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Find the Best Online Python Tutors & Teachers for Private Lessons

For over a decade, our private Python tutors have been helping learners improve and fulfil their ambitions. With one-on-one lessons online, you’ll enjoy high-quality, personalised teaching that’s tailored to your goals, availability, and learning style.

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526 online python teachers

Ali

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United Kingdom
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5.0

2 reviews

(2)

$28

60-min

/h

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1Students

Friendly Python, C & C++ Tutor — GCSE, A-Level and UniversityTranslate this text using Google Translate.

Friendly Python, C & C++ Tutor — GCSE, A-Level and UniversityTranslate this text using Google Translate.

I offer one-to-one Programming tuition in Python, C, and C++, for GCSE Computer Science, A-Level Computing, and university students studying engineering, computer science, or related subjects. Lessons are available online or in person around Birmingham. What I cover: Python for beginners and intermediate learners C and C++ programming GCSE and A-Level Computer Science (all exam boards) University coursework support, debugging help, and project guidance Core concepts: variables, loops, functions, data structures, object-oriented programming, file handling, basic algorithms How I teach: I start by understanding exactly where you are — whether that's "I've never coded before" or "I'm stuck on a specific assignment." Then I build lessons around small, practical examples you can actually run and modify yourself. I'm patient with errors (everyone gets them), and I make sure you understand the why behind the code, not just how to copy it. For university students, I can also help with debugging, code reviews, and explaining tricky concepts in plain English. If you or your child is preparing for exams, working on coursework, or just wants to finally feel comfortable with coding, I'd love to help.

Jude

United Kingdom
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Recently active
$40

60-min

/h

UK Financial Data Scientist teaching Data Science and Machine Learning with Python through clear explanations, visual examples and practicalTranslate this text using Google Translate.

UK Financial Data Scientist teaching Data Science and Machine Learning with Python through clear explanations, visual examples and practicalTranslate this text using Google Translate.

My lessons are designed to take you from simply following code to genuinely understanding how data science works. We can cover the complete data science process, including data cleaning, exploratory data analysis, feature engineering, visualisation, statistics, machine learning, model evaluation and communicating results. Depending on your goals, lessons can include: Python, pandas, NumPy and scikit-learn Data cleaning and exploratory analysis Regression and classification Decision trees, random forests and boosting Clustering and dimensionality reduction Cross-validation and model evaluation Feature engineering and model interpretation Neural networks and deep learning foundations Bayesian modelling and PyMC Portfolio and interview preparation Support understanding university modules and projects I use diagrams, analogies and practical demonstrations to make difficult ideas easier to understand. We will normally begin with an intuitive explanation, look at the underlying logic or mathematics, and then implement the concept in Python. Lessons are personalised around your level. Complete beginners receive a structured learning path, while experienced students can focus on advanced topics, project guidance, debugging or interview preparation. You will be encouraged to explain ideas back to me, interpret results and make your own modelling decisions. My goal is not only to help you produce working code, but to help you become an independent and confident data scientist.

Nuria

Spain
$35

60-min

/h

Applied Artificial Intelligence classes | Python, Generative AI, LLM and AutomationTranslate this text using Google Translate.

Applied Artificial Intelligence classes | Python, Generative AI, LLM and AutomationTranslate this text using Google Translate.

Do you want to learn Artificial Intelligence from scratch or do you need support with a subject, practice or project related to AI? The classes are online, one-on-one, and fully tailored to your level and goals. We can work from the fundamentals to practical applications using Python, generative AI tools, language models, and APIs. We can work on content such as: fundamentals of Artificial Intelligence; Python applied to AI and data processing; data preparation, cleaning and analysis; NumPy, pandas and data visualization; Introduction to Machine Learning; classification, regression and model evaluation; Generative AI and Language Models (LLM); use of ChatGPT, Gemini and other AI tools; design and improvement of prompts; consumption of AI model APIs; task automation using AI; AI integration in applications; search and work with information and documents; development of small projects and prototypes; internships, projects and exam preparation. The goal is not only to learn how to use AI tools, but to understand how they work, when to use them, and how to practically integrate them into your own projects. We can start from scratch, work on the syllabus of your subject, or develop a specific application or project step by step. In addition to the classes, you will have access to our educational platform with its own documentation, exercises, examples, practices and other resources to continue working between sessions. Additional information for the student You can bring your own syllabus, practical exercises, data, or project. We will adapt the classes to your prior knowledge and the objective you want to achieve.

Nikita

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India
$15

60-min

/h

Python, SQL, Excel, Data Analytics, Data ScienceTranslate this text using Google Translate.

Python, SQL, Excel, Data Analytics, Data ScienceTranslate this text using Google Translate.

