GenAI with Python
Python full-stack developer with GenAI
- First batch
Course Information
Python full-stack developer course 08-Jan-2025 న మొదలవుతోంది.
Time: 7.00 PM to 8.30 PM IST
WhatsApp Community link: Click here
Course content
| What is a Computer Language? |
| Types of Programming Languages |
| What is Object Oriented Programming? |
| Programming Applications Examples |
| Programmer vs. Developer |
| Introduction to Python - History of Python |
| Importance of Python |
| What Can I Build Using Python? |
| Lab Design |
| Introduction to Software Tools |
| Installation of Python on Windows |
| Installation of PyCharm on Windows |
| Installation of Python on Linux |
| Installation of PyCharm on Linux |
| Online Python Practice Tool |
| Python Programming using Notepad |
| Introduction to IDE |
| Python's Syntax and Structure |
| Basic Input and Output Operations |
| Code Comments and Documentation |
| Code Errors and Debugging Basics |
| Python Style Guide (PEP 8) Overview |
| Navigating Python’s Interactive Shell |
Introduction to Variables |
| Introduction to Constants |
| Naming conventions |
| Memory Management for Variables |
| Displaying and Formatting Variable Output |
| Basic Mathematical Operations |
| Global vs. Local Variables |
Understanding Built-in Data Types in Python |
| Getting the Data Type of a Variable |
| Type Conversion and Casting Between Data Types |
| Exercise: Weekly Meal Planner |
| Introduction to Strings |
| String Slicing Techniques |
| Exercise: Personalized Email Generator |
| Using Escape Characters in Strings |
| Exercise: Text Censoring Tool |
| Searching and Replacing Substrings in Strings |
| String Manipulation Using Regular Expressions |
Introduction to Operators |
| Assignment Operators |
| Conditional Statement (if-else) |
| Comparison Operators |
| Exercise: Password Strength Checker |
| Logical Operators |
| Exercise: Shopping List Finder |
| Identity Operators |
| Membership Operators |
| Ternary Operators |
Introduction to Conditional Statements |
| if-elif-else Ladder Statements |
| Exercise: Restaurant Menu Selector |
| Nested Conditional Statements |
| Exercise: Movie Ticket Price Checker |
Introduction to Loops (for & while) |
| Exercise: Daily Task Reminder |
| Loops Control Statements |
| Exercise: Guest Check-In System |
| Using the zip() Function for Iteration |
| Introduction to Nested Loops |
Introduction to Functions |
| Exercise: Welcome Message Function |
| Function Parameters and Arguments |
| Return Statement |
| Anonymous or Lambda Functions |
Introduction to Lists |
| Introduction to List Slicing |
| Lists with Loops |
| Exercise: Recent Purchase History |
| Sorting and Reversing Lists |
| Nested Lists |
| Copying Lists: Shallow vs. Deep Copy |
Introduction to Tuples |
| Tuple Methods: Count and Index |
| Nested Tuples |
| Exercise: RGB Color Picker |
| Concatenating and Repeating Tuples |
Introduction to Sets |
| Exercise: Social Media Hashtag Organizer |
| Frozen Sets |
| Subset and Superset Operations in Sets |
| Exercise: Course Prerequisites Checker |
Introduction to Arrays |
| Exercise: Heart Rate Monitoring |
| Slicing Arrays |
| Types of Arrays |
Introduction to Dictionaries |
| Exercise: Personalized Playlist Manager |
| Nested Dictionaries |
| Merging Dictionaries |
| Introduction to Object-Oriented Programming |
| Constructor and Destructor |
| Access Modifiers & OOP Principles |
| Introduction to Encapsulation |
| Getters and Setters |
| Introduction to Inheritance |
| Types of Inheritance |
| Introduction to Polymorphism |
| Types of Polymorphism |
| Exercise: Online Shopping Cart |
| Introduction to Abstraction |
| Introduction to Errors and Exceptions |
| Types of Errors and Exceptions |
| Introduction to Try and Except |
| Introduction to File Handling |
| Basic File Operations |
| Handling File Errors and Exceptions |
| Reading and Writing Comma-Separated Values (CSV) |
| Navigating the Filesystem |
