Machine Learning and Data Science Foundation

Program Overview

Program Name	Machine Learning and Data Science Foundation
Domain / Stream	Machine learning
Duration	1 Month (4 Weeks)
Mode	Online (Self-Paced + Weekly Tasks)
Certificate	Yes — MSME & ISO 9001:2015 Certified
Program Fee	₹999 ₹499 50% OFF (One-Time)
Student Rating	★★★★★ 4.8 / 5 by 1000+ students
Offer Letter	Issued on Enrollment
Issued By	Shiva Tech Innovations (UDYAM-WB-14-0205610)

Program Details & Curriculum

Machine Learning and Data Science Internship

Duration: 30 Days | Certificate: MSME Recognized | Support: support@vidyawan.in

Program Overview

This internship takes you through the complete lifecycle of a Data Science project — from messy raw data to a deployed, monitored ML model — using real datasets from Indian companies like Jio, Zerodha, Flipkart, and Zomato. Every level is guided by VIDYA, our AI Teacher, who evaluates your answers, gives personalised feedback, and ensures you actually understand before you move forward.

Content Standard — How Every Topic is Taught

This is what separates this internship from a course. Every single topic follows a fixed learning standard before you are allowed to proceed.

Stage	What Happens
Concept First	The theory and reasoning behind the topic is explained before any code
Demonstration	A real Indian dataset example is shown to make it concrete
Guided Practice	You implement it yourself with VIDYA providing hints, not answers
Assessment	MCQ and applied questions tested by VIDYA instantly
Revision and Lock	Key points are reinforced so retention is long-term
Advanced Application	Edge cases, pitfalls, and deeper implementation
Real-World Connect	The concept is mapped to an actual industry use case

VIDYA AI operates across all seven stages — checking answers, identifying weak spots, simplifying difficult ideas, and guiding you without doing the work for you.

Curriculum — 8 Levels

Level 1 — Data as a Raw Material

Module	What You Learn
The Data Reality Check	Auditing real-world messy data, missing values, duplicates, inconsistencies
Data Cleaning	Null treatment, type fixing, outlier handling via IQR and z-score
Exploratory Data Analysis	Descriptive stats, correlations, distributions, hypothesis formation
Data Visualisation	Histograms, heatmaps, box plots, scatter plots — Matplotlib and Seaborn

Level 2 — Feature Engineering

Module	What You Learn
Encoding and Scaling	One-Hot, Label Encoding, Min-Max Normalisation, Standard Scaling
Feature Creation and Selection	Correlation filtering, Mutual Information, Recursive Feature Elimination
Handling Imbalanced Data	SMOTE, undersampling, class weight adjustment
Train/Test Split and Validation	k-Fold, Stratified Split, data leakage prevention

Level 3 — Core ML Algorithms

Dataset: Jio Customer Churn

Module	What You Learn
Linear and Logistic Regression	Gradient descent, cost functions, sigmoid — built from first principles
Decision Trees and Random Forests	Tree construction, overfitting, bagging, feature importance
SVM and KNN	Margin maximisation, geometric intuition, computational trade-offs
Model Evaluation	Confusion Matrix, Precision, Recall, F1, ROC-AUC — not just accuracy

Level 4 — Unsupervised Learning and Dimensionality Reduction

Dataset: Zerodha Stock Data

Module	What You Learn
K-Means Clustering	Elbow Method, Silhouette Score, business interpretation of clusters
Hierarchical Clustering and DBSCAN	Dendrograms, arbitrary-shape clusters, outlier detection
PCA	Variance retention, loadings, scree plots, feature compression
t-SNE and Full Unsupervised Pipeline	Non-linear visualisation, high-dimensional cluster interpretation
Stacking and Blending	Meta-learning, base models, holdout-based blending

Level 5 — Advanced ML and Ensemble Methods

Module	What You Learn
XGBoost	Sequential boosting, regularisation, native missing value handling
LightGBM and CatBoost	Leaf-wise growth, native categorical encoding, speed benchmarking
Hyperparameter Optimisation	Optuna, Bayesian optimisation, search space design

Level 6 — NLP and Deep Learning Foundations

Dataset: Flipkart Product Reviews

Module	What You Learn
NLP Fundamentals	Tokenisation, TF-IDF, Bag of Words, text classification
Transformers and HuggingFace	BERT fine-tuning, context-aware embeddings, sentiment classification
Clustering Interpretation and Pitfalls	Validating cluster quality, avoiding over-interpretation, honest communication

Level 7 — MLOps: From Notebook to Production

Dataset: Jio Churn Pipeline from Level 3 — now deployed

Module	What You Learn
ML Pipelines	scikit-learn Pipeline objects, reproducible preprocessing, leakage prevention
Model Serving	FastAPI, Docker containerisation, REST API deployment
Model Monitoring	Data drift, concept drift, automated retraining triggers

Level 8 — Capstone: Full End-to-End Industry Project

Dataset: Zerodha Portfolio Risk Score

Module	What You Do
Project Scoping and Architecture	Define the problem and design the ML system before writing a single line of code
Full Pipeline Build	Data to cleaned features to trained, saved model
Deployment and Live Testing	Deploy as a live API, run real inference
Portfolio Presentation	Document, present, and prepare for professional showcase

Industry Projects

Complete any 2 of the 5 projects below. Each must be built independently and published on LinkedIn.

#	Project	Skills Applied
1	Flipkart Review Sentiment Analyzer	NLP, TF-IDF, Classification
2	CRED Credit Score Estimator	Regression, Feature Engineering
3	PhonePe Fraud Transaction Detector	Imbalanced Classification, Precision-Recall
4	IRCTC Train Delay Forecast	Regression, Time-Series Features
5	Zomato Delivery Time Predictor	Regression, Real-world Feature Design

Minimum 2 approved submissions are required for certification eligibility.

Certification

Once your project submissions are reviewed and approved by the admin team, your MSME recognized certificate is generated and available for download directly from your dashboard.

Internship Timeline

Phase	Days	What Happens
Learning Phase	Day 1 to Day 7	Complete all 8 levels
Task Phase	Day 8 to Day 29	Build projects, publish on LinkedIn, submit URLs
Certification	Day 30	Verification and certificate issuance

Why Our Certification Holds Weight

100% Original Content

No curated links. Every module is custom-built using real Indian operations and platforms such as Zomato, UPI, IRCTC, Flipkart, PhonePe, BookMyShow, Jio, Netflix, and Zerodha. Learners work on practical scenarios that reflect how modern businesses and digital services operate in the real world.

Zero-Setup Native Coding

Write, run, and test your code directly within the Vidyawan platform. No external software, complex installations, or additional tools are required, allowing you to focus entirely on learning and building.

AI-Powered Gatekeeping

Our advanced AI assessment layer evaluates your responses, provides real-time feedback, and verifies your understanding before allowing progression to the next stage. This ensures genuine learning rather than passive completion.

The Mastery Loop

A structured and mandatory learning progression designed to build true competency:

Read → Practice → Apply → Demonstrate

Each stage must be completed successfully before moving forward, ensuring that knowledge is understood, applied, and proven through performance.

Performance Badge System

Students are awarded performance badges based on their quiz scores, task completion, and overall engagement.

🥇 Gold Badge

90%+ score & 4+ projects completed

🥈 Silver Badge

Consistent performance — 70–89%

🥉 Participant Badge

All learners who complete the program

Certificates You Will Receive

Verify Existing →

Upon successful completion you receive a Government-Recognised Certificate from Vidyawan — a registered MSME enterprise (UDYAM-WB-14-0205610), governed under ISO 9001:2015 quality standards.