Every session uses actual business problems. Your trainer brings live corporate datasets into the classroom and solves real problems with you — not hypothetical case studies. By month 4, you have a portfolio that hiring managers recognize.
While other programs are still teaching 2020-era ML, our curriculum covers Generative AI, Large Language Models (LLMs), RAG systems, and autonomous AI agents — the skills companies are actively hiring for right now.
You will build machine learning models and neural networks using NumPy before using frameworks. This deep understanding makes you stand out in technical interviews where others fail.
Interview prep starts early so you have 3 months to practice. You get personal feedback from industry mentors, resume reviews, LinkedIn profile optimization, and access to our private data science job fair.
Build Your Own Project (BYOP) — you do not complete a template project. You propose, build, and present a real project relevant to your target industry. This gives your portfolio a story that is uniquely yours.
After each module, you take a test. A minimum 60% score is required to proceed. This keeps quality high and ensures every PDCloudEx alumni is someone employers can trust.
On successful completion, you receive the 7-Month Industrial Hands-On Certification from PDCloudEx — a credential that signals practical skill, not just course completion.
| Python | SQL | NumPy / Pandas | Scikit-Learn | TensorFlow / Keras |
|---|---|---|---|---|
| PyTorch | LangChain | OpenAI API | Hugging Face | FAISS / Pinecone |
| Matplotlib / Seaborn | Tableau | Power BI | Git & GitHub | Google Colab / Jupyter |
| Month | Focus Area | What You Learn + Outcome |
|---|---|---|
| M1 | Python & SQL Foundations | Python syntax, data structures, functions, OOP | SQL queries, joins, aggregations, subqueries | Outcome: Write production-quality Python scripts and query any database. |
| M2 | Data Analysis & Statistics | Pandas, NumPy, Matplotlib, Seaborn | Probability, distributions, hypothesis testing, A/B testing | Outcome: Analyze real datasets and deliver business insights from raw data. |
| M3 | End-to-End Machine Learning | Linear & logistic regression, SVMs, decision trees, random forests, boosting | Feature engineering, model evaluation, cross-validation | Outcome: Build and deploy your first ML model on a real dataset. |
| M4 | ML Projects + Mock Interviews | Advanced ML: PCA, clustering, anomaly detection | Industry project work | Mock interview round 1 starts | Outcome: Portfolio-ready ML project + first round of interview practice. |
| M5 | Deep Learning & NLP | Neural networks from scratch, CNNs, RNNs, LSTMs | NLP: tokenization, embeddings, transformers, BERT | Outcome: Build a sentiment analysis or image classification model. |
| M6 | Generative AI & LLM Systems | Prompt engineering, fine-tuning LLMs, RAG architecture | LangChain, OpenAI API, Hugging Face | Building autonomous AI agents | Outcome: Deploy a production-grade LLM-powered application. |
| M7 | Career Strategy & Placement | Resume building, LinkedIn optimization, portfolio review | Mock interviews with industry mentors | Job fair access | Outcome: Walk out with a job-ready profile and active placement support. |
| Feature | PDCloudEx | upGrad | Simplilearn | Coursera |
|---|---|---|---|---|
| GenAI & LLM Curriculum | ✅ Yes | ✅ Yes | Partial | ❌ No |
| Real Industry Projects | ✅ Yes | ❌ No | ❌ No | ❌ No |
| Build Your Own Project (BYOP) | ✅ Yes | ❌ No | ❌ No | ❌ No |
| 1:1 Mentor Sessions | ✅ Yes | Paid Extra | ❌ No | ❌ No |
| Mock Interviews from Month 4 | ✅ Yes | ❌ No | ❌ No | ❌ No |
| Live Industry Trainer | ✅ Yes | Recorded | Recorded | Recorded |
| Placement Support | ✅ Active | ✅ Active | Partial | ❌ No |
| Program Duration | 7 Months | 11 Months | 6 Months | Self-paced |
| Price | ₹84,000* | ₹1,50,000+ | ₹1,00,000+ | ~₹50,000/yr |
Only 20 seats available. Fill the form and our counsellor will call you within 24 hours to answer all your questions — no pressure, just clarity.
Module 1: Python Fundamental for Data ScienceModule 2: SQL for Data Science Module 3: Statistics for Data Science Module 4: Linear Algebra for Data Science Module 5: Exploratory Data Analysis (EDA) Module 6: Data Visualization & Tools using Python Module 7: Machine Learning Module 8: Logistic Regression Module 9: Machine Learning Module ( Naive Bayes ) Module 10: Machine Learning Module ( Support Vector Machines )
Module 11:
Machine Learning Module ( Decision Trees )
Module 12:
Machine Learning Module: Random Forests
Module 13:
Machine Learning Module: Boosting Algorithms
Module 14:
Machine Learning Module: Unsupervised Learning – Clustering
Module 15:
Machine Learning Module: Dimensionality Reduction – Principal Component Analysis (PCA)
Module 16:
Deep Learning
Module 17:
Natural Language Processing (NLP)
Module 18:
Generative AI