Program Overview
Advanced Executive Certification in Generative AI 
Offered by: Centre for Training and Employment, CTE
Collaboration with: Council for Citizen Rights, CCR (NGO)
Duration: 3 months
Mode: Online and Offline

Regular Classes Start from 28th December (Wednesday)
Enrollments open till 27th December, Tuesday. Choose your preferred Weekday or Weekend Batches with timings:
1) Morning: 9:00 AM / 11:00 AM
2) Evening: 6:00 PM / 8:00 PM

For Assistance:
Call/WhatsApp: 9281434941, 9281434940, 9281434939, 9949073398
WhatsApp Only: 8072211686, 9346000855
Visit our centers for in-person guidance.

Key Features:

•  120 hours of applied learning
•  Live In in-person classroom and Video Classes
• Each Session Live Recording is available in the Learning Management System (LMS) Portal
•  Lifetime Access to Course
•  1:1 mentorship
•  50+ industry projects
•  Certification from the Centre for Training and Employment (MSME, ISO, AICTE)
• Guranteed Job Assistance Till Placement
•  Campus immersion at India's reputed Universities, Institutions, MNCs, Industries
•  Up to ₹50 Lakh startup incubation support

Our Credentials:

•  Active Students: 75 Thousand+  
•  Instructors: 56+ Industry Experts  
•  Hiring Partners: 125+  
•  Corporates & Colleges Upskilled: 75+  
•  States with Learners: 18+  

Why Generative AI?

Transformational Technology: Generative AI is revolutionizing industries by enabling machines to create content such as text, images, videos, music, and even code, pushing the boundaries of creativity and automation.

Hottest Job Roles: With applications in content creation, software development, healthcare, and more, roles like Generative AI Engineer, Prompt Engineer, and AI Content Specialist are rapidly emerging as top career choices.

High Demand: The global market for Generative AI is expected to grow at a staggering 34.6% CAGR, driving demand for professionals skilled in this cutting-edge technology.

Salary Potential: Generative AI experts earn significantly higher salaries, often 50–70% more than traditional AI roles, due to their ability to innovate and solve complex business challenges.

Skill Development: Master state-of-the-art techniques in transformer models, neural networks, natural language processing (NLP), diffusion models, and tools like ChatGPT, DALL·E, and Stable Diffusion to lead the next wave of AI innovation.

Learning Path and Roadmap

Roadmap, Skills & Tools, Learning Path of the Course - available at LinkedIn:

1. Foundation  

•     AI/ML Concepts  
•     Python, Libraries  

2. Key Models  

•     GANs, VAEs  
•     LSTM  
•     Transformers, BERT, GPT  

3. Advanced Techniques  

•     LLMs (GPT, BERT)  
•     RAG  
•     Chatbots  

4. Predictive Analytics  

•     Supervised/Unsupervised Learning  
•     Feature Engineering, Selection  

5. Specialized Models  

•     OLS Regression  
•     Naive Bayes  
•     Markov Chains, GMM

Skills To Master

•  Deep Learning: Neural networks, backpropagation, GANs, VAEs, Transformers, LSTMs  
•  Natural Language Processing (NLP): Text generation, language modeling, chatbot development  
•  Machine Learning: Regression, classification, feature engineering, model evaluation  
•  Model Implementation: Fine-tuning and implementing LLMs, transformers, RAG  
•  Data Preprocessing: Feature extraction, scaling, encoding, and dimensionality reduction 
•  Mathematics: Probability theory, Markov chains, Gaussian mixture models, and linear regression  

Tools & Technologies

•  Programming Languages: Python, TensorFlow, Keras, PyTorch  
•  Data Processing: Pandas, NumPy  
•  NLP Libraries: NLTK, SpaCy, HuggingFace Transformers  
•  Cloud Platforms: AWS, Azure, Google Cloud (for deploying models)  
•  ML & Deep Learning Frameworks: Scikit-learn, TensorFlow, Keras, PyTorch  
•  Data Visualization: Matplotlib, Seaborn, Plotly  
  
  Top Paid Jobs in Generative AI
   
• Generative AI Engineer: ₹9-22 LPA
  • Role: Develop and optimize generative AI models for creating text, images, videos, and more.
• AI Content Creator: ₹8-20 LPA
  • Role: Use generative AI tools to automate and enhance content creation for marketing, entertainment, and education.
• Deep Learning Specialist: ₹10-25 LPA
  • Role: Design and implement deep learning architectures for generative AI applications.
• AI Research Scientist: ₹12-30 LPA
  • Role: Explore new techniques in generative AI, focusing on advancements in GPT models and diffusion models.
• Creative AI Developer: ₹8-18 LPA

Role: Build applications that harness generative AI to deliver innovative and interactive user experiences.
 

