Great for freshersIdeal for career switchers

Data Science & Machine Learning

Go from spreadsheet analyst to ML engineer in 12 weeks.

Duration: 12 weeks
Duration
Sessions: 18
Sessions
Labs: 9
Labs
Projects: 3
Projects

What You'll Be Able To Do

After completing this course, you will confidently:

Manipulate and analyze large datasets using NumPy arrays and Pandas DataFrames
Build publication-quality data visualizations with Matplotlib and Seaborn
Implement supervised learning models including linear regression, decision trees, and ensemble methods
Apply unsupervised learning techniques including clustering, dimensionality reduction, and anomaly detection
Evaluate and tune ML models using cross-validation, grid search, and performance metrics
Design and train neural networks using TensorFlow and PyTorch for structured and unstructured data
Build NLP pipelines for text classification, sentiment analysis, and named entity recognition
Deploy trained models to production using FastAPI endpoints and Docker containers

What You'll Build

Real portfolio projects that showcase your skills to employers.

Customer Churn Prediction System

Build an end-to-end ML pipeline that predicts customer churn using real telecom data. Includes feature engineering, model comparison (logistic regression vs gradient boosting), hyperparameter tuning, and a FastAPI prediction endpoint.

PythonPandasscikit-learnXGBoostFastAPI

Interview value:

Demonstrates the complete ML workflow from data cleaning to deployment — the most common data science interview scenario.

Movie Recommendation Engine

Design a hybrid recommendation system combining collaborative filtering and content-based methods. Evaluate using precision, recall, and NDCG metrics on the MovieLens dataset.

Pythonscikit-learnTensorFlowPandas

Interview value:

Recommendation systems are a classic ML interview topic that shows depth in both algorithms and evaluation methodology.

NLP Sentiment Analysis Pipeline

Build a text classification pipeline that processes product reviews, extracts features using TF-IDF and word embeddings, and classifies sentiment using both traditional ML and deep learning approaches.

PythonNLTKTensorFlowscikit-learnDocker

Interview value:

NLP skills are in high demand and this project shows your ability to work with unstructured text data at scale.

Course Curriculum

12 weeks of structured, hands-on learning.

1Python for Data Science — NumPy

NumPy arrays — creation, indexing, slicing, broadcasting
Vectorized operations and performance vs Python loops
Linear algebra with NumPy — dot products, matrix operations
Random number generation and statistical functions

Lab: NumPy Array Operations & PerformanceDocker Lab

2Pandas & Data Manipulation

DataFrames — creation, selection, filtering, and groupby
Handling missing data — imputation strategies and trade-offs
Merging, joining, and reshaping datasets
Time series data handling with Pandas

Lab: Pandas Data Wrangling with Real DatasetsDocker Lab

3Data Visualization & Exploratory Analysis

Matplotlib — figures, axes, subplots, and customization
Seaborn — statistical plots, heatmaps, and pair plots
Exploratory data analysis workflow and best practices
Feature engineering from visual insights

Lab: EDA & Visualization on Kaggle DatasetDocker Lab

4Supervised Learning — Regression

Linear regression — assumptions, coefficients, and residual analysis
Polynomial regression and regularization (Ridge, Lasso, ElasticNet)
Feature scaling, encoding categorical variables, and pipelines
Cross-validation and bias-variance trade-off

Lab: Regression Models — Housing Price PredictionDocker Lab

5Supervised Learning — Classification

Logistic regression and decision boundaries
Decision trees, random forests, and gradient boosting (XGBoost)
Evaluation metrics — accuracy, precision, recall, F1, AUC-ROC
Handling imbalanced datasets — SMOTE, class weights, threshold tuning

Lab: Classification — Customer Churn PredictionDocker Lab

6Unsupervised Learning & Dimensionality Reduction

K-means clustering, DBSCAN, and hierarchical clustering
Silhouette score and elbow method for cluster evaluation
PCA — principal component analysis for dimensionality reduction
t-SNE and UMAP for high-dimensional visualization

Lab: Customer Segmentation with ClusteringDocker Lab

7Model Tuning & Production Pipelines

Hyperparameter tuning — grid search, random search, Bayesian optimization
scikit-learn Pipelines and ColumnTransformers for reproducibility
Model serialization with joblib and pickle
Feature importance analysis and model interpretability with SHAP

Lab: ML Pipeline — End-to-End with scikit-learnDocker Lab

8Neural Networks Fundamentals

Perceptrons, activation functions, and the universal approximation theorem
Backpropagation and gradient descent variants (SGD, Adam, RMSprop)
Regularization — dropout, batch normalization, early stopping
TensorFlow and Keras — Sequential and Functional API

Lab: Neural Network — Image Classification with KerasDocker Lab

9Deep Learning — CNNs & Transfer Learning

Convolutional neural networks — filters, pooling, and feature maps
Transfer learning with pre-trained models (ResNet, VGG, EfficientNet)
Data augmentation strategies for small datasets
PyTorch fundamentals — tensors, autograd, and training loops

