Advanced AI/ML with Python (90 Days)
A deep, 90-day advanced AI/ML program — beyond classical machine learning into deep learning, computer vision, NLP with transformers, model deployment and MLOps basics. For learners who already know ML fundamentals and want production-grade depth.
Amit A.
AI/ML Engineer & Manager
Overview
You will go beyond scikit-learn into deep learning with CNNs and transformers, build and deploy real models via an API, and learn the MLOps basics needed to keep AI systems running in production.
Skills You'll Learn
Prerequisites
- Working knowledge of Python, NumPy and Pandas
- Basic machine learning concepts (train/test split, regression, classification)
- A computer capable of running Python ML libraries (GPU helpful but not required)
Course Roadmap
Module 1
Modules 1–4: ML Foundations Refresher → Feature Engineering at Scale
A fast, practical refresher before going advanced. Push classical ML further. The techniques that win real-world competitions. Where the biggest real-world gains come from.
Module 2
Modules 5–8: Introduction to Deep Learning → Recurrent Networks & Transformers for Sequences
The foundation of modern AI. Train real deep learning models. Apply deep learning to computer vision. Understand modern sequence modeling.
Module 3
Modules 9–12: NLP with Transformer Models → Working with Cloud ML Services
Apply state-of-the-art NLP practically. Take a model from notebook to API. Keep models reliable after launch. Use the cloud the way real ML teams do.
Module 4
Module 13: Capstone: Advanced End-to-End AI/ML System
A complete, portfolio-defining advanced AI/ML project.
Course Curriculum
13 modules · 13 quizzes
- NumPy/Pandas/scikit-learn recap
- The ML workflow
- Evaluation metrics refresher
- Regularization techniques
- Advanced classification algorithms
- Handling imbalanced data
- Random Forest deep dive
- XGBoost & LightGBM
- Stacking & blending
- Advanced encoding techniques
- Automated feature engineering
- Feature selection at scale
- Neural network fundamentals
- Backpropagation intuition
- Activation & loss functions
- Building & training a neural network
- Regularization (dropout, batch norm)
- Hyperparameter tuning
- CNN architecture basics
- Transfer learning
- Image classification project
- RNN/LSTM basics
- Attention mechanism intuition
- Why transformers replaced RNNs
- Using pretrained transformer models
- Fine-tuning basics
- Text classification & embeddings
- Serving models with FastAPI/Flask
- Batch vs real-time inference
- API performance considerations
- Model versioning
- Monitoring a deployed model
- Retraining triggers (conceptual)
- AWS/GCP ML services overview
- Managed training & deployment basics
- Cost & scaling considerations
- Project planning
- Build, train, deploy
- Final presentation
Projects
Ensemble Model Showdown
Compare Random Forest, XGBoost, and LightGBM on a real dataset with full tuning.
Image Classifier with Transfer Learning
A CNN-based image classifier using a pretrained backbone.
Text Classification with a Transformer Model
Fine-tune a pretrained transformer for a real classification task.
Deployed Model API
A trained model served via a FastAPI endpoint with monitoring basics.
Capstone: End-to-End Advanced AI System
Data → deep learning model → deployed, monitored API.
Assignments
Tune an Ensemble Model
MediumTune XGBoost hyperparameters and beat a baseline model.
Build Your First Neural Network
MediumTrain a simple feedforward neural network from scratch.
Transfer Learning Exercise
HardFine-tune a pretrained CNN on a new image dataset.
Fine-Tune a Transformer
HardFine-tune a pretrained transformer for a text task.
Serve a Model via API
HardWrap a trained model in a FastAPI endpoint.
Add Basic Model Monitoring
HardLog predictions and track basic drift/performance metrics.
Deploy to the Cloud
HardDeploy a model-serving API to a cloud provider.
Capstone Presentation
HardPresent your full advanced AI/ML project end-to-end.
Meet Your Instructor
Amit A.
AI/ML Engineer & Manager
12+ years of Experience in AI/ML, LLM, GenAI, MLOps and production AI System etc.
Certificate You'll Earn
CERTIFICATE ID: LNTRN-UPSK-000000
Your Name Here
Successfully completed the Advanced AI/ML with Python Mastery Certificate at Lunetron Web Services. We appreciate their dedication and wish them success in their career.”
ISSUING DATE - 27TH MARCH, 2026
Certified proficiency in advanced machine learning, deep learning, and production AI/ML deployment.
Student Reviews
The Machine Learning and Deep Learning courses are incredibly well structured. The instructors explain complex concepts simply, and the capstone project became the centerpiece of my portfolio.
Aarush Rai
B.Tech / AI-ML
I liked the hands-on approach of the training. Working on assignments and mini projects made the concepts easier to understand, and the AI/ML introduction was a useful addition to the course.
Divyanshu Mishra
B.Tech / AI-ML
The Machine Learning and Deep Learning courses are incredibly well structured. The instructors explain complex concepts simply, and the capstone project became the centerpiece of my portfolio.
V K Srivarshini
B.Tech / CSE
₹9,999
₹19,999
50% off · Save ₹10,000
Career Outcomes
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