Sectors for Generative AI Professionals

1. AI/ML Startups

• Innovate with cutting-edge generative technologies in agile and fast-growing companies.

2. Tech Giants

• Collaborate with industry leaders like Google, Microsoft, and OpenAI to scale AI-driven solutions.

3. Research Labs

• Contribute to advancements in generative AI, exploring models for text, image, and video synthesis.

4. Creative Content Platforms

• Develop tools to automate and enhance creative processes in media, marketing, and entertainment industries.

5. Automation and Process Optimization Companies

• Leverage generative AI to revolutionize workflows, from design to data synthesis.

Faculty and Advisors

•  Renowned professors
•  Industry experts

Career Services

•  1:1 Mock Interviews  
•  Dedicated Placement Support  
•  Career Fairs and Resume Building  
•  Access to 200+ job postings monthly  
  
  Detailed Modules

1. Generative AI

•  Introduction to Generative AI
•    Definition and importance
•    Applications of Generative AI (text, images, music, etc.)
•  Generative Models Overview
•    GANs (Generative Adversarial Networks)
•    VAEs (Variational Autoencoders)
•    Diffusion Models
•  Deep Learning Basics
•    Neural networks and backpropagation
•    Activation functions
•    Loss functions in generative models
•  Training Generative Models
•    Training strategies
•    Evaluation metrics (Inception Score, FID, etc.)

2. LSTM (Long Short-Term Memory)

• Introduction to LSTM
•    RNN vs LSTM
•    The architecture of LSTM (Input, Forget, and Output gates)
•  LSTM for Sequence Modeling
•    Applications in NLP and time series
•    Handling the vanishing gradient problem
•  Advanced LSTM Architectures
•    Bidirectional LSTM
•    Stacked LSTM
•    LSTM vs GRU (Gated Recurrent Units)

3. Transformers

•  Introduction to Transformers
•    Attention mechanism
•    Encoder-decoder architecture
•  Self-Attention and Multi-Head Attention
•    Scaled dot-product attention
•    Importance in NLP and machine translation
•  BERT and GPT Models
•    BERT architecture (Bidirectional Encoder Representations from Transformers)
•    GPT architecture (Generative Pre-trained Transformer)
•    Fine-tuning transformers for specific tasks

4. LLM (Large Language Models)

•  Introduction to LLMs
•    Scaling laws in LLMs
•    Key differences between LLMs and traditional models
•  Training LLMs
•    Pre-training vs fine-tuning
•    Transfer learning in LLMs
•  Applications of LLMs
•    Text generation
•    Sentiment analysis
•    Question answering and summarization
•  Ethical Issues with LLMs
•    Bias, fairness, and transparency

5. Retrieval Augmented Generation (RAG)

•  Introduction to RAG
•    Combining generative and retrieval models
•    Use cases in large-scale text generation
•  RAG Architecture
•    Retrieval step
•    Generative step
•    RAG models in BERT/GPT
•  Training RAG Models
•    End-to-end training of RAG models
•    Retrieval methods (TF-IDF, BM25, etc.)

6. Chatbot using LLMs

•  Introduction to Chatbots
•    Rule-based vs AI-based chatbots
•    Components of a chatbot (intent recognition, dialogue management)
•  Building Chatbots with LLMs
•    Using GPT or BERT for conversational agents
•    Fine-tuning language models for chat applications
•  Handling Context and Memory in Chatbots
•    Maintaining conversation state
•    Multi-turn dialogues and context

7. Predictive Analysis and Machine Learning

•  Overview of Predictive Analytics
•    Supervised vs unsupervised learning
•    Types of predictive models (regression, classification)
•  Machine Learning Algorithms
•    Decision Trees, Random Forest, SVM, KNN, etc.
•    Model selection and evaluation metrics (accuracy, precision, recall, etc.)
•  Model Deployment and Monitoring
•    Model performance in production
•    Continuous improvement of predictive models

