Dr. Gurinderjeet Kaur
Hands-on Computer Science tutor with engaging, problem-solving lessons
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Private tutor - Dr. Gurinderjeet Kaur
Doctorate degree
Dr. Gurinderjeet - Know your tutor
Hello, I am Dr. Gurinderjeet Kaur, a dedicated computer science educator with a Doctorate in Computer Science Engineering. I simplify complex concepts and emphasize a hands-on learning approach, guiding students through coding exercises and real-world projects. My goal is to help students build practical skills, confidence, and critical thinking. I specialize in Computer Science, CSS, Databases, HTML, Java, JavaScript, Matlab, Python, R, SQL, Artificial Intelligence, Microsoft Excel, C, C++, and Coding for Kids. Additionally, I teach Mathematics and Science up to grade 10 level. Whether you're a school student, a college learner, or a professional, I tailor lessons to fit your level. Additionally, I can also teach Mathematics and Science subjects up to grade 10 level. My teaching approach in these subjects emphasizes clarity of concepts, with a focus on making abstract theories understandable through real-world examples and practical exercises. I tailor lessons to the learning pace of each student, ensuring that they grasp every concept fully before moving on. My teaching is interactive, encouraging curiosity and active participation. I focus on helping students apply their knowledge practically, fostering problem-solving abilities, and creating a supportive environment where they feel comfortable asking questions. Tailoring Learning for Every Level Whether you are a school student, college student, or a professional looking to upskill, I can cater to learners at all levels. I have experience teaching students with varying degrees of expertise and can adapt my teaching style to suit their individual needs.
Meet Dr. Gurinderjeet
Dr. Gurinderjeet graduated from Thapar Institute of Engineering and Technology India


Programming tutor specialities
Project help
Test prep
Debugging
Common Core State Standards - CCSS (USA)
Next Generation Science Standards - NGSS (USA)
Australian Curriculum (AU)
New Zealand Curriculum - NZC (NZ)
GCSE (UK)
A-Levels (UK)
Upskilling
Homework help
Exam prep
Programming class overview
My teaching style is designed to foster a supportive and interactive learning environment where students feel empowered to ask questions, share ideas, and collaborate with others. I firmly believe that the best learning happens when students are encouraged to think critically and apply their knowledge in real-world situations. I focus on: Interactive Learning: I regularly use live coding sessions, real-time problem-solving, and collaborative projects to ensure that students not only understand the material but can also apply it practically. This helps students stay engaged and gives them a sense of accomplishment when they see their code come to life. Encouraging Curiosity: I encourage students to ask questions and explore beyond the curriculum. In programming, learning never stops. I provide guidance on how to approach learning new languages, frameworks, or concepts on their own, fostering a spirit of curiosity that will benefit them long after the course ends. Practical Applications: Every concept I teach is paired with practical examples and real-world scenarios. Whether it's building a website, solving a complex algorithm problem, or developing an AI model, I ensure that students are ready to apply their skills in real-world environments.

Computer Science concept taught by Dr. Gurinderjeet
The student and tutor reviewed functions to process lists of any, including calculating depth and removing elements. The student then worked on understanding expression trees and their evaluation using mutual recursion. The student will send notes for the next session, which will cover lambda and abstract functions.
List of Any Definition
Determining Depth of a List of Any
Atoms in Racket
Evaluating Expression Trees (Mutual Recursion)
Expression Trees
Removing Elements from a List of Any
The Tutor and Student reviewed advanced JavaScript concepts, including higher-order functions, callbacks, map, filter, and reduce methods. The Student was assigned practice questions related to these topics as homework. They scheduled their next session to continue practicing and increase the difficulty level.
Higher-Order Functions
Callbacks
Map Method
Filter Method
Reduce Method
The session involved an extra credit assignment focused on creating an ETL and ML pipeline for real-time fraud detection. The student and tutor reviewed the project requirements, data structure, and expected components. The student began setting up the project repository and folder structure, with plans to start filling in the code and addressing the 'todos' outlined in the assignment.
Synthetic Data Generation
Pydantic Schemas
AI Prompt Engineering
Git Branching Strategy
Data Quality Issues
ML Pipeline
ETL Pipeline
The session covered Bayesian statistics, including Poisson and Gamma distributions, and uniform distributions. The Student worked on problems involving finding posterior distributions with Gamma priors and uniform likelihoods. The Tutor assigned the Student the task of watching videos and creating cheat sheets for various distributions for future sessions.
Poisson Distribution
Gamma Distribution
Poisson-Gamma Conjugate Prior
Uniform Distribution
Posterior Distribution Calculation
Likelihood Function for Uniform Distribution
The session covered Bayesian inference, including prior distributions, likelihood, and posterior distributions. The Student worked through an example problem related to posterior probability and the tutor assisted with the concepts and formulas. The Student is expected to practice similar problems for the next session.
Prior Distribution
Likelihood Function
Joint Distribution
Bayesian Inference
Marginal Distribution
Location Normal Model Example
Posterior Distribution
The Student and Tutor reviewed and debugged caching implementations in a Python project, focusing on in-memory and Redis caching strategies. The Student implemented warm caching and clear caching functionalities, resolving errors in hit rate calculations and data storage logic. The session concluded with successful test runs after correcting the caching implementation and benchmark configurations, with plans to work on an ML pipeline ETL project next session.
Cache Clearing
Conditional Caching Logic
Warm Cache Implementation
In-Memory vs. Redis Caching
Cache Hit Rate Calculation
Debugging Caching Issues
Your programming tutor also teaches
Computer Science
Databases
Web Development
App Development
Artificial Intelligence
Learner for programming class
School
All Levels
College
Adult / Professional
Home schooled
Teaching tools used by tutor
Android Studio
Bitbucket
Jupyter Notebook
NetBeans
Dynamic programming classes
Chat for quick help
Note taking
Parent feedback
Mobile joining
Pets are welcomed
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