy multiple machine learning models in production and implement load balancing strategies to ensure efficient resource utilization and response times.
Skills learned:
Multi-Model Serving
Load Balancing
Resource Optimization
Image Enhancement and Filtering
Learn to enhance image quality using basic image processing techniques like blurring, sharpening, and edge detection. This foundational skill is crucial for preparing images for further analysis.
Skills learned:
Image filtering
edge detection
blurring
sharpening
Basic Object Detection in Images
Understand how to detect objects within images using bounding boxes and simple detection algorithms. Lay the groundwork for developing systems that can identify objects in various scenarios.
Skills learned:
Object detection
bounding box creation
using detection algorithms
Image Classification of Everyday Objects
Train a simple CNN to classify images into categories like animals, vehicles, or everyday items. This project introduces you to deep learning concepts and how they’re applied in image classification.
Skills learned:
Image classification
CNN basics
training deep learning models
Face Detection in Photos
Learn to detect faces in photos using algorithms like Haar cascades. This hands-on project provides a foundation in object detection and recognition systems.
Skills learned:
Face detection
Feature recognition
Algorithm implementation
Panorama Stitching
Combine multiple images to create seamless panoramic views using keypoint detection, feature matching, and image alignment techniques.
Skills learned:
Keypoint detection
Feature matching
Image stitching
Tracking Moving Objects in Videos
Track objects like moving balls or people in a video sequence using basic tracking algorithms. This project introduces the essentials of object tracking and algorithm implementation.
Skills learned:
Object tracking
Video analysis
Tracking algorithm implementation
Color-Based Image Segmentation
Segment images into meaningful regions by identifying specific colors and grouping similar pixels. Learn how segmentation techniques are used in organizing image content.
Skills learned:
Image segmentation
Color detection
Pixel classification
Edge Detection for Shape Identification
Detect edges in images to identify shapes and structures. This project focuses on preprocessing techniques that lay the foundation for more advanced computer vision tasks.
Skills learned:
edge detection
shape identification
Preprocessing techniques
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$349.6 Billion
Amount industry is set to grow by 2030
HIGH ENTRY- LEVEL SALARY
₹8-14 LPA+
Current average CTC for entry-level Data Scientists in India.
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Is This Course Right For You?
Are you looking for a career change?
Do you want to switch from your current job to a more rewarding and in-demand career?
Do you want a promotion?
Are you a working professional looking to upgrade your career with the most sought after skill in today’s job market?
Are you a beginner to data science?
Are you a complete beginner to data science with no coding background who’s looking for a comprehensive program that teaches you everything you need to know from scratch?
If you answered ‘Yes’ to any of the above, SkillCamper’s Full Stack Career Path is the perfect fit for you!
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Curriculum & Course Materials
Live coding environment
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100+ hours of instruction
20+ assignments
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Banking & finance domain focused curriculum
Capstone projects
Live Classes
Flexible study options
Cancel anytime in first 7 days, full refund
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15+ hours of sessions with industry veterans & experts
Personalized mentorship by course instructors
Unlimited 1:1 doubt solving sessions
Career Support
Personalized placement assistance
1:1 mock interviews with industry experts
Soft-skills training module
Essential digital tools for digital workplace module
Interview preparation module
Masterclass on resume building & LinkedIn
Access to curated companies & jobs
Frequently Asked Questions
What technologies will I learn in the Full Stack Generative AI Bootcamp?
You’ll master a range of cutting-edge AI technologies, including generative models like GPT, Gemini, LLAMA, and Claude. You’ll also learn how to work with Retrieval-Augmented Generation (RAG), vector databases, and LLM frameworks, along with tools for data preparation, model fine-tuning, and deployment.
Can I join the Generative AI bootcamp without any prior experience?
Yes! This course is designed for beginners, so you don’t need any prior experience in AI or programming. We start from the basics and guide you through to more advanced concepts, helping you build your skills step by step.
How long is the Full Stack Generative AI Bootcamp, and is it flexible?
The bootcamp lasts for 4 months, with flexible learning options. You can learn at your own pace or attend live classes with industry experts. It’s structured for working professionals, requiring a time commitment of 15–20 hours per week.
What will I learn in this bootcamp?
You’ll learn the fundamentals of generative AI, including how to build, fine-tune, and deploy AI models. Key topics include prompt engineering, working with large language models (LLMs), RAG architecture, and ethical AI practices. You’ll also apply your skills through real-world projects in industries like banking and finance.
Will I have access to the course materials after the bootcamp ends?
Yes, you will have lifetime access to all course materials, recorded sessions, and project files, so you can revisit the content and continue practising after completing the bootcamp.
What kind of career support will I receive during and after the bootcamp?
We provide full placement support, including 1:1 mentorship, mock interviews with industry leaders, resume-building workshops, and access to our network of partner companies. We also offer soft skills training and interview preparation to ensure you’re ready for the job market.
What are the key features of this bootcamp?
This bootcamp offers a comprehensive learning experience with hands-on projects, industry-tested problem-solving frameworks, and AI-powered tools to enhance your learning. You’ll also benefit from 1:1 mentorship, flexible learning options, and a 7-day money-back guarantee.
What is the cost of the bootcamp, and are there payment options?
The bootcamp costs ₹75,000, with easy EMI options available. You can also try the program risk-free with a 7-day no-questions-asked money-back guarantee.
What types of projects will I work on during the bootcamp?
You’ll work on industry-relevant projects, such as fraud detection in banking, sales optimization, and customer segmentation. These real-world case studies will give you practical experience in applying generative AI to real-world problems.
How will this bootcamp prepare me for a career in AI?
By the end of the bootcamp, you’ll have a solid portfolio of AI projects, in-depth knowledge of generative AI models, and hands-on experience with industry-specific challenges. With our placement support, you’ll be well-prepared to land a job in the rapidly growing AI industry.
I don’t have a tech background; can I still take this course?
Absolutely. This course is designed for complete beginners. We start from the basics, covering everything from Python to SQL, making it accessible to those without a tech background.
What type of job support do you provide after completing the course?
We offer comprehensive placement assistance, including resume building, mock interviews, and leveraging our network of industry partners.
How do you prepare students for the job market?
We equip students with industry-relevant skills, help craft winning resumes, and provide mock interview practice with experienced mentors.
Do you guarantee a job after completing the course?
While we do not guarantee a job, we provide extensive support to help you become highly competitive in the job market.
Can you help me find a job in any specific industry?
Our job assistance is generalized; we prepare you for a variety of roles in the data science field rather than focusing on specific industries.
What is the cost of the Data Science bootcamp?
The cost varies depending on the program. Our full bootcamp is priced between ₹50,000 and ₹75,000, depending on whether you choose self-paced or live instruction.
Are scholarships available for the courses?
Yes, we offer scholarships that can cover up to 70% of the tuition fees, making our courses more accessible to a wider range of students.
What is included in the course fee?
The fee includes access to all course materials, live coding sessions, AI-based learning platform, case studies, capstone projects, and mentorship from industry experts.
What payment options are available for the course fees?
We offer flexible payment options, including easy EMIs, and you can cancel anytime in the first 7 days for a full refund.
Is financial aid or other support available aside from scholarships?
While our primary financial support is through scholarships, our enrollment advisors can also assist you with payment plans and financing options to help manage the cost of your education.
Ready to become a Data Scientist that industry loves to hire? Apply Now.
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