You will get trained in Machine Learning | Workshop | Crash Course | Training
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Project details
1. Python Basics & Advanced Concepts
Basics: Variables, loops, conditionals, functions, OOP (Object-Oriented Programming).
Advanced: List comprehensions, lambda functions, decorators, context managers.
Data structures: Lists, tuples, dictionaries, sets.
2. Machine Learning Fundamentals & Advanced Concepts
Supervised Learning: Linear regression, decision trees, k-NN.
Unsupervised Learning: Clustering (e.g., k-means).
Deep Learning: Basics of neural networks, activation functions, backpropagation.
Evaluation: Cross-validation, confusion matrix, accuracy, precision, recall.
3. Tools & Frameworks
Data Analysis Libraries: NumPy, Pandas for data manipulation.
Visualization: Matplotlib for plotting.
Machine Learning Libraries: Scikit-learn for algorithms, preprocessing, and evaluation.
Deep Learning: TensorFlow basics for building neural networks.
Deployment: Introduction to Streamlit for building interactive ML apps.
4. Hands-on Project & Real-World Application
Pick a small project (e.g., a simple linear regression model or an image classifier).
Use Scikit-learn or TensorFlow for the model.
Deploy using Streamlit for a simple interface.
Basics: Variables, loops, conditionals, functions, OOP (Object-Oriented Programming).
Advanced: List comprehensions, lambda functions, decorators, context managers.
Data structures: Lists, tuples, dictionaries, sets.
2. Machine Learning Fundamentals & Advanced Concepts
Supervised Learning: Linear regression, decision trees, k-NN.
Unsupervised Learning: Clustering (e.g., k-means).
Deep Learning: Basics of neural networks, activation functions, backpropagation.
Evaluation: Cross-validation, confusion matrix, accuracy, precision, recall.
3. Tools & Frameworks
Data Analysis Libraries: NumPy, Pandas for data manipulation.
Visualization: Matplotlib for plotting.
Machine Learning Libraries: Scikit-learn for algorithms, preprocessing, and evaluation.
Deep Learning: TensorFlow basics for building neural networks.
Deployment: Introduction to Streamlit for building interactive ML apps.
4. Hands-on Project & Real-World Application
Pick a small project (e.g., a simple linear regression model or an image classifier).
Use Scikit-learn or TensorFlow for the model.
Deploy using Streamlit for a simple interface.
Lesson Purpose
Data ScienceStudent Age
Teen (13-17), Adult (18-65), OtherDevelopment Technology
Python, OtherWhat's included
| Service Tiers |
Starter
$99
|
Standard
$249
|
Advanced
$599
|
|---|---|---|---|
| Delivery Time | 2 days | 7 days | 30 days |
Number of Lessons | 2 | 5 | 20 |
Lesson Length (Minutes) | 540 | 1440 | 3000 |
Exercises & Supporting Materials |
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SB
Srijit B.
Jan 23, 2026
Engineer for LLM Integration and Backend Development
Bhupendra Singh and the Robotronix team were extremely professional and collaborative throughout the project. Communication was consistent and proactive, with daily updates and regular feedback loops to ensure alignment. They were flexible and readily available for impromptu calls whenever clarifications were needed. The team stayed closely engaged while completing each milestone, actively seeking approvals to ensure we were always on the same page. The final MVP delivered met all expectations outlined at the start of the contract and provided a strong, reliable foundation for our product.
PV
Prathish V.
Nov 11, 2025
AI/ML Engineer – Short-Term Contract (Agentic AI, Voice AI, Unstructured Data, Dashboard)
overall experience with them was good.
AA
Aditya A.
Aug 1, 2025
Pet Health-Activity Classification On Tri-Axial Sensor Data
Working with Bhupendra Singh from ROBOTRONIX ENGINEERING TECH PVT LTD on our IOT product was a truly positive experience. The project involved a mix of hardware, firmware, and machine learning work, and Bhupendra approached it with clarity and focus from day one. His solutions were thoughtful, well-tuned to our needs, and grounded in a strong technical understanding. We got full support on web development and mobile application.
