Pranjal Bhatt Python,Data Science,Machine Learning,Data Analysis
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This isn’t your typical course where you stop at toy datasets like Iris — if you’re serious about mastering GenAI, Machine Learning, LLMs, building real-world AI applications, deploying them locally and on cloud, fine-tuning models, and working with MLOps and AWS services like Lambda and Bedrock, let’s connect.
I will help you with your resume building with actual deployed projects.

GenAI
- LLMs, RAG, LangChain and Agentic AI (multi-agent frameworks)
- Prompt engineering and AI application development
- Vector databases like Chroma and FAISS

Python
- Python basics to advanced for data and AI
- Writing clean and production-ready code
- Libraries: Pandas, NumPy, Scikit-learn

Machine Learning
- End-to-end lifecycle (EDA, feature engineering, model building)
- Model tuning and evaluation
- Advanced algorithms like XGBoost and CatBoost

MLOps
- MLflow for experiment tracking and model management
- CI/CD pipelines using Jenkins
- Model monitoring and production pipelines

AWS
- Deployment using EC2 and S3
- Basics of cloud-based ML deployment
- Scalable architecture understanding

Resume Building
- Resume creation with strong project storytelling
- Portfolio with real-world projects
- Interview preparation for Data Science and AI roles

Industry Projects
- AI based data analyst using multi Agent framework with RAG.
- Document QnA using advanced RAG concepts like Hybrid RAG,Re-Ranking,Agentic RAG
- Comprehensive Industrial level clinical Trails study
- At least 10 Machine learning basic to advance projects from data preparation to model training deployment, model versioning using MLFLOW .
- MLOPS implementation using jenkins,Docker,lambda, bedrock,github actions, Cloudwatch,streamlit for frontend.

Subjects

Deep Learning Beginner-Expert
Artificial Intelligence Beginner-Expert
Natural Language Processing Beginner-Expert
Machine learning Python Beginner-Expert
Python 3 Beginner-Expert
Data Science with Python Beginner-Expert
MLOps Beginner-Expert
Large Language Models Beginner-Expert

Experience

Data Scientist (Dec, 2017–Present) at Impetus Infotech India Pvt Ltd

Working on various projects involving Mathematical modelling for various business.

Education

BE (Jul, 2011–May, 2015) from bachlors of engineering, masters in design Electronics and Telecommunication

Fee details

₹1,000/hour (US$10.53/hour)

Courses offered

Python Basics to Advance
- US$400
- Duration: 1 Month
- Delivery mode: Online
- Group size: Individual
- Instruction language: English, Hindi
- Certificate provided: No
Includes
Python Data types basic and Collection types
Control Statements
Loops
Function,Modules & Packages
Staring Handling
List, Dict, Tuple, Sets in Detail
Object oriented Programming in Python
Inheritance
Exception Handling
Files I/O
Multithreaded Programming in python
3 minor and 2 Major Projects
Python For Data Analytics and Data Science
- US$500
- Duration: 45 Days
- Delivery mode: Online
- Group size: Individual
- Instruction language: English, Hindi
- Certificate provided: No
Python Introduction
Operators
Data Types
Control statement
Loops
Function Modules and Packages
String Handling
Python Regular Expression
List, Dictionary,Tuple and Sets
Numpy
Pandas Basics
Pandas Advanced
Data Visualization using Matplotlib and Seaborn
Basic Statistics
3 pandas and 2 statistics Project
SQL for Data Analytics
- US$300
- Duration: 30 Days
- Delivery mode: Online
- Group size: Individual
- Instruction language: English, Hindi
- Certificate provided: No
Introduction to SQL
✓ Sql Select statement
✓ Write basic SQL queries
✓ Group and aggregate data to produce summary statistics
✓ Join tables and apply filters and sub-queries
✓ Write functions to explore and manipulate data
✓ Communicate your insights to stakeholders
✓ Advanced SQL concepts like ranking function, running total calculations,CTE etc.
3 Projects
Machine Learning for Data Scientist in Python
- US$1000
- Duration: 60 Days
- Delivery mode: Online
- Group size: Individual
- Instruction language: English, Hindi
- Certificate provided: No
This Course covers both
Introduction to Machine Learning
Statics for Machine Learning
Data Preprocessing
- EDA
- Outlier Detection and Treatment
- Feature Imputation
- Feature Reduction and Selection
- Feature Encoding
Algorithm Selection and model training
Model tuning
Model Deployment
Model performance monitoring
5 Projects