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 engineering
Assignment help
Business intelligence
Predictive modeling
Statistical analysis
CoTutorAI modules
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Learner types for data science class
Data Science for intermediate
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.
Data Science concepts taught by medini
The Tutor introduced Large Language Models (LLMs) and their transformer-based architecture, explaining the evolution from traditional NLP to transformer models with attention mechanisms. They demonstrated how to interact with LLMs using a platform like Groq, covering API usage, prompts, and parameters, and discussed challenges like hallucinations and solutions like RAG for industry-specific applications.
Transformers and Attention Mechanism
LLM Architectures: Encoder-Only
Decoder-Only
and Encoder-Decoder
Temperature
Top-K
and Top-P Sampling
Hallucination and Retrieval Augmented Generation (RAG)
The class covered fundamental electrical circuit concepts, including Ohm's Law, series and parallel resistor combinations, and circuit problem-solving techniques. The student practiced calculating current, voltage, and resistance in various circuit configurations. Future sessions will explore capacitors, inductors, and advanced circuit analysis laws.
Ohm's Law: The Foundation of Circuit Analysis
Resistor Combinations: Series and Parallel Circuits
Solving Combined Series-Parallel Circuits
Basic Circuit Components and Concepts
The Student and Tutor conducted an introductory session on basic electrical circuits and the fundamental concept of resistance. They practiced calculating equivalent resistance in both series and parallel configurations, including a combination circuit example. The future curriculum for the Student's electrical technology course, focusing on Ohm's law, Kirchhoff's laws, and AC circuits, was also discussed.
Resistors in Series
Electrical Circuit Basics & Resistor Purpose
Resistors in Parallel: General Formula & LCM Method
Resistors in Parallel: Two Resistors Shortcut
Solving Combination Circuits
The Student and Tutor commenced a new focus on digital systems, moving away from programming. They reviewed digital system concepts, including number systems and detailed explanations of various logic gates (AND, OR, NOT, NAND, NOR, XOR, XNOR). The session involved the Student practicing deriving and simplifying Boolean equations from circuit diagrams, and the Tutor reviewed fundamental Boolean algebra laws. The Student will practice more circuit and equation reduction problems for homework, and the Tutor requested a past paper for tailored future sessions.
Introduction to Karnaugh Maps (K-Maps)
Logic Circuit Analysis: Equation Derivation
Boolean Algebra Laws for Logic Minimization
Digital Logic Gates & Truth Tables
Student and Tutor reviewed various supervised machine learning models, including logistic regression, decision trees, and SVMs. The Tutor then introduced deep learning, covering the fundamentals of neural networks, different architectures like CNNs and LSTMs, and the evolution to Large Language Models (LLMs) and Agentic AI. They explored tools like Groq and Flowise for building and testing AI agents, and the Tutor committed to sharing reference materials for further study on deep learning and agents.
Specialized Deep Learning Architectures: CNN
RNN
LSTM
From Transformers to Large Language Models (LLMs) & Agentic AI
LLM Ecosystem
Tools & Limitations
Artificial Neural Network (ANN) Architecture & Training
Introduction to Deep Learning & Neural Networks
The Student and Tutor reviewed and practiced problems related to RC circuits, focusing on discharging calculations for capacitance, charge, and current over time. The Tutor then introduced the fundamental concepts of RL circuits in a DC context, highlighting the time constant and distinguishing them from AC applications. The next session is planned to cover AC RL, RC, and RLC circuits, including phasor diagrams.
Time Constants (τ) in RC and RL Circuits
Distinguishing DC and AC Circuit Analysis
RL Circuit Characteristics and DC Operation
RC Circuit Discharging Analysis
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