Lab: CNN Image Classifier with Transfer LearningDocker Lab

10Natural Language Processing

Text preprocessing — tokenization, stemming, lemmatization, stopwords
Bag of words, TF-IDF, and word embeddings (Word2Vec, GloVe)
Recurrent neural networks and LSTM for sequence modeling
Sentiment analysis pipeline from raw text to prediction

Lab: NLP — Sentiment Analysis on Product ReviewsDocker Lab

11Model Deployment & MLOps Basics

Serving models with FastAPI — input validation and response schemas
Containerizing ML models with Docker
Model versioning and experiment tracking with MLflow
Monitoring model performance and data drift in production

Lab: Deploy ML Model as FastAPI + Docker ServiceDocker Lab

12Capstone Project & Interview Preparation

End-to-end capstone project execution and presentation
Data science interview question patterns and whiteboard exercises
Statistics refresher — hypothesis testing, p-values, confidence intervals
Portfolio presentation and resume optimization for data roles

Lab: Capstone — Full ML Pipeline PresentationDocker Lab

Hands-On Labs Included

You build these yourself — guided exercises with real tools, not passive demos.

NumPy & Pandas Data Wrangling

Docker Lab

2 hours

PythonNumPyPandasJupyter

EDA & Visualization on Kaggle Dataset

Docker Lab

2 hours

PythonMatplotlibSeabornPandas

Classification — Customer Churn Prediction

Docker Lab

2.5 hours

Pythonscikit-learnXGBoostPandas

Neural Network — Image Classification with Keras

Docker Lab

2 hours

PythonTensorFlowKeras

NLP — Sentiment Analysis on Product Reviews

Docker Lab

2.5 hours

PythonNLTKTensorFlowscikit-learn

Who Is This For?

Freshers & Graduates

Just graduated or finishing your degree? This course gives you the practical skills and portfolio projects that campus placements and entry-level interviews demand.

Career Switchers

Moving from another domain into tech? The structured curriculum and real-world projects bridge the gap between theory and what employers actually look for.

Ideal If You Are:

Fresh graduates from CS, mathematics, statistics, or engineering disciplines
Business analysts and Excel power users moving into data science
Software developers who want to add ML skills to their toolkit
Domain experts in finance, healthcare, or e-commerce seeking data-driven career paths

Prerequisites

Basic Python programming (variables, functions, loops, lists)
High-school level mathematics (algebra, basic statistics)
A laptop with at least 8 GB RAM and a stable internet connection
No prior ML or data science experience required

Career Support Included

We don't just teach you — we help you land the job.

Mock Interviews

Practice with real-world interview scenarios. Get feedback on technical depth, communication, and problem-solving approach.

Resume Review

One-on-one review sessions to craft a resume that highlights your projects, skills, and achievements the right way.

Portfolio Coaching

Guidance on presenting your course projects as professional portfolio pieces that stand out to hiring managers.

LinkedIn Optimization

Tips and templates to optimize your LinkedIn profile so recruiters find you and reach out.

Learn from Industry Practitioners

Our instructors are working professionals who build production systems daily. They bring real-world experience, battle-tested patterns, and the kind of practical insight that textbooks can't teach.

Course Details

Format	Live Online
Duration	12 weeks
Schedule	18 sessions
Batch Size	Max 15 students
Certificate	Yes, on completion
Lab Setup	Docker-based (runs on your laptop)
Price	Enquire for pricing

Frequently Asked Questions

Will I get a job after completing this program?

We design the program to maximize your employability — real portfolio projects, interview preparation, and skills aligned with what hiring managers ask for. While we cannot guarantee placement, graduates who complete all labs and projects are well-prepared for data science and ML engineering interviews.

Do I need experience in machine learning or statistics?

No. We cover the necessary statistics and linear algebra within the program. You need basic Python skills (loops, functions, lists), but all ML concepts are taught from the ground up.

Do I need a powerful GPU for deep learning?

No. All deep learning labs are designed to run on CPU within Docker containers on standard laptops. For larger experiments, we guide you through using free GPU resources on Google Colab.

What datasets will I work with?

You will work with real-world datasets from domains like telecom (churn prediction), e-commerce (recommendations), finance (fraud detection), and healthcare. We use publicly available datasets from Kaggle and UCI ML Repository.

How is this different from a Coursera or Udemy data science course?

This is a live, instructor-led program with a 15-student batch size. You get real-time feedback, code reviews, and personalized guidance — not pre-recorded videos. The curriculum is designed by an architect who has hired data scientists at Fortune-500 companies.

What if I miss a live session?

All sessions are recorded and available on the student portal within 24 hours. The instructor and TAs are available on Slack for questions between sessions.