8. Feature Engineering & Feature Selection

•  Feature Engineering Basics
•    Importance of feature engineering
•    Types of features (numerical, categorical, time-series)
•  Techniques in Feature Engineering
•    Scaling, encoding, and transformation
•    Handling missing values and outliers
•  Feature Selection Methods
•    Filter methods (correlation, chi-square)
•    Wrapper methods (RFE, genetic algorithms)
•    Embedded methods (Lasso, decision trees)

9. Ordinary Least Squares (OLS)

•  Introduction to OLS Regression
•    Simple linear regression theory
•    Assumptions of OLS regression
•  OLS Estimation
•    Least squares estimation process
•    Estimation of coefficients and error terms
•  Model Evaluation in OLS
•    R-squared and Adjusted R-squared
•    ANOVA and F-tests

10. Naive Bayes

•  Introduction to Naive Bayes
•    Bayes' theorem overview
•    Conditional independence assumption
•  Types of Naive Bayes Classifiers
•    Gaussian Naive Bayes
•    Multinomial Naive Bayes
•    Bernoulli Naive Bayes
•  Applications of Naive Bayes
•    Text classification
•    Spam filtering and sentiment analysis

11. Markov Chain

•  Introduction to Markov Chains
•    Definition and properties of Markov processes
•    Transition matrices and states
•  Types of Markov Chains
•    Discrete vs continuous state Markov chains
•    Absorbing and recurrent chains
•  Applications of Markov Chains
•    PageRank algorithm
•    Weather prediction, board games, and queuing systems

12. Gaussian Mixture Model (GMM)

•  Introduction to GMM
•    Mixture models and probability distributions
•    Gaussian components and their significance
•  Expectation-Maximization (EM) Algorithm
•    Maximizing likelihood using EM
•    GMM vs K-means clustering
•  Applications of GMM
•    Clustering and density estimation
   

  Real-Time Projects:

•  Generative AI Models: Build and train GANs, VAEs, and diffusion models to generate images, text, or music.
•  NLP Projects: Create chatbots using LLMs (like GPT-3), sentiment analysis models, and text summarizers.
•  Time Series Forecasting with LSTMs: Predict stock prices, weather patterns, or sales data.
•  Feature Engineering Pipeline: Implement automated feature extraction, transformation, and selection for real-world datasets.

 AI-Driven Predictive Models: Develop machine learning models for customer segmentation, fraud detection, and recommendation systems.

Top Job Roles in Generative AI

1. Generative AI Engineer

• Responsibilities: Design and develop generative AI models for text, image, and video synthesis.

Skills: Generative Modeling, GANs, Transformer Architectures, Python, OpenAI API.

2. AI Content Specialist

• Responsibilities: Create automated and innovative solutions for text, media, and creative industries.

Skills: Content Automation, GPT Models, LangChain, Media Generation Tools.

3. AI Specialist

• Responsibilities: Implement and optimize AI frameworks for generative applications across industries.
• Skills: AI Framework Implementation, Deep Learning, Model Fine-Tuning, Optimization Techniques.
  
  4. Machine Learning Engineer
• Responsibilities: Build and train generative AI models for advanced use cases, including design and simulations.

Skills: Machine Learning, Python, TensorFlow, PyTorch, Generative Model Tuning.

5. Creative AI Developer

• Responsibilities: Develop creative tools powered by AI for industries like art, gaming, and marketing.

Skills: AI-Powered Development, Software Engineering, Generative APIs, Visualization Tools.

6. NLP Engineer

• Responsibilities: Enhance natural language models for tasks like story generation, translation, and sentiment analysis.

Skills: NLP, SpaCy, NLTK, OpenAI API, GPT Models.

7. AI Research Scientist

• Responsibilities: Explore novel approaches in generative AI, advancing state-of-the-art models.

Skills: Research and Development, Algorithm Design, Generative Model Innovation.

8. Ethical AI Specialist

• Responsibilities: Address ethical challenges in generative AI, including bias, data security, and responsible use.
• Skills: Ethical AI Design, Bias Detection and Mitigation, Policy Frameworks.
  
  9. AI Consultant
• Responsibilities: Guide organizations in leveraging generative AI to innovate and transform processes.

Skills: AI Strategy, Communication, Business Process Optimization.

10. AI Product Manager

• Responsibilities: Manage the lifecycle of AI-powered products, integrating generative AI technologies.

