medini bv
Interactive Machine Learning lessons with problem-solving focus
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medini bv
Bachelors degree
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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
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/ lesson
1 lessons
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/ lesson
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
Assignment help
Data engineering
Statistical analysis
Machine learning
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.
Data Science concepts taught by medini
The session focused on machine learning, covering supervised learning algorithms like linear regression, logistic regression, decision trees, and random forests. The student learned about feature engineering, model selection, and deployment strategies. The tutor assigned the student to create a GitHub account and explore uploading projects, with a follow-up lesson planned to implement machine learning models on a dataset.
Linear Regression
GitHub for Version Control and Collaboration
Random Forests
Decision Trees
Logistic Regression
Supervised
Unsupervised
and Reinforcement Learning
The session focused on data preprocessing techniques in Python, including column selection, data transformation using encoding methods, scaling, normalization, and data grouping. The Student practiced implementing these techniques using Pandas and Scikit-learn on a sample dataset. The next steps involve further data transformation, visualization, and potentially implementing machine learning models.
Data Transformation: Encoding Categorical Variables
Feature Selection: Dropping Irrelevant Columns
Data Analysis: Grouping and Aggregation
Data Transformation: Scaling and Normalization
The student and tutor discussed data analysis and engineering using Python, focusing on data cleaning techniques. The student learned to use Pandas and other libraries to handle missing data, remove duplicates, and correct data types in preparation for analysis and machine learning. The next steps include categorical cleansing and statistical analysis.
Data Transformation
Descriptive Statistics and Data Understanding
Data Cleaning and Preprocessing
Python Libraries for Data Analysis
Exploratory Data Analysis (EDA) Tools
Data Pipelining in Data Analysis/Engineering
The session involved debugging line sensor and encoder code for a robot. The student worked on resolving errors in the encoder code and troubleshooting issues with sensor readings. The tutor provided guidance and code modifications to achieve functional sensor readings. The next step is to integrate the sensor code with the motor control code for robot movement, planned for the next session.
Analog vs. Digital Readings
Library Integration and Troubleshooting
Hardware Connections and Pin Assignments
Understanding Sensor Calibration
Identifying and Resolving Code Errors
The Student and Tutor worked on debugging the Student's line-following robot code by adjusting sensor pin configurations and integrating the code with the robot's hardware. They tested and modified code related to line sensor readings, motor control, and calibration routines to improve the robot's performance. The tutor will rewrite the code and share it for testing in the next session, focusing on assessment file requirements.
Line Sensor Pin Configuration
Library Integration and Management
Calibration Process for Line Sensors
Debugging Strategy: Step-by-Step Approach
Motor Speed Configuration
The Student and Tutor discussed Python modules, packages, and libraries, focusing on installing external libraries with pip. The session covered pandas Series and DataFrames, demonstrating their creation and basic indexing. The Student began working on reading an Excel file into pandas and will continue practicing indexing and column name manipulation for homework.
Modules
Libraries
and Packages
PIP - Python Package Index
Pandas: Series vs. DataFrame
Indexing with .loc and .iloc
Reading Data with Pandas
Exploring Data: .head() and .tail()
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