medini bv
Interactive Machine Learning lessons with problem-solving focus
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medini bv
Bachelors degree
/ 55 min
About your data science tutor
I have pursued my master degree in Robotic Engineering from M S Ramaiah University of Applied Sciences Banglore and bachelors in Electronics and Communication Engineering. My roles and responsibilities and learning path towards Industry, - I worked as a intern at JSW steels Ballary by exploring the opportunities and learning's i was able to create the static Web-Page for the Company that can take the order for the customers for the products. - Further continuing the path i was intern at ComAvia System Technologies pvt. ltd Jalahalli Bangalore. During the period i has an extreme exposure to embedded system IOT. Also carried a mini project to publish the finger print data to cloud and retrieve it at the client and match the data. The main aim of the project is to make a cloud integration for the Bio metric attendance system. - I started to work as Robotics trainer for national and international students as freelancer in Wiingy Technology Pvt. Ltd during the period of M.Tech pursuing. Here i handled different course including Robotics, IOT, Arduino Programming, App development, STEM Education, Python, MATLAB, Machine learning etc. - After completion of M.Tech i joined Waveaxis Technology Pvt. Ltd as a Engineer Trainee in computer vision and Image processing. After the training period i prompted as Junior software developer. In this turning mode i had contributed my implementation 2 major project and 3 minor projects. Computer Vision, Machine Vision, Image processing, Halcon, Deep Learning, Python, OpenCV are skills of experience. - Further in the carrier moved ahead to join JyoSH AI solution Pvt, Ltd as a Senior Robotic Engineer for handling the areas of Embedded system design (H/W and S/W), Robotics, Robotics Vision, Machine learning and deep learning, Image processing, Sensor Integration, STM controller programming, Jetson GPU python coding for Machine vision in realtime, camera calibration and integration etc. - progressively i joined DLithe Consultancy Pvt. Ltd. as a Embedded Engineer. As a Embedded Engineer am handling the domains of Embedded Hardware, Software, Artificial Intelligence, Machine Learning and Robotics. being the technical expert and working on research and innovation in the domain to build the products for customer and also give the quality guidance for the students and teachers in enhancement of the industrial requirement and niche technology exploration.
medini graduated from GOVERNMENT ENGINEERING COLLEGE RAMANAGARA
Data Science tutor skills
Data visualization
Machine learning
Statistical analysis
Business intelligence
Data engineering
Learner types for data science class
College
Data sciece class overview
As an expert of Artificial Intelligence, Machine Learning and Robotics and being the technical expert and working on research and innovation in the domain to build the products for customer and also give the quality guidance for the students and teachers in enhancement of the industrial requirement and niche technology exploration. I work as per the student knowledge and understanding. Making the concept clear making them independent after learning to solve any type of problems. In this exciting journey i trained more than 1000+ students in various domain receiving a constant support and encouragement for teaching. The way of teaching that i adopt has always been a grace for me to deal and comprehensive the content effective for all grades of students. I always believe in clear understanding and breakdown the problem to possible solution as minimal as possible to develop a competitive skills to solve the problem statement or coding skill. Trying to provide best support, helping them with the learning growth, supportive materials handing and doubt clarification anytime which i follow in the effective tutoring.
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Data Science concepts taught by medini
The student and tutor reviewed and practiced applying Thevenin's theorem to solve complex electrical circuits. They worked through multiple examples involving different circuit configurations, including those with multiple voltage sources, current sources, and various resistor combinations, and briefly touched upon converting between Thevenin and Norton equivalents. The tutor plans to send more practice questions to the student.
Thévenin's Theorem
Norton's Theorem
Superposition Theorem (Implicit Application)
Circuit Analysis Techniques for Multiple Sources
The Tutor and Student reviewed Thevenin's and Norton's theorems, practicing the conversion between the two equivalent circuits. They worked through example problems, focusing on calculating equivalent resistance, Thevenin voltage (V_TH), and understanding voltage division in different circuit configurations. The session aimed to solidify the student's understanding of these network reduction techniques.
Norton's Theorem Fundamentals
Thevenin's Theorem vs. Norton's Theorem
Calculating Equivalent Resistance (R<0xE2><0x82><0x99> / R<0xE2><0x82><0x97><0xE2><0x82><0x9B>)
Determining Thevenin Voltage (V<0xE2><0x82><0x97><0xE2><0x82><0x9B>) and Norton Current (I<0xE2><0x82><0x99>)
The class covered Thevenin's and Norton's theorems for simplifying electrical circuits. The student practiced applying these theorems to calculate equivalent voltage/current sources and resistances in given circuits, with further concepts like AC circuit analysis and phasors mentioned for future sessions.
Series vs. Parallel Resistances and Voltage/Current Behavior
Circuit Reduction and Equivalence
Norton's Theorem
Thévenin's Theorem
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.
Ensemble Learning: Bagging vs. Boosting
XGBoost (Extreme Gradient 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.
Supermesh Analysis
Nodal Analysis
Supernode
Ideal vs. Practical Components
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