| Introduction to NumPy |
| Working with Array Elements |
| Data Analysis with NumPy |
| Reshaping and Combining Arrays |
| Series |
| DataFrame |
| Missing Values |
| Replace Values |
| Search |
| Index |
| Builtin & Customized Functions |
| Value Counts |
| Group By |
| Concat & Append |
| Merge |
| Stack & Unstack |
| Pivot |
| Melt |
| Categorical to Dummy Variables |
| Cross Tab |
| String Methods |
| Regular Expression |
| contains() method |
| startswith() method |
| Multiple Strings Methods |
| Manipulate Column Names |
| Filter Columns based on Keyword |
| Filter Columns based on Datatypes |
| CSV |
| Tabbed File |
| Fixed Width File |
| JSON File |
| HTML |
| XML |
| API |
| Save Dataframe to CSV File |
| Encoded File |
| Bad Data |
| Introduction to Large Language Models & its architecture |
| In depth intuition of transformer – Attention all your need paper |
| How ChatGPT is trained. |
Fundamentals |
| What is GenAI? |
| What is OpenAI? |
| Other LLMs? |
| What is LangChain? |
| The Fundamentals? |
Software setup |
| Setup OpenAI Account |
| Setup API Key |
| Setup Open Source LLMs |
LangChain in action |
| Setup Project |
| LangChain in action |
| Use Open Source Models Locally |
| Use Mistral AI |
| What is Streamlit? |
| Use Streamlit GUI |
| API Update |
| Turn on Debug |
Prompt Templates |
| Introduction |
| API Update |
| PromptTemplate in action |
| Add two more placeholders |
| Improve the prompt |
| Create a Travel Guide App |
| Prompt Template |
| Interview Helper |
| Run Llama Model Locally |
| Travel Guide Using Llama |
Chains |
| Introduction |
| API Update |
| PromptTemplate in action |
| Add two more placeholders |
| Improve the prompt |
| Create a Travel Guide App |
| Run Llama Model Locally |
| Travel Guide Using Llama |
RAG |
| What is RAG? |
| Use Case and Code Walkthrough |
| Implement RAG Part 1 |
| API Update |
| Implement RAG Part 2 |
| Test |
| History Aware RAG Bot |
Maintaining Chain History |
| Introduction |
| Use ChatPromptTemplate |
| Code Walk Through |
| Use StreamlitChatMessageHistory |
| Display History |
| Use ChatMessageHistory |
| Chat History |
Image Processing |
| Introduction |
| Create Image Analyzer App |
| Use Streamlit |
Embeddings |
| Introduction |
| Using the Embeddings Model |
| Similarity Finder |
| Use Llama |
| Get Multiple Embeddings |
| Embeddings |
| KYC Use Case |
| KYC Part 1 |
| KYC Part 2 |
| Test |
| Few More Use Cases |
Vector Stores |
| Introduction |
| Code Walk Through |
| Implement Job Search Helper |
| Test |
| Use Retriever |
| Use Llama Model |
| Use FAISS Vector Store |
| Create a Diet Helper App |
Introduction |
| Welcome to the Ollama course |
| Installing and Setting up Ollama |
| This is a milestone |
| Model customizations and other options |
| All Ollama Command Prompt / Terminal commands |
| Quiz |
Open WebUI – ChatGPT like interface for Ollama models |
| Introduction to Open WebUI |
| Setting up Docker and Open WebUI |
| Open WebUI features and functionalities |
| Getting response based on documents and websites |
| Open WebUI user access control |
| Quiz |
Types of Ollama Models and their capabilities |
| Types of Ollama models |
| Text models available in Ollama |
| Vision models available in Ollama |
| Code generating models available in Ollama |
| Create custom model from gguf file |
Using Ollama with Python |
| Installing and Setting up Python environment |
| Using Ollama in Python using Ollama library |
| Calling Ollama Model using API and OpenAI compatibility |
Using Ollama with LangChain in Python |
| What is LangChain and why are we using it? |
| Basic modules of LangChain |
Creating RAG application using Ollama and LangChain |
| Understanding the concept of RAG (Retrieval Augmented Generation) |
| Loading, Chunking and Embedding document using LangChain and Ollama |
| Answering user question with retrieved information |
| Demonstrating Conceptual Understanding of RAG Applications Using LangChain and Ollama |
