Sonali Kubde
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Sonali Kubde
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
-
/ lesson
About your coding tutor - Sonali
I am a passionate coding tutor with over 2 years of teaching experience. Armed in masters degree, I have excelled Java and spring boot. My expertise lies in Java, spring boot. I can do the assignments for Java in limited time. I am pretty good with Jenkins and postman which goes hand in hand with Java
Sonali graduated from Mumbai University
Coding tutor specialities
Assignment help
Project help
Exam prep
Homework help
Debugging
Learner types for coding classes
Coding for Adults
Coding for Beginners
Coding for Kids
Coding for College students
Coding for School students
Coding class highlights
My teaching style is clear, adaptive, and goal-focused. I simplify complex ideas into easy-to-understand steps, using real-world examples and analogies when helpful. I adjust the pace to match your learning speed and provide just the right amount of challenge to keep you engaged without feeling overwhelmed. I encourage active thinking and questions, helping you build a strong understanding rather than just memorizing. Whether you prefer visuals, practice problems, or step-by-step explanations, I tailor the approach to suit your learning style and goals. The focus is always on making learning effective, engaging, and empowering for you.
Coding concepts taught by Sonali
The Student revised data analysis techniques using Pandas, focusing on data cleaning and preparation. They worked through a pre-existing notebook on Uber data, converting data types, handling missing values, and extracting date components. The next session will cover Matplotlib and Seaborn for data visualization.
Data Shape vs. Data Insights
Data Type Conversion: Datetime
Feature Engineering: Extracting Date Components
Handling Missing Values (NaNs)
Data Correlation with Heatmaps
The Student and Tutor reviewed cross-validation techniques, including different types like k-fold and stratified cross-validation. They also discussed methods for handling imbalanced datasets such as over-sampling and under-sampling. The Tutor assigned further review of sampling techniques and model tuning in preparation for the upcoming quiz.
SMOTE (Synthetic Minority Over-sampling Technique)
Holdout Validation
Imbalanced Data Handling
Stratified Cross Validation
Leave-One-Out Cross Validation (LOOCV)
Cross Validation
K-Fold Cross Validation
The session involved guided practice on Pandas, focusing on merging, concatenating, and data exploration techniques. The student worked through a worksheet in Google Colab, loading datasets and performing various data analysis tasks. The student was assigned to review the material covered in the session before the next class.
File Path Handling in Pandas
Concatenate vs. Merge
Group By Operations
Inner
Left
Right
and Outer Joins
Understanding `axis` in Pandas Operations
The Student reviewed a data analysis project focused on predicting customer satisfaction. They covered data cleaning, exploratory data analysis with visualizations, linear regression, and Lasso regularization. The Student plans to review the material again before submitting the project.
Linear Regression and Feature Selection
Logistic Regression and Churn Prediction
Lasso Regression with Cross-Validation
Regularization: Lasso Regression
Data Visualization: Summarized vs. Raw Data
Train-Test Split
Data Quality Issues and Handling
Data Preparation: Concatenation and Merging
The Student reviewed advanced Pandas concepts with the Tutor, including merging, concatenation, groupby operations, and pivot tables. The Student practiced these concepts using code examples, manipulating dataframes, and addressing errors. The next session is scheduled to continue reviewing Pandas and data manipulation techniques, including topics in week five.
Removing Rows and Columns
Pivot Tables
GroupBy Operations
Concatenating DataFrames
Merging DataFrames
DataFrame Manipulation: Renaming and Column Operations
Handling Missing Data
The session involved a detailed explanation of the random forest algorithm, differentiating it from the bagging method, and discussing strategies to avoid overfitting. The Student also took a quiz to test their knowledge, achieving a perfect score. The Student and Tutor scheduled another session to work on the code implementation of the random forest algorithm.
Bagging vs. Random Forest: Core Difference
Overfitting in Bagging and Random Forests
Feature Selection and Missing Data
Random Forest Algorithm Steps
Out-of-Bag (OOB) Error
Class Weight in Random Forest
Approach & tools used by coding tutor
Xcode
Bitbucket
Visual Studio Code
Git & GitHub
Postman
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