Python tutor near me in the USA
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Python tutoring from beginner to advanced
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Sonali taught 10 days ago
The class covered univariate and bivariate data analysis techniques in Python using libraries like Seaborn. The student practiced creating and interpreting various plots, including histograms, box plots, bar plots, and heatmaps, to understand data distributions and relationships. Data preprocessing steps, such as outlier detection and feature selection, were also discussed to prepare for model building in the next session.
Univariate Analysis with Histograms and Box Plots
Labeled Bar Plots for Categorical Data
Bivariate Analysis and Stacked Bar Plots
Heatmaps for Correlation Analysis
Identifying and Handling Outliers with Quantiles
Steven taught 10 days ago
The student and tutor reviewed the steps of the data life cycle to build a regression model, focusing on feature engineering, data scaling, and train-test splitting. They analyzed correlation matrices and discussed the impact of data standardization. The student plans to complete the assignment, incorporating the discussed techniques and insights.
Data Life Cycle
Outlier Handling
Feature Scaling (Standardization)
Data Splitting: Train
Test
Importance of Context in Data Interpretation
Random Seed for Reproducibility
Sonali taught 20 days ago
The Student and Tutor discussed the t-SNE method for dimensionality reduction and then completed a graded quiz on K-means clustering. They reviewed the importance of scaling data and the interpretation of elbow plots in determining the optimal number of clusters. The session concluded with the Tutor introducing a coding example that implemented t-SNE, which will be continued in a future session.
T-distributed Stochastic Neighbor Embedding (t-SNE)
Dimensionality Reduction
The Elbow Method
Silhouette Score
Scaling Variables in K-Means
K-Means Clustering Algorithm
Data Analysis Workflow for Machine Learning Models
Sonali taught 21 days ago
The session reviewed the concept of clustering as an unsupervised machine learning technique, focusing on how data points are grouped based on similarity and distance measures. The student and tutor discussed the differences between unsupervised and supervised learning. The tutor went through the student's notes on the topic.
Unsupervised Learning
Clustering Defined
No Predefined Categories
Similarity and Distance Measures
Cluster ID
Sonali taught 27 days ago
The Student and Tutor reviewed decision trees and methods to prevent overfitting, including pre-pruning and post-pruning. The Student took a quiz on the material, and the Tutor identified areas for future focus, such as Gini impurity and the steps involved in building decision trees. The next session will cover pruning.
Decision Tree Target Variables
Objective of Decision Trees: Gini Impurity
Overfitting and Pruning
Building a Decision Tree: Steps
Determining the Best Split
Minimum Samples Leaf Hyperparameter
Differences: Classification vs. Regression Trees
Sonali taught 28 days ago
The Tutor and Student reviewed key concepts related to decision trees and model evaluation metrics, working through practice quizzes to solidify understanding. They focused on interpreting metrics like precision, recall, and Gini impurity, and understanding how to apply these concepts in machine learning model evaluation. The Student will continue practicing with these concepts in future sessions.
Drug Response Prediction
Credit Card Approval Prediction
Box Plot and Median
Scatter Plot and Correlation
Decision Tree Root Node
Leaf Node and Class Proportion
Impurity Measures (Gini and Entropy)
Decision Tree Splitting
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Python class statistics in the U.S.
Total Python tutors
394 Python tutors available
Expert Python tutors
76% hold a Master’s degree
Python Industry experience
84% of tutors have worked in software development
Everything you need to know about Python
All about learning python
Python, used by 50% of developers worldwide, is a high-level, object-oriented programming language known for its readability and simplicity. It powers web frameworks (Django, Flask), data science (Pandas, NumPy), machine learning (TensorFlow, PyTorch), and IoT applications (MicroPython). With its versatility, python developers earn an average of $120,000 per year in the U.S. (Indeed).
You can learn python through self-study, coding boot camps, online courses, or tutoring. While self-study could allow learning at your own pace, a private tutor can help in fast and structured learning, making it ideal for those who want to master Python faster.
However, challenges like debugging errors, grasping complex libraries, and applying concepts can slow progress. Most learners take 3 to 6 months to gain proficiency, but real expertise comes with practice.
With python’s growing demand in AI, fintech, and cybersecurity, mastering it is a smart investment. Whether through self-learning or a tutor, consistency and hands-on practice are key to success.
How can a tutor help you in learning python?
Assignment help & exam prep
Ever struggled with a tough python assignment? A tutor can pinpoint challenges and guide you through recursive functions, debugging loops, or data structures.
Beyond assignments, they identify concept gaps and fill them with practical coding exercises in file handling, SQL, or Matplotlib. This hands-on approach boosts exam performance and builds confidence for future tasks.
Debugging & error resolution
Ever spent hours stuck on a bug? A tutor can walk you through debugging, explain error messages, and show you how to write cleaner, more efficient code. With personalized guidance, you’ll write cleaner, more efficient, and Pythonic code that’s easier to understand and maintain.
Hands-on learning & real projects
A tutor can guide you through real-world python applications—building Django web apps, automating tasks with Selenium, or writing REST APIs with Flask. This hands-on approach gives you the skills to excel in python development!
What to look for in a python tutor?
Experience and expertise
Look for a python tutor with a strong background in fields like data science, machine learning, or web development.
A Python tutor with experience in real-life projects, like building machine learning models, creating web apps with Flask or Django, or analyzing data with pandas, can offer you valuable insights and improve your learning.
Teaching skills and practical knowledge
Python tutors who use real-life examples can help you understand topics better. For instance, if you're building projects like GUI applications in Tkinter or customer segmentation models in data science, a tutor integrates real-world examples to explain concepts, making complex topics easier to grasp.
Cost
The cost of python tutors online ranges between $30 to $200 per hour, depending on factors like the tutor's experience, location, and the platform used. Beginner tutors or group sessions are usually cheaper, while experienced professionals or one-on-one sessions cost more.
Wiingy offers 1-on-1 python tutoring at an affordable rate of 28$ per session.
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