medini bv
Collaborative Computer Science & coding lessons with creativity
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medini bv
Bachelors degree
Enroll after the free trial
Each lesson is 55 min
50 lessons
20% off
/ lesson
30 lessons
15% off
/ lesson
20 lessons
10% off
/ lesson
10 lessons
5% off
/ lesson
5 lessons
-
/ lesson
1 lessons
-
/ lesson
medini - Know your tutor
Hello, I'm Medini BV, a Computer Science and Robotic Engineer and tutor with a Bachelors in Electronics and Masters in Robotics. Am Having 3+ years of Industrial experience and 2+ years of tutoring. In this journey i have poured knowledge to 200+ students including working professional, Engineering, College and School students. My teaching philosophy revolves around making complex concepts simple for students and give depth knowledge with practical implementation. I specialize in teaching Python, Artificial Intelligence, Machine Learning. Deep Learning, Computer Vision, Data Science, C, C++, Embedded Systems, Electronics, Arduino programming, ROS and STEM for kids. I believe in engaging students through interactive learning methods to ensure they grasp the subject thoroughly. Let's embark on a learning journey together!
medini graduated from GOVERNMENT ENGINEERING COLLEGE RAMANAGARA


Programming tutor specialities
Test prep
Advanced Placement (AP) Program (USA)
State-Specific Standards (USA)
Project help
Homework help
Assignment help
Exam prep
Next Generation Science Standards - NGSS (USA)
Debugging
Learner for programming class
All Levels
Adult / Professional
School
College
Programming class overview
As a Computer Science and Programming tutor, I believe in making learning engaging and collaborative. I personalize classes based on students' interests, level of understanidng making the session more interactive. My teaching style is empathetic and practical, focusing on real-world applications of concepts. I also incorporate creative methods to enhance learning, such as gamified activities. I create a structured plan with exercises to help students build their skills gradually. I aim not only to help them academically but also to prepare them for internships and jobs in leading tech companies giving them industrial exposure and requirements.
Your programming tutor also teaches
Artificial Intelligence
C
C++
Coding for kids
Computer Science
Matlab
Flexible Scheduling
Allows 1h early scheduling
Allows 1h early rescheduling
Can wait for 20 mins after joining

10 day Refund
Free Tutor Swap

Computer Science concepts taught by medini
The session covered mesh analysis, including super mesh analysis for circuits with current sources bridging loops. The tutor and student also reviewed the superposition theorem, practicing its application to calculate currents and voltages by considering each source independently and then summing the results. The student was assigned practice problems for both mesh and superposition theorems.
Mesh Analysis
Superposition Theorem
Supermesh Analysis
The Tutor and Student reviewed advanced regression models, focusing on XGBoost as a superior alternative to Decision Trees and Random Forests due to its gradient boosting approach. They implemented XGBoost using Python, discussed key parameters and ensemble methods (bagging vs. boosting), and explored visualization techniques for model importance and tree structure. The next steps will involve moving to classification models.
XGBoost (Extreme Gradient Boosting)
Ensemble Learning: Bagging vs. Boosting
XGBoost Regressor vs. Ranker
The student and tutor practiced applying nodal analysis and the supernode concept to solve complex electrical circuits. They worked through several example problems, focusing on identifying nodes, formulating equations, and solving for unknown voltages and currents. The next topic planned is supermesh analysis.
Nodal Analysis
Supernode
Supermesh Analysis
Ideal vs. Practical Components
The student and tutor reviewed advanced circuit analysis techniques, focusing on super node analysis for nodal analysis and briefly touching upon super mesh analysis for mesh analysis. They worked through example problems to solidify the understanding of applying these methods to circuits with voltage sources between non-reference nodes.
Super Mesh Analysis
Mesh Analysis
Super Node Analysis
Node Analysis
The class focused on machine learning concepts, specifically decision trees and random forests. The tutor explained how decision trees are built using MSE to split data and discussed their limitations, leading into the introduction of random forests as an ensemble method to improve accuracy. Future topics will include other regression and classification models.
Random Forest: Ensemble Learning
Decision Trees: Core Concepts
Mean Squared Error (MSE)
Ensemble Learning and Random Forests
The class reviewed electrical circuit analysis techniques, specifically super loops and the Superposition Theorem. The student practiced applying these methods to solve circuit problems with multiple sources, and the tutor provided detailed explanations and examples. Future sessions will cover more complex combinations of these theorems and address remaining challenges with super loops.
Super Loop Analysis
Current Divider Rule
Superposition Theorem
Teaching tools used by tutor
Jupyter Notebook
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