Machine Learning Engineer Course & Curriculum

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

Module 1: Data Engineering & Fundamentals

15 hours mastering data handling, manipulation, and storage solutions.

  • Python for data science and ML
  • Pandas, NumPy, and data manipulation
  • Data cleaning and preprocessing
  • Feature engineering techniques
  • SQL and database fundamentals
  • Big data tools introduction
  • Data pipeline design

Module 2: Machine Learning Algorithms

15 hours building predictive models with supervised and unsupervised learning.

  • Linear and logistic regression
  • Decision trees and random forests
  • Support vector machines
  • Clustering algorithms (K-means, DBSCAN)
  • Dimensionality reduction (PCA, t-SNE)
  • Model evaluation and validation
  • Scikit-learn framework mastery

Module 3: Deep Learning & Neural Networks

12 hours designing complex architectures for advanced ML applications.

  • Neural network fundamentals
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • TensorFlow and PyTorch frameworks
  • Transfer learning techniques
  • Hyperparameter tuning
  • Model optimization strategies

Module 4: Model Deployment & Production

8 hours taking models from development to scalable cloud solutions.

  • AWS SageMaker deployment
  • Model serving with Docker
  • REST API development
  • MLOps best practices
  • Model monitoring and maintenance
  • A/B testing frameworks
  • Production performance optimization