Complete Data Science & Machine Learning

Program Overview

From raw unstructured datasets to deployable predictive models. Master Python, Pandas, Scikit-Learn, PyTorch, statistical hypothesis testing, classification/regression algorithms, and cloud ML deployments on AWS.

What You Will Master:

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Master data wrangling, EDA, feature engineering, and statistical hypothesis testing

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Implement supervised learning: Random Forests, XGBoost, LightGBM, and SVMs

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Build unsupervised clustering and anomaly detection systems (K-Means, DBSCAN, PCA)

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Construct deep learning neural networks with PyTorch for vision and sequence data

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Deploy machine learning models with FastAPI, Docker, and AWS SageMaker endpoints

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Set up experiment tracking, model registry, and data drift monitoring with MLflow

Industry-Aligned Curriculum

Designed with tech leads to bridge theory with real enterprise engineering demands.

5 Modules • 16 Weeks Structured Track • Hands-On Live Coding Labs

01 Python for Data Science, NumPy & Advanced Pandas

Weeks 1-3

Vectorized operations, multi-dimensional slicing, and broadcasting with NumPy
Data cleaning, missing value imputation, and complex aggregation in Pandas
Exploratory Data Analysis (EDA) and publication-ready visualizations with Seaborn
SQL for Data Science: Window functions, CTEs, self-joins, and query profiling

02 Applied Statistics & Probability for Modeling

Weeks 4-6

Probability distributions: Normal, Binomial, Poisson, and Central Limit Theorem
Hypothesis testing: t-tests, ANOVA, Chi-Square, p-values, and A/B test design
Correlation, covariance, collinearity diagnosis, and VIF calculation
Dimensionality reduction with Principal Component Analysis (PCA) and t-SNE

03 Supervised & Unsupervised Machine Learning Mastery

Weeks 7-10

Linear & Logistic Regression with regularizations (L1 Lasso, L2 Ridge)
Decision Trees, Ensemble learning, Random Forest, and Gradient Boosting (XGBoost)
Hyperparameter tuning: GridSearchCV, RandomizedSearch, and Optuna Bayesian methods
Clustering algorithms (K-Means, DBSCAN) and anomaly detection

04 Deep Learning & Neural Network Architectures

Weeks 11-13

Multi-Layer Perceptrons (MLPs), backpropagation, and activation functions
Convolutional Neural Networks (CNNs) for image recognition in PyTorch
Recurrent Neural Networks (RNNs, LSTMs) and Attention mechanisms for sequence data
Transfer learning with pretrained models and Hugging Face pipelines

05 MLOps, Cloud Deployment & Capstone Defense

Weeks 14-16

Model packaging with FastAPI, Docker, and REST endpoints
Experiment tracking, artifact storage, and model versioning with MLflow
Deploying to AWS EC2 and SageMaker, continuous evaluation, and drift detection
Final capstone showcase, mock technical interviews, and resume review

Production Capstone Projects

Build verified GitHub repository projects to showcase in your portfolio and resume.

Fintech Capstone

Customer Churn Prediction & Lifetime Value System

High-accuracy gradient boosted model with automated feature engineering, SHAP interpretability values, and dynamic retention dashboard in Streamlit.

Production ML

End-to-End E-Commerce Recommendation Engine

Hybrid collaborative and content-based recommendation service handling 500,000+ items with low-latency API serving via FastAPI.

Real-time Streaming

Automated Fraud Detection & Risk Scoring Pipeline

Anomaly detection model handling highly imbalanced transaction streams with synthetic data balancing (SMOTE) and Kafka ingestion.

Technologies & Tools You Will Use

Practical mastery on the modern tech stacks demanded by high-growth startups and tech giants.

Python Pandas NumPy Scikit-Learn PyTorch TensorFlow XGBoost MLflow Docker AWS Streamlit SQL

Learn from Industry Veteran

Live mentorship and code reviews from practitioners who have shipped software at scale.

Rahul Sharma

Principal Data Scientist & Machine Learning Lead

Former Lead Data Scientist at Fortune 500 tech firms. Expert in statistical modeling, predictive algorithms, and large-scale MLOps.

Career Outcomes & Placement Support

Comprehensive career guidance from Day 1 to land competitive tech roles.

Target Roles You Qualify For:

🎓 Data Scientist 🎓 Machine Learning Engineer 🎓 Predictive Analytics Specialist 🎓 Business Intelligence Lead

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Resume & LinkedIn Review by hiring managers

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1-on-1 Mock Technical & Behavioral Interviews

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Direct Placement Referrals to 500+ Partner Companies

Frequently Asked Questions

Got questions about admissions, batch schedules, or refunds? Here are quick answers.

Who is eligible to join this course?

College students, fresh graduates, and working professionals looking to upskill or transition into high-paying tech careers are all eligible. Basic familiarity with computer operations is sufficient; foundational concepts are thoroughly taught in the initial modules.

What happens if I miss a live class?

All live lectures are recorded in high-definition and uploaded to your student portal within 2 hours. You retain lifetime access to recordings, lecture slides, code repositories, and batch discussion forums.

How does the monthly EMI payment work?

We offer flexible zero-cost and low-cost monthly EMI options starting at just ₹2,500 / month. You can split the tuition without financial stress and focus 100% on learning.

Will I receive a recognized certificate?

Yes! Upon completing the syllabus and submitting the required capstone projects, you will receive an industry-recognized Certificate of Completion from Talvion IT Academy with a verifiable credential ID for LinkedIn.