Build practical skills in data analysis, machine learning, model development, MLOps and production-ready deployment through a structured, project-based learning journey.
Days*
Program Duration*
Hands-on Labs*
Industry Projects*
Mock Interviews
Build the knowledge and practical skills needed to understand, develop and deploy machine learning solutions.
The ML, MLOps & Data Science program introduces students to the complete machine learning lifecycle — from data collection and preparation to model development, evaluation, deployment and monitoring.
Students learn how to transform raw data into meaningful insights and build machine learning solutions that can be used in practical business and technology scenarios.
Understand the essential skills required to build reliable, practical and deployment-ready machine learning solutions.
Understand how data supports business and operational decisions.
Learn how to train, evaluate and improve predictive models.
Understand how ML models move from notebooks into applications.
Explore automation, versioning, deployment and model monitoring.
Develop practical skills across data science, machine learning, model deployment and MLOps through structured learning and project-based experiences.
Build the technical foundation and practical capabilities required to work with modern machine learning workflows.
Python programming, NumPy and Pandas.
Programming FoundationData cleaning, exploration and visualization.
Data UnderstandingSupervised and unsupervised learning.
Machine LearningMetrics, validation and model improvement.
Model PerformanceExperiment tracking, versioning and deployment.
Production WorkflowPractical projects, documentation and GitHub.
Career PreparationPython, NumPy, Pandas, statistics, data cleaning, exploratory data analysis and visualization.
Supervised learning, unsupervised learning, regression, classification, clustering and model evaluation.
Feature engineering, model optimization, pipelines, NLP fundamentals and advanced model workflows.
ML pipelines, experiment tracking, model versioning, APIs, containers and monitoring.
End-to-end project, GitHub portfolio, resume preparation, mock interviews and project presentation.
The curriculum is designed to connect concepts with practical exercises, project work and deployment-oriented learning. Final phase durations will be confirmed before publishing.
Apply your learning through practical projects that cover data analysis, machine learning, model deployment, MLOps workflows and model monitoring.
Clean and analyze datasets, identify important trends and present meaningful insights through interactive data visualizations.
Develop a predictive model, evaluate its performance and expose the trained model through an application interface for practical usage.
Create a repeatable workflow for model training, experiment tracking, versioning and deployment using structured MLOps practices.
Explore model performance tracking, monitor important metrics and identify changes in model behaviour through structured monitoring workflows.
A structured technology ecosystem covering programming, data science, machine learning, deployment and MLOps.
The final technology list should be reviewed and confirmed according to the tools, frameworks and platforms actually included in the training curriculum. Technologies may be updated based on the confirmed hands-on labs and project requirements.
Develop practical data science, machine learning and MLOps skills through structured learning milestones and industry focused project experience.
Work with datasets, clean data, identify patterns, prepare reports and support data-driven decisions.
Support data exploration, statistical analysis, predictive modelling and machine learning projects.
Support model development, evaluation, feature engineering and machine learning workflows.
Work with model deployment, automation, versioning, monitoring and ML production workflows.
Build Python-based applications, automation scripts, data utilities and backend development workflows.
Assist with AI and machine learning initiatives, data preparation, model testing and documentation.
Gain practical exposure to datasets, analytics, model development and project-based learning.
Support deployment pipelines, model operations, monitoring and repeatable machine learning workflows.
Track your learning progress through structured TekPrizm milestones designed around practical skills and project-based learning.
Python programming, NumPy, Pandas and data handling fundamentals.
Data cleaning, exploration, visualization and analytical thinking.
Supervised learning, unsupervised learning, evaluation and model building.
Feature engineering, model pipelines and advanced machine learning workflows.
Experiment tracking, versioning, automation and machine learning operations.
Model APIs, deployment workflows, monitoring and performance tracking.
Certification names represent proposed TekPrizm learning milestones. Final issuance and assessment criteria should be confirmed before publication. These milestones do not represent external vendor certifications unless officially authorized.
Build practical experience through data analysis, machine learning projects, model evaluation and deployment workflows. A documented portfolio can help demonstrate technical capability during project reviews and interviews.
Students work through practical learning activities that connect data concepts with real project requirements and technical workflows.
Understand data preparation, analytics and machine learning concepts.
Apply concepts through model development, evaluation and project workflows.
Organize project code, documentation and outcomes in a GitHub-based portfolio.
Practise explaining technical decisions, projects and learning outcomes.
Final number to be confirmed
Final number to be confirmed
Practical ML workflows
GitHub-based project documentation
Final number to be confirmed
Course certificates / assessments
Final activity counts and assessment details should be confirmed by TekPrizm before publishing.
Students can explore opportunities in organizations that use data analytics, predictive modelling, automation and machine learning solutions across different industries.
Actual hiring depends on role requirements, candidate skills, interview performance and employer selection processes.
Develop practical skills that can be demonstrated through projects and technical discussions.
Data, AI and automation solutions for technology teams and business clients.
Data analytics, business intelligence and machine learning services.
Data-driven products, intelligent applications and product analytics.
Risk analysis, fraud detection and financial analytics applications.
Recommendation systems, customer analytics and demand prediction.
Analytics and ML applications for research, operations and decision-making.
Build a portfolio that helps you communicate your technical learning and project experience with clarity.
Experience a structured learning journey that combines data science concepts, machine learning projects and practical MLOps workflows.
Final duration to be confirmed
Program ScheduleSuggested 3–4 hours, subject to final schedule
Learning TimeOn-Campus / Online / Hybrid
Flexible LearningData Science Trainer / ML Trainer / MLOps Mentor
Guided LearningFind answers to common questions about the program, learning process, projects and certification.
Build practical data skills, develop machine learning projects and explore the tools used in modern ML workflows.