A comprehensive 24-week job-ready program designed for beginners and working professionals to master Python, machine learning, deep learning, NLP, Generative AI, MLOps, and deployment through live classes and industry projects.
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Get placed or get a full refundCourse Manager: IIT Alumnus
Supervises curriculum & placement| Phase 1: FoundationsWeeks 1-6 | Phase 2: Machine LearningWeeks 7-14 | Phase 3: Deep Learning & AIWeeks 15-20 | Phase 4: Industry & DeploymentWeeks 21-24 |
|---|---|---|---|
| Week 1: Python for Data Science | Week 7: ML Fundamentals & Supervised Learning | Week 15: Deep Learning Foundations | Week 21: MLOps & Model Deployment |
| Python setup, Jupyter, variables, loops, functions, file I/O | ML types, train-test split, regression, regularization, evaluation metrics | ANN, forward/backpropagation, activation functions, optimizers, TensorFlow & Keras basics | ML lifecycle, MLflow, FastAPI/Flask, Docker, AWS/GCP/Hugging Face Spaces |
| Week 2: Python Advanced & Libraries | Week 8: Classification Algorithms | Week 16: Convolutional Neural Networks | Week 22: Data Engineering & Big Data |
| List comprehensions, lambda, OOP basics, NumPy, Pandas | Logistic Regression, Decision Trees, KNN, Naive Bayes, precision/recall/F1/ROC-AUC | CNNs, transfer learning, fine-tuning, augmentation, YOLO intro | ETL workflows, Apache Spark basics, Airflow, BigQuery/Redshift, Kafka intro |
| Week 3: Mathematics for Data Science | Week 9: Ensemble Methods | Week 17: Natural Language Processing | Week 23: Business Intelligence & Dashboarding |
| Linear algebra, statistics, probability, distributions, hypothesis testing | Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, LightGBM, SHAP | Text preprocessing, TF-IDF, embeddings, sentiment analysis, NER, LSTM classification | Power BI/Tableau, live data sources, KPI design, stakeholder storytelling, Python reporting |
| Week 4: Data Visualization | Week 10: Unsupervised Learning | Week 18: Transformers & Large Language Models | Week 24: Capstone Project & Career Prep |
| Matplotlib, Seaborn, Plotly, dashboard design, storytelling with data | K-Means, Hierarchical clustering, DBSCAN, PCA, t-SNE, UMAP | Attention, BERT, GPT, Hugging Face, fine-tuning, prompt engineering | Team capstone, resume, LinkedIn, mock interviews, GitHub portfolio cleanup |
| Week 5: SQL & Databases | Week 11: Model Optimization & Pipelines | Week 19: Generative AI & LLM Applications | Capstone Outcome: Full-stack AI product with deployment |
| SELECT, WHERE, JOIN, window functions, normalization, SQLAlchemy, MongoDB basics | GridSearchCV, RandomizedSearchCV, Optuna, pipelines, feature selection, SMOTE | OpenAI API, Claude API, RAG, LangChain, LlamaIndex, chatbot development | End-to-end industry-grade portfolio project for placement readiness |
| Week 6: Exploratory Data Analysis | Week 12: Time Series Analysis | Week 20: Computer Vision & Generative Models | Placement-oriented support across the final phase |
| Data cleaning, missing values, outliers, feature engineering, correlation, EDA workflow | Decomposition, stationarity, ADF test, ARIMA, SARIMA, Prophet, ML forecasting | GANs, Stable Diffusion basics, image generation APIs, CV applications, video analysis intro | Interview prep, mentor guidance, industry review, deployment readiness |
| Foundation Projects: CSV analysis, sales analysis, statistical EDA, COVID dashboard, SQL analysis, HR attrition EDA | ML Projects: House price prediction, fraud detection, churn prediction, segmentation, forecasting, recommender systems | AI Projects: MNIST classifier, cats vs dogs, sentiment analysis, Q&A bot, RAG chatbot, AI image generation app | Final Output: Production-ready deployed AI application |