Data Science and Machine Learning Course & Curriculum

NCPL's hands-on Data Science and Machine Learning training — full curriculum, real projects, certification prep, and job placement support. We train, mentor and place you.

Module 1: Python, Statistics & Data Foundations

Build the core toolkit every data scientist needs

  • Python for data science: NumPy, Pandas, Matplotlib, Seaborn
  • SQL for data extraction and analytics
  • Descriptive & inferential statistics, hypothesis testing
  • Probability distributions and Bayesian thinking
  • Exploratory Data Analysis (EDA) workflows
  • Data cleaning, wrangling, and feature engineering
  • Git, GitHub, Jupyter, and reproducible notebooks

Module 2: Machine Learning Algorithms

Master supervised and unsupervised ML with scikit-learn

  • Regression: Linear, Polynomial, Ridge, Lasso
  • Classification: Logistic Regression, KNN, SVM, Naive Bayes
  • Tree-based models: Decision Trees, Random Forest, XGBoost, LightGBM
  • Unsupervised learning: K-Means, DBSCAN, Hierarchical clustering
  • Dimensionality reduction: PCA, t-SNE, UMAP
  • Model evaluation: cross-validation, ROC-AUC, confusion matrix
  • Hyperparameter tuning with GridSearchCV & Optuna

Module 3: Deep Learning & Modern AI

Neural networks, NLP, computer vision, and GenAI

  • Neural network fundamentals with TensorFlow & PyTorch
  • CNNs for computer vision and image classification
  • RNNs, LSTMs, and Transformers for sequence data
  • Natural Language Processing: tokenization, embeddings, BERT
  • Time series forecasting with ARIMA, Prophet, and LSTMs
  • Introduction to LLMs, RAG, and prompt engineering
  • Recommendation systems and collaborative filtering

Module 4: MLOps, Deployment & Capstone

Productionize models and deliver end-to-end projects

  • Model packaging with Docker and FastAPI
  • MLOps essentials: MLflow, DVC, model registry
  • Deploying models on AWS / Azure / GCP
  • CI/CD for ML pipelines with GitHub Actions
  • Model monitoring, drift detection, and retraining
  • Responsible AI: bias, fairness, and explainability (SHAP, LIME)
  • Capstone: end-to-end ML project + portfolio & interview prep