Skills: Product Management, Generative AI Strategy, Collaboration Across Teams.

11. Creative Industry Specialist

• Responsibilities: Use generative AI tools to create art, music, and marketing content.
• Skills: Generative Design, Artistic AI Tools, Creative Automation.
  
  12. AI Model Trainer
• Responsibilities: Train, refine, and deploy generative AI models tailored for specific industries.
• Skills: Model Training, Generative Model Optimization, Industry-Specific AI Applications

 

Projects - top list:

•  Generative AI Models: Build and train GANs, VAEs, and diffusion models to generate images, text, or music.
•  NLP Projects: Create chatbots using LLMs (like GPT-3), sentiment analysis models, and text summarizers.
•  Time Series Forecasting with LSTMs: Predict stock prices, weather patterns, or sales data.
•  Feature Engineering Pipeline: Implement automated feature extraction, transformation, and selection for real-world datasets.
•  AI-Driven Predictive Models: Develop machine learning models for customer segmentation, fraud detection, and recommendation systems.

Placement Opportunities - top list:

•  Tech Companies: Google, Microsoft, Amazon, Facebook, Apple, Tesla  
•  Startups: Specialized AI companies focusing on chatbots, generative models, and NLP  
•  Consultancies: Accenture, Deloitte, PwC, EY (focused on AI transformation in businesses)  
•  AI Research Labs: OpenAI, DeepMind, and academic research institutions  

Value Addition of Generative AI Certification

•  Specialized Knowledge: Master state-of-the-art AI techniques and models.  
•  Real-World Impact: Learn to build applications that generate content (text, images, etc.), enabling businesses to innovate.  
•  Career Growth: Stand out in high-demand fields like AI, NLP, and machine learning, with the potential for fast career advancement.  
• Project Portfolio: Gain hands-on experience with industry-relevant projects to showcase in interviews.

Innovative Features:  

•  Learn by Doing: Hands-on exercises, projects, quizzes, assignments and capstone projects.
•  Gamified Learning: Hackathons and group activities.
•  Peer Networking: Build professional connections through collaborative learning.

Program Pedagogy:  

1. Instructor-led Training: Delivered by top industry experts.  

2. Projects and Exercises: Gain real-world experience.  

3. Hackathons: Understand real-world project-building techniques.  

4. Personalized Learning: Tailored 1:1 mentoring.  
 

About Centre for Training and Employment (CTE):

• CTE is a leading online education provider, and also classroom / offline services partnering with prestigious colleges and industries, consultants.

Mission: Democratizing education with cutting-edge training solutions. 

Flexible Fee Structure and Payment Options

• At CTE Academy, we aim to make quality education accessible and affordable for everyone. 
• Explore our flexible fee categories, scholarships, and EMI options tailored to suit your financial needs.

Fee Categories

• Training Only: ₹9,500
• Training and Placement:
  • One Time Payment (OTP) - ₹14,500
  • Total EMI (with HDFC Account Only or any Credit Card as enlisted)  ₹18000 (EMI starts from ₹734 per month)
• Book for Offline - Classroom (Hyderabad - Ameerpet and Vijayawada) / Work and Learn / Online EMI without HDFC or without credit card.
  • Reserve Seat for ₹3000
  • Fees structure same as Online via LMS
• Pay After Placement:
  • Before Placement: ₹9,500 (EMI available starting from ₹734 per month)
  • After Placement: ₹34,500

Scholarship Program

• Who Can Apply: Unemployed and underprivileged candidates
• Scholarship Amount: 10% to 40% off on the One-Time Payment (OTP) fees

Exclusive EMI Plans - CTE Academy via Unacademy (Graphy) Collaboration with HDFC:

• Make learning stress-free with our easy-to-manage EMI options. 
• We now offer special coupons to reduce the overall course fee for candidates opting for EMI plans.
• All you need is an HDFC Bank account or any Credit Card as enlisted.
• 3 Months Plan: ₹5,134.00 x per Month
       6 Months Plan: ₹2,618.00 x per Month
       9 Months Plan: ₹1,780.00 x per Month
       12 Months Plan: ₹1,361.00 x per Month
       18 Months Plan: ₹943.00 x per Month
       24 Months Plan: ₹734.00 x per Month

​​​​​​​​​​​​​​​​​​​​​"Empowering Creativity with the Future of AI: Innovate, Automate, and Transform with Generative AI."