Using Tools and Agents with Ollama models |
| Understanding Tools and Agents |
| Tools and Agents using LangChain and Llama3.1 |
| Quiz |
| New AI Features in Ollama (The Latest Updates You Must Know) |
| The final milestone! |
RAG - Introduction |
| Introduction |
| Software Setup |
| Download Slides |
| Download Projects |
| Why PostgreSQL for Vector Search? |
Real-World RAG Implementation |
| Usecase and Project Walkthrough |
| Install Requirements Locally |
| Connect to Vector DB |
| Use PGAdmin |
| Load Documents |
| Chunk |
| Ingest |
| Implement Ingest API |
| Test Ingest |
| E-Commerce Semantic Search Assignment Walkthrough |
| Implement RAG Pipeline – Build Chain |
| Implement RAG Pipeline – Answer |
| Implement RAG API |
| Test RAG |
| LangSmith Introduction |
| LangSmith Dashboard |
| Connect to Vector Store |
| Vectorization |
| RAG |
Indexes |
| Introduction |
| Code Walkthrough |
| Create Index |
| Indexing |
| Indexes |
Caching |
| Introduction |
| Semantic Cache in Action |
| Test Cache |
| Caching |
Evals |
| Introduction |
| Code Walkthrough |
| Return Chunks |
| Implement Evals |
| Trace with LangSmith |
| Evaluations (Evals & RAGAS) |
| Evals |
Re-Ranking |
| Introduction |
| Re-Ranking Implementation |
| Re-Ranking |
Using Metadata |
| Introduction |
| Update Schema |
| Ingest Metadata |
| Use Metadata Filtering |
| Add API Support |
| Metadata |
Agentic RAG |
| Introduction |
| Update Data |
| API Update |
| Project Setup |
| Install Requirements |
| Implement Tools |
| Install Postman |
| Test Using Postman |
| MCP Client Code Walkthrough |
| Create Agent |
Prompt Engineering for GenAI |
What is a Prompt? |
What is Prompt Engineering? |
Hands-on Lab: Crafting effective prompt |
Best practices in Prompt engineering |
Reading material: prompt engineering tools |
Prompt Engineering Methods |
Interview pattern prompt technique |
Hands-on Lab: Interview approach |
Chain-of-Thought prompt technique |
Hands-on Lab: COT approach |
Tree-of-Thought prompt technique |
Hands-on Lab: TOT approach |
Reading material: Prompt engineering |
Demo of the Project |
Preparing the Data For SQlite3 Database |
Preparing The Data For My SQL Database |
Creating the Streamlit Web app and Configuring the Databases |
Integrating Web App With Langchain SQL Toolkit And Agenttype |
| What Is VCS? |
| Vcs History |
| Sccm |
| Revision Control System |
| Subversion |
| Concurrent Versions System |
| Why GIT |
| GIT Stages (Working directory, Staging area, Repository -Local, Central, Remote ) |
| GIT Installation |
| GIT Lifecycle |
| GIT Logs |
| GIT Push, Pull, clone |
| GIT Cloning |
| GIT Branch |
| GIT Merge |
| GIT Stash |
| GIT Cherry Pick |
| GIT Revert |
| Merge Conflicts |
| Configuration Of User |
| Ignoring Content |
| Branching Strategies |
| GIT Branch (Create, Delete, Rename, Switch) |
| Git Repos (Private & Public) |
| Integrating Repos |
| Forking |
| Github Wikis |
| Linking Projects |
| Github Fileadd |
| Tokens |
| Compare & Pull Request |
| Renaming Repos |
| Danger Zone Options |
| Making Public Repo As Private |
| Deleting Repos |
| Accessing The Private Repos |
| Advantages & Disadvantages |
| Difference Between Git And Other Tools |
Introduction |
| Private Course Feedback Link |
| How to make the best of this course |
The Fundamentals |
| What is GitHub Copilot |
| Limitations |
| Setup Copilot in Visual Studio Code |
Work with Python |
| Generate Code |
| Explain and Debug |
| Review and Refactor |
| Create a Task Manager |
| Add / Remove Task |
| Generate Unit Tests |
| Assignment: Debug and Fix |
Create a Django REST API |
| Use Case |
| Copilot for Planning and Design |
| Create the Django Project |
| Setup Database |
| Create Models, Serializers, ViewSets |
| Configure URLs |
| Run Migrations |
| Test |
| Create Unit Tests |
| Run Tests |
| Add a Test Method |
| Assignment: Generate Tests |
Create a React Frontend |
| Create a React Project |
| Configure Routing |
