medini bv
Interactive Machine Learning lessons with problem-solving focus
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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
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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 engineering
Assignment help
Business intelligence
Data visualization
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.
Flexible Scheduling
Allows 1h early scheduling
Allows 1h early rescheduling
Can wait for 20 mins after joining
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10 day Refund
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Data Science concepts taught by medini
The Tutor and Student reviewed the concepts and implementation of linear regression models in Python. They covered data preprocessing, model fitting, prediction, and evaluation using libraries like Scikit-learn, and discussed relevant metrics for both regression and classification. The Tutor shared the code for further practice.
Machine Learning Model Types: Regression vs. Classification
Linear Regression: Concepts and Implementation
Data Preprocessing and Feature Engineering
Model Evaluation Metrics: Regression vs. Classification
The session covered the fundamentals of Git and GitHub, focusing on repository creation, branching, and basic commands. The student practiced setting up a local Git repository and pushing it to GitHub. The tutor plans to provide notes and tutorials for further study.
Git Workflow: Working Directory
Staging
Commit
Git Configuration: Username and Email
Connecting Local Repository to Remote (GitHub)
Basic Git Commands
Repositories (Repo) and Branches
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.
Logistic Regression
Machine Learning vs. Deep Learning vs. AI
Supervised
Unsupervised
and Reinforcement Learning
Linear Regression
Decision Trees
Random Forests
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.
Feature Selection: Dropping Irrelevant Columns
Data Analysis: Grouping and Aggregation
Data Transformation: Scaling and Normalization
Data Transformation: Encoding Categorical Variables
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 Pipelining in Data Analysis/Engineering
Exploratory Data Analysis (EDA) Tools
Python Libraries for Data Analysis
Data Cleaning and Preprocessing
Data Transformation
Descriptive Statistics and Data Understanding
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
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