ML
Data Science & ML
Python · Pandas · Scikit‑learn · NLP — 16 Weeks
16 Weeks Project-Led Career Support

Data Science & Machine Learning Program

Hands-on Python, Pandas, scikit-learn, NLP projects, capstone & career mentorship.

Duration: 16 weeks
Format: Live + Recorded Labs
Level: Beginner → Intermediate

Program Overview

This 16-week program prepares you end-to-end for data science roles: data wrangling, analysis, ML modeling, NLP, and deployment. Weekly projects and an industry-grade capstone reinforce practical skills.

Core Skills Covered

  • Python programming for data
  • Pandas & NumPy for cleaning & transformation
  • Visualization (Matplotlib, Seaborn)
  • scikit-learn: supervised & unsupervised learning, pipelines
  • NLP basics & intermediate: tokenization, embeddings, text classification
  • Model evaluation, cross-validation, hyperparameter tuning
  • Deployment basics: APIs & simple cloud deployment

Who should join?

Aspiring data scientists, analytics professionals, software engineers transitioning to ML, and graduates seeking hands-on ML experience.

Prerequisites

  • Basic spreadsheet familiarity & logical problem-solving
  • Laptop (Windows/macOS/Linux) + internet
  • ~10–15 hours/week commitment

Outcomes & Support

  • 4+ project artifacts including capstone
  • Resume & interview coaching
  • Completion certificate & technical report
  • Hiring partner access & placement assistance

16-Week Curriculum

Each week combines live lectures, hands-on labs, and project deliverables.

Week 1 — Python Foundations
Syntax, data types, control flow, functions and modules.
Week 2 — Python for Data
Lists, dicts, file I/O, virtual environments, best practices.
Week 3 — NumPy & Pandas I
Series/DataFrame, indexing, selection, basic transformations.
Week 4 — Pandas II & Data Cleaning
Missing data, merging, groupby, apply, reshaping and performance tips.
Week 5 — Exploratory Data Analysis
Visualization, hypothesis testing basics.
Week 6 — Supervised Learning I
Linear regression, metrics, bias-variance tradeoff, scikit-learn API.
Week 7 — Supervised Learning II
Classification, tree-based models, evaluation metrics.
Week 8 — Model Tuning & Pipelines
Cross-validation, grid/random search, pipelines, feature engineering.
Week 9 — Unsupervised Learning
Clustering, PCA, anomaly detection.
Week 10 — Time Series & Advanced Topics
Intro to forecasting, seasonality, and features.
Week 11 — Introduction to NLP
Text preprocessing, bag-of-words, TF-IDF, text classification basics.
Week 12 — Advanced NLP
Embeddings, word2vec, transformers overview, fine-tuning basics.
Week 13 — Model Deployment Basics
Serialization, APIs, deployment patterns, monitoring basics.
Week 14 — Ethics & Responsible AI
Bias, fairness, explainability, data privacy considerations.
Week 15 — Capstone Development
Project execution, ETL, modeling, mentor reviews.
Week 16 — Final Presentation & Hiring Prep
Project demos, portfolio polish, mock interviews, career sessions.

Capstone & Projects

End-to-end projects for portfolio readiness, covering data ingestion, cleaning, modeling, and presentation. Mentored review included.

Sample Capstone Ideas

  • Customer churn prediction with action plan
  • Text classification pipeline (NLP)
  • Time series demand forecasting

Assessment

Weekly assignments, mid-program tests, and final capstone evaluation with technical report and completion certificate.

How to Apply

  1. Fill out the application form with your basic details and background information.
  2. Complete the course payment of ₹9,500/- to confirm your enrollment. Multiple payment options are available.
  3. Receive your official admission letter after payment confirmation.
  4. Start attending classes, available both online and offline according to your preference.

Refund & Cancellation

Full refund if cancelled within 7 days of enrollment and before course start. Contact admissions for details.

Application Form