| Implement Home Page |
| Debug and Enable CORS |
| Implement Add Patient |
| Use Toastify |
| Implement Add Clinical Data |
| Add Clinical Data — Part 2 |
| Add Back Links |
| Apply Styling |
| Assignment: Implement Delete Feature |
Dockerize with Copilot |
| Introduction to Dockerization |
| Create requirements.txt |
| Create Dockerfile |
| Create Docker Compose |
| Docker Compose Up & Run Application |
What we provide
100% Placement Assistance
We provide you 100% placement assistance to the freshers and the experienced persons also. You will get this assistance for 1 year starting from your enrollment date. Note: If you are eligible to that job, you should apply, attend and win. We don't provide you job and package gurantee.
Online community
We provide online community to all the students. Here all current batch students and trainer will be available to clarify the doubts.
Study material
We provide 100% written and video material with life time access to all the students. The student can refer them anytime online. But, can't download.
Certificate
After completion of the course, student will receive a certificate which confirms the course completion. Note: Its just for formality. The certificate which is provided by the respective service provider is only the valid one.
Assignments
After the completion of every tool, trainer gives an assignment to all the students with related topics. Every student must complete that and show to the trainer in the next class. We don't take any action oppotiste to the student incase not complete the assignment. But, its a good practice and we encourage our students to follow that.
Mock interview
After completion of the course, student must request the trainer for the mock interview. We will conduct the interview to the students candidates only.
Resume building
We provide 100% support to our students to prepare the resume with good standards. We provide few templates to build a resume with industry standards.
Career guidance
We provide suggestions and tips to the students under this guidance program to reach their dream career.
FAQ's
- Cloud computing in Telugu is one of the best platform to learn the leading software courses in Telugu. We provide you 100% written and video material with life time access. Our trainers are well experienced and passionated on teaching. All the sessions are going with live and interation with the trainers.
- DevOps is one of the leading IT course which has good demand across the world. Any IT company must need a DevOps team to release a good IT product to the market.
- Yes, we provide free demo classes before the start of every batch and you can pay using UPI, Credit card and Account transfer option. Student can attend the demo class for free and then decide to pay.
- Daily after completion of the class, we give time to the students to ask their doubts. After that, student will get a discussion fourm to post their doubts and get clarification.
- Getting job is always depends upon the student capability and market demand. We encourge our students to pratice a minimum 3 hrs in a day which really helps to get the job.
- Yes, we provide certificate to the students who completed with good attendance.
- We never provide job gurantee to the students. Instead, we provide placement assistance for 1 year from the enrolment date. It means, we provide you jobs information. Student should apply, attend and get the job.
- No, there is no any refund program with us.
- No, we don't provide such options to the students. We provide all recorded sessions to our students with life time access.
- Student will get very limited support only after completion of the course. During the course student will get 100% support.
Trainers
Naga
Trainer
Fee
₹ 6,000/-
This course includes:
08-Jan-2026
Start date
Monday-Saturday (7.00 PM to 8.30 PM IST)
Class Time
2-3 months
Course Duration
Telugu
Teaching Language
Online
Mode Of Teaching
12 months
Content Access
