Offline Career Track
Intermediate
14 Weeks
AI / ML
Learn machine learning the practical way with Python, data preprocessing, model building, evaluation, and a modern introduction to AI tooling and LLM awareness.
Batch starting soon
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Call for more details: +91 99893 09198
WHO THIS IS FOR
Python learnersData analytics graduatesAspiring ML engineers
Python for ML
Data Preparation
Machine Learning
Model Evaluation
Visualization
Mini Projects
Course Outline
Curriculum Timeline
Each stage connects teaching, lab work, assignments, and doubt resolution so students can keep moving with direct classroom support.
Module 1
Python & Math Foundations
- Python refresher for data workflows
- NumPy arrays, statistics mindset, and linear algebra basics
- Data exploration habits and notebook-based thinking
- Set the groundwork for machine learning practice
Module 2
Data Preparation & Feature Engineering
- Pandas cleaning, missing values, encoding, and scaling
- Feature engineering basics and train/test mindset
- Use preprocessing workflows and pipelines
- Prepare usable datasets for ML experiments
Module 3
Supervised Learning
- Regression and classification workflows
- Linear models, decision trees, random forests, and ensemble basics
- Model training, tuning, and evaluation metrics
- Build interpretable ML experiments with scikit-learn
Module 4
Unsupervised Learning & Model Evaluation
- Clustering, dimensionality reduction, and unsupervised basics
- Cross-validation, overfitting, and model comparison
- Common ML pitfalls and best practices
- Present results clearly using visuals and metrics
Module 5
Applied AI Projects
- End-to-end project workflow from data to model to report
- Deployment awareness and model persistence basics
- Domain case studies like churn, forecasting, or recommendation
- Team review and iteration process
Module 6
Modern AI Awareness
- Intro to LLMs, prompt engineering, and AI-assisted workflows
- Model ethics, bias, validation, and responsible usage
- Where ML ends and GenAI tools begin
- Plan your next step toward advanced ML or AI engineering
Who this course is for
Building the Intelligence of Tomorrow
Python Developers
Engineering Students
Data Enthusiasts
Future AI Engineers
After this course, you can
Models. Logic. Deployment.
Build predictive ML models
Understand neural networks
Master Scikit-learn & TensorFlow
Deploy AI solutions locally
Skills you'll master
Tools & Tech You'll Work With
Python
NumPy
Pandas
Matplotlib
scikit-learn
Notebook workflow
Math for ML Lab
Real Dataset Training
Deep Learning Intro
Project Deployment
High-end Logic Prep
Math for ML Lab
Real Dataset Training
Deep Learning Intro
Project Deployment
High-end Logic Prep
Batch seats are limited