Machine Learning with Python Training
Machine Learning with Python Training aims to instruct participants in Python programming to create machine learning models and use data science methods. Important subjects covered in the course include feature engineering, model evaluation, supervised and unsupervised learning, and the use of well-known libraries like Scikit-learn, Pandas, and TensorFlow. Participants learn how to handle big datasets to create prediction models for practical uses.
- 10+ Courses
- 30+ Projects
- 400 Hours
Machine Learning with Python Training is suitable for the following target audiences:
Data Scientists and Analysts: Professionals wishing to deepen their understanding of machine learning and use sophisticated predictive modeling in their jobs are best suited as data scientists and analysts.
Software Developers: Ideal for developers who wish to enhance their knowledge of Python’s data science skills and incorporate machine learning methods into their applications.
IT Professionals and Engineers: Designed for people who wish to improve organizational decision-making, automate procedures, and solve complicated challenges using machine learning approaches.
Students and Academics: Ideal for individuals who require practical expertise with Python machine learning and are pursuing jobs in data science, artificial intelligence, or similar sectors.
Data Scientist: Data scientists use data analysis and machine learning models to address business problems and create data-driven decisions.
Machine Learning Engineer: Create, develop, and implement machine learning models and algorithms in real-world settings across various sectors, including technology, healthcare, and finance.
AI Engineer: Develop applications for artificial intelligence by using machine learning models for tasks including computer vision, natural language processing, and autonomous systems.
Data Analyst: To evaluate huge datasets, derive insights, and aid in business decision-making, apply machine learning techniques.
Software Developer (ML-focused): Improve functionality and performance by incorporating machine learning techniques into software products and applications.
The increasing need for machine learning specialists in sectors including healthcare, banking, e-commerce, and technology in both the USA and Canada provides excellent employment possibilities with competitive pay and room for growth.
- Agile Overview
- Agile Manifesto & Principles
- Agile vs. Traditional (Waterfall) Approaches
- Scrum, Kanban, XP – overview of frameworks
- Role of a Business Analyst in Agile BA responsibilities in Agile teams
- Collaboration with Product Owner, Scrum Master, Dev Team
- Scrum Deep Dive
- Roles, Events, Artifacts
- Product Backlog, Sprint Planning, Review, and Retrospective
- Kanban Basics (WIP limits, continuous flow)
- Scaling Agile (SAFe, LeSS, Nexus) – intro level
- Tools included : Jira / Trello setup basics
- Gathering Requirements in Agile
- Just-in-time requirements
- Collaborative discovery
- User Stories & Acceptance Criteria
- INVEST criteria and Definition of Ready / Done
- Story Mapping & Backlog Grooming
- Jira / Confluence Training
- Backlog creation, story linking, boards
- Wireframing & Prototyping
- Tools: Balsamiq / Figma / Draw.io
- Facilitation Techniques
- Agile workshops (Story Mapping, Impact Mapping)
- Agile Planning Levels (Roadmap, Release, Sprint planning)
- Estimation Techniques (Planning Poker, T-Shirt sizing)
- Prioritization Methods (MoSCoW, Kano Model, WSJF)
- Performance Metrics (Velocity, Burnup/Burndown charts , Lead/Cycle Time)
- BA Contribution Metrics (Requirements churn, value tracking)
- How to create and maintain Dashboards in Jira
- Case Study: Analyse sprint reports & suggest improvements
- Stakeholder Management
- Effective Communication
- Facilitation & Conflict Resolution
- Agile Project Simulation End-to-end product backlog creation
- Resume Building for Agile BA
- Mock Interviews & Job Placement
This course is ideal for aspiring Business Analysts, working professionals transitioning to Agile roles, Product Owners, Project Managers, and anyone interested in Agile practices and business analysis.
No technical background is required. The course is designed to help non-technical individuals understand Agile concepts and tools in a practical, easy-to-follow manner.
The duration for Agile Business Analyst training is 2 months (8 weeks), with sessions held 2 times per week (either during week or weekends), including theory, hands-on practice and project work.
Yes, upon successful completion, you’ll receive a Certificate of Completion from Checkmate IT Tech. We also guide you on pursuing global certifications like IIBA-AAC and ICAgile-BA.
Absolutely! The course includes hands-on exercises, case studies, and a capstone project to simulate real Agile environments.
We offer online training classes to promote easy access to all candidates. Recordings are also made available for revision or if you miss a session.
Yes. We provide resume reviews, mock interviews, LinkedIn optimization, and guidance on job portals to help boost your chances in the job market.
You can enroll via our website or contact our support team directly via email or phone. We’ll guide you through the quick and easy registration process.
https://checkmateittech.com/
Email info@checkmateittech.com OR Call Us +1-347-4082054
- Submit Form
Job opportunities in USA and Canada
Data Scientist: Data scientists use data analysis and machine learning models to address business problems and create data-driven decisions.
Machine Learning Engineer: Create, develop, and implement machine learning models and algorithms in real-world settings across various sectors, including technology, healthcare, and finance.
AI Engineer: Develop applications for artificial intelligence by using machine learning models for tasks including computer vision, natural language processing, and autonomous systems.
Data Analyst: To evaluate huge datasets, derive insights, and aid in business decision-making, apply machine learning techniques.
Software Developer (ML-focused): Improve functionality and performance by incorporating machine learning techniques into software products and applications.
The increasing need for machine learning specialists in sectors including healthcare, banking, e-commerce, and technology in both the USA and Canada provides excellent employment possibilities with competitive pay and room for growth.
