Class Location

Online (video chat via skype, google hangout etc)

Student's Home

Tutor's Home

Years of Experience in Automation Testing Training

15

Teaching Experience in detail in Automation Testing Training

Selenium with Java – Learn end-to-end web automation using Selenium WebDriver, Java, TestNG, Maven, Page Object Model, data-driven frameworks, and real-time project implementation. Cucumber (BDD Framework) – Understand Behavior Driven Development using Gherkin, feature files, step definitions, hooks, tags, reports, and industry-standard framework design. REST API Testing (Automation) – Gain hands-on experience in API testing concepts including HTTP methods, status codes, request/response validation, authentication, and automation using tools like Rest Assured with real-world scenarios. Cypress – Learn modern JavaScript-based automation for fast and reliable UI testing, including Cypress architecture, commands, assertions, fixtures, API testing, and CI integration. Playwright – Master next-generation automation for web applications using Playwright with TypeScript/JavaScript, covering cross-browser testing, fixtures, reporting, and scalable framework development. The sessions are live, interactive, and practical, with real-time examples, assignments, interview-focused guidance, and framework-level implementation. Special emphasis is given to industry best practices, project-based learning, and career-oriented preparation.

Class Location

Online (video chat via skype, google hangout etc)

Student's Home

Tutor's Home

Years of Experience in Python Training classes

11

Teaching Experience in detail in Python Training classes

I am conducting online training programs in Python, Data Science, and Machine Learning, designed to help learners develop strong technical and analytical skills required for modern IT and data-driven roles. These courses are suitable for students, freshers, and working professionals who want to build a solid foundation or advance their careers in analytics and AI. The Python course covers core programming concepts including data types, control structures, functions, object-oriented programming, exception handling, and file handling, along with hands-on practice using real-world examples. The Data Science module focuses on data collection, data cleaning, exploratory data analysis (EDA), statistics, and data visualization using libraries such as NumPy, Pandas, Matplotlib, and Seaborn. Learners will gain practical experience in analyzing datasets and extracting meaningful insights. The Machine Learning training introduces key concepts such as supervised and unsupervised learning, regression, classification, clustering, model evaluation, and feature engineering. Implementation is done using scikit-learn, with real-time datasets and mini-projects.