What made a difference for us was the support he continued to offer even after the official scope was complete. He was generous with his time—jumping on calls, answering questions, and helping us troubleshoot as we moved things forward.
We’re genuinely thankful for the collaboration and would gladly recommend Bhupendra to anyone exploring advanced work in IOT and ML
What made a difference for us was the support he continued to offer even after the official scope was complete. He was generous with his time—jumping on calls, answering questions, and helping us troubleshoot as we moved things forward.
We’re genuinely thankful for the collaboration and would gladly recommend Bhupendra to anyone exploring advanced work in IOT and ML
PM
Priyanka M.
Jul 28, 2025
AI Engineer for healthcare enterprise development
SR
S Rishabh R.
Jun 25, 2025
LLM Specialist to Develop AI-Powered Manufacturing Query Tool
They are good at what they do and were quick to understand the terminologies and were able to turn around a quick V1 for us.
Hoping to work with them soon !
Hoping to work with them soon !
About Bhupendra Singh
Python Specialist (ML, AI, DL, LLM, Langchain, IoT)
100%
Job Success
Indore, India - 6:27 pm local time
Committed to pushing technological boundaries, I bridge global innovation with local business requirements, delivering transformative data science solutions that seamlessly adapt cutting-edge methodologies to contextual challenges.
Academic & Professional Credentials
- Master of Science in Data Science
- 13+ Years of Cutting-Edge Experience in AI, Machine Learning, and Big Data Solutions
⚙️ My Core Strengths
🤖 Generative AI & Creative Intelligence
Proficient in Stable Diffusion, DALL·E, MidJourney, ControlNet, and ComfyUI
Building custom pipelines for text-to-image generation and creative automation
🧩 Natural Language Processing
Deep expertise in BERT, GPT, ELMo & Transformer-based models
Advanced linguistic modeling, semantic search, and chatbot systems
👁️🗨️ Computer Vision Systems
High-accuracy implementations in:
Object Detection
Image Segmentation
Facial Recognition
OCR & Edge AI solutions
🔧 Technical Toolkit
🖥️ Programming Languages
Primary: Python
Additional: Java, C/C++, MATLAB, PHP
🧠 Machine Learning & AI Frameworks
Deep Learning: PyTorch, TensorFlow, Keras
Classic ML & Processing: Scikit-learn, Pandas, NumPy
NLP Stack: SpaCy, Transformers, NLTK
☁️ Cloud & Big Data Ecosystem
Cloud ML: AWS SageMaker, Azure ML, GCP AI Platform
Big Data: Apache Spark, Hadoop
Real-Time Data Streaming: Apache Kafka, AWS Kinesis
🧠 Automation & Workflows
n8n for low-code/no-code automation
Integrating AI models, APIs, and services into seamless workflows
📊 Data Analytics & Visualization
Tools: Tableau, Power BI, Grafana
Turning raw data into strategic decisions and actionable dashboards
🌟 What Sets Me Apart
Architecting end-to-end AI/ML solutions—from ideation to deployment
Real-time, scalable data pipelines tailored for business growth
Cloud-native, secure, and cost-effective implementations
Blend of technical depth + domain knowledge = Real Results
#datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #data #programming #bigdata #coding #technology #datascientist #deeplearning #computerscience #tech #dataanalysis #datavisualization #analytics #java #pythonprogramming #database #business #software #statistics #cybersecurity #programmer #developer #iot #dataanalyst #innovation
Steps for completing your project
After purchasing the project, send requirements so Bhupendra Singh can start the project.
Delivery time starts when Bhupendra Singh receives requirements from you.
Bhupendra Singh works on your project following the steps below.
Revisions may occur after the delivery date.
Python Basics & Advanced Concepts
Begin with foundational Python concepts Learn essential data structures (lists, dictionaries, sets, tuples) and algorithms. How to leverage Python’s functional capabilities. Understand Python modules and libraries to organize code efficiently.
Machine Learning Fundamentals & Advanced Concepts
Supervised & unsupervised learning & progress to advanced topics like deep learning. Key algorithms such as linear regression, decision trees, k-nearest neighbors & neural networks. Model evaluation, feature engineering, and optimization techniques.