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Machine Learning Engineer Machine Learning

Machine Learning Engineer

Master Python, machine learning algorithms, model optimization, and deployment to build intelligent, production-ready AI solutions.

This course provides practical training in machine learning engineering, covering Python programming, statistics, data preprocessing, supervised and unsupervised learning, feature engineering, model evaluation, and deployment. Students learn to develop predictive models, build reproducible ML pipelines, use deep learning fundamentals, and deploy machine learning applications through APIs and cloud platforms. Hands-on projects prepare learners for entry-level machine learning engineering and applied AI roles.

Duration
6 Months
Level
Intermediate
Format
Online
Language
English, Hindi
Time commitment
8–10 hours per week
Course code
MLE-101

Who this course is for

Computer science students, graduates, Python developers, data analysts, software engineers, and aspiring AI professionals who want to develop, evaluate, deploy, and maintain machine learning models.

What you will learn

  • - Develop Python programs for machine learning applications.
  • - Apply statistics and probability to model development.
  • - Clean, transform, and preprocess datasets.
  • - Perform feature engineering and feature selection.
  • - Implement supervised and unsupervised learning algorithms.
  • - Train regression, classification, and clustering models.
  • - Evaluate models using appropriate performance metrics.
  • - Improve models through hyperparameter tuning.
  • - Build reproducible machine learning pipelines.
  • - Develop neural network models using deep learning frameworks.
  • - Deploy models using APIs and application frameworks.
  • - Monitor model performance and detect data drift.
  • - Use Git, testing, and MLOps fundamentals in ML projects.

Course curriculum

Module 1: Python Programming for Machine Learning Module 2: Mathematics and Statistics Module 3: Data Preprocessing Module 4: Supervised Machine Learning Module 5: Unsupervised Machine Learning Module 6: Model Evaluation and Optimization Module 7: Feature Engineering and ML Pipelines Module 8: Deep Learning Fundamentals Module 9: Model Deployment and MLOps Module 10: Capstone Project

Prerequisites

- Basic computer and internet knowledge. - Python programming knowledge is recommended. - Basic mathematics, statistics, and algebra are helpful. - Familiarity with SQL is beneficial. - A laptop or desktop with internet access. - No previous professional machine learning experience is required.

Projects and practical work

- Project 1: House price prediction using regression. - Project 2: Customer churn prediction using classification. - Project 3: Customer segmentation using clustering. - Project 4: Fraud or anomaly detection using machine learning. - Project 5: Develop and deploy a prediction API using FastAPI. - Final Project: Build an end-to-end machine learning solution featuring data preprocessing, feature engineering, model training, evaluation, deployment, and performance monitoring.

Certificate

Certificate of Course Completion Students who successfully complete the course modules, practical assignments, assessments, and capstone project will receive an institute-issued Machine Learning Engineer Course Completion Certificate.