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ML & MLOps Masters 2026 - Build, Train, Evaluate, Deployment
New
100 students
ML & MLOps Masters 2026 - Build, Train, Evaluate, Deployment
Python + Stats to ML models, clustering, time series, and MLOps—build, evaluate, deploy end to end.
Created byDr. Satyajit Pattnaik, Satyajit Pattnaik, Surbhit Kumar
Last updated 6/2026
English
What you'll learn
- Master Python for Data Science — Write clean, efficient code for data manipulation, automation, and building ML applications.
- Build Strong Statistical Foundations — Understand probability, distributions, hypothesis testing, confidence intervals, and statistical inference used in real-w
- Work Professionally with SQL — Query, analyze, and extract insights from large databases with advanced SQL techniques
- Perform Expert Exploratory Data Analysis (EDA) — Uncover patterns, handle missing data, detect outliers, and create insightful visualizations to drive better de
- Build and Deploy Machine Learning Models — Master supervised and unsupervised learning algorithms (Regression, Classification, Clustering, Ensemble methods etc)
- Specialize in Time Series Analysis & Forecasting — Work with real-world time-dependent data using ARIMA, Prophet, LSTM, and other advanced forecasting technique
- Explore the world of MLOps and various concepts related to Ops
- Apply Everything Through Hands-on Projects — Work on multiple industry-style projects (e.g., predictive ai, customer churn, sales forecasting, recommendations)
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Course content
11 sections • 361 lectures • 64h 14m total length
Requirements
- No programming experience is needed.
- A laptop with atleast 8gb memory is recommended (4gb memory is also fine, can use Google Colab as a backup)
- No prior knowledge on SQL, or Machine Learning is needed
Description
Welcome to ML & MLOps Masters 2026 - Build, Train, Evaluate & Deploy Models! This course is designed for learners who want to master the full machine learning lifecycle—from Python and statistics through modeling (classification, regression, clustering, and time series) to production-grade deployment using MLOps.
Whether you’re starting out or already know the basics, you’ll learn how to build accurate models, evaluate them properly, and then package them into real pipelines that can be monitored, retrained, and improved over time.
What You Will Learn
In this Masters program, you will develop practical skills across:
Python for ML: Write production-minded Python code for data and ML workflows
Statistics for Modeling: Distributions, hypothesis testing, uncertainty, and assumptions that impact ML
Data Prep & EDA: Explore, clean, and transform datasets for reliable training
SQL (optional but applied): Query and shape data efficiently for ML use cases
Machine Learning Core: Train, validate, and tune models that actually perform
Classification / Regression / Clustering: Choose algorithms and metrics correctly
Time Series & Forecasting: Handle temporal data and build forecasting pipelines
Model Evaluation & Validation: Metrics, cross-validation, leakage prevention, and model diagnostics
MLOps Foundations: Model packaging, deployment patterns, versioning, and pipeline structure
Monitoring & Retraining: Detect drift, evaluate performance in production, and improve models
Real-World Project Development: Build end-to-end systems you can showcase
Projects You Will Build
You’ll work on multiple projects that mirror real business and technical needs. Example project directions include:
Cancer Risk Assessment
Churn Prediction
Course Structure
The course is delivered through modules designed to build momentum and ensure you retain everything you learn:
Video lessons (concept + implementation)
Hands-on coding exercises
Quizzes and checkpoints
Project-based learning (your portfolio grows module by module)
Conclusion
By the end of ML & MLOps Masters 2026 - Build, Train, Evaluate & Deploy Models, you won’t just “know ML”—you’ll know how to ship ML: build strong models, evaluate them with confidence, deploy them reliably, and maintain them using real MLOps practices.
Enroll now and start building models that work in production.
Who this course is for:
- Complete beginners willing to jump into the field of Data Science, AI & Gen AI
- Experienced Professionals willing to switch to Data Science, AI & Gen AI