Are you looking to learn Python programming from scratch or improve your coding skills for college, job interviews, or a career in Data Analytics and Artificial Intelligence? You're in the right place! In this course, I provide personalized one-to-one online lessons designed for beginners, students, and working professionals. My teaching approach is practical and interactive, focusing on real-world applications rather than just theory. Every concept is explained in a simple, easy-to-understand way with plenty of hands-on practice. ### What you'll learn: * Python Programming from Beginner to Intermediate * Problem Solving and Coding Logic * Object-Oriented Programming (OOP) * File Handling and Exception Handling * SQL and Database Basics * Microsoft Excel for Data Analysis * Power BI Dashboard Development * Data Analytics Fundamentals * Introduction to Machine Learning and Artificial Intelligence * Interview Preparation and Project Guidance ### Why learn with me? * Personalized lessons based on your goals and current skill level * Step-by-step explanations with practical coding exercises * Real-world projects to build confidence and strengthen your portfolio * Help with assignments, academic projects, interview preparation, and career guidance * Friendly, patient, and supportive learning environment with regular doubt-solving sessions Whether you're preparing for exams, starting your programming journey, or aiming to build a career in Data Analytics, I'll help you gain the practical skills and confidence you need. Book a lesson today, and let's start learning together!

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Ammar

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Canada
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Recently active
$24

60-min

/h

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1Students

Master AI, Machine Learning, Data Science, Python & Programming with a PhD Engineer and Professor | 25+ Years’ Expertise | All levelsTranslate this text using Google Translate.

Master AI, Machine Learning, Data Science, Python & Programming with a PhD Engineer and Professor | 25+ Years’ Expertise | All levelsTranslate this text using Google Translate.

A- TOPICS YOU CAN EXPLORE AND MASTER: 1- PYTHON FOUNDATIONS • Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming • Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code • Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management 2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS • Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design • Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use • Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies • Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges 3- DATA PREPARATION AND EXPLORATION • NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data • Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation • Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation 4- MATHEMATICAL FOUNDATIONS • Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics • Loss functions, gradients, distance measures, regularization, likelihood, and model complexity • Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms 5- SUPERVISED MACHINE LEARNING • Linear and polynomial regression, logistic regression, and regularized models • k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers • Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results 6- UNSUPERVISED LEARNING • Clustering using k-means, hierarchical clustering, and density-based methods • Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery • Method selection, evaluation of data structure, and interpretation of results without predefined labels 7- MODEL EVALUATION AND IMPROVEMENT • Training, validation, and test sets; cross-validation; hyperparameter optimization • Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2 • Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization 8- DEEP LEARNING • Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent • Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations • TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment 9- ARTIFICIAL INTELLIGENCE APPLICATIONS • Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models • Computer vision, image classification, fundamental principles of object detection, and image preprocessing • Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications 10- GENERATIVE AI AND LARGE LANGUAGE MODELS • Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation • Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant • Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation 11- TOOLS AND LIBRARIES • Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch • Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project • Additional libraries may be introduced depending on the selected specialization and dataset 12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION • Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results • University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications • Code review, debugging, documentation, reproducibility, model comparison, and communication of results ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- B- PERSONALIZED TUTORING: LEARNING HOW TO REASON Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected. My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly. Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan. The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach. A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps. You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level. My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.

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Our students evaluate their Python teacher.

To ensure the quality of our Python teachers, we ask our students to review them.

Only reviews of students are published and they are guaranteed by Apprentus. Rated 4.9 out of 5 based on 216 reviews.

Baia was instrumental in helping my daughter prepare for the OMPT-F exam. From the very first lesson, she was organized, knowledgeable, and focused on the areas that mattered most for success on the test. What sets Baia apart is her ability to explain complex mathematical concepts in a simple, structured way while building confidence at the same time. Her engineering background gives her a deep understanding of mathematics and allows her to explain not only how to solve problems, but also why the concepts work. She provided targeted practice materials, mock exams, and clear guidance on the key topics that carried the highest impact. Baia was always responsive to questions between lessons and consistently went above and beyond to ensure my daughter was fully prepared. Thanks to her support, my daughter developed a much stronger understanding of mathematics and a more positive attitude toward the subject. She now approaches challenging problems with far more confidence than before. I highly recommend Baia to anyone preparing for the OMPT exams, university mathematics, or looking for a patient, knowledgeable, and highly effective math tutor.

Ghous is a very kind and pleasant teacher. He explains everything clearly and is always responsive and supportive. He adapts to the student’s pace and is very flexible when it comes to scheduling and learning preferences. He has a strong command of Electrical Engineering topics and is able to explain even complex concepts in a simple and understandable way. Ghous is also very helpful with assignments, even when they are in a different language, which shows both his deep subject knowledge and his adaptability. I’m very satisfied with his lessons and would definitely recommend him to others.

Highly recommended teacher!!! Matias teaching methods are great. Very clear and concise. Doesn’t waste your time explaining meaningless background information and always lectures with the intent to help you understand the material. He’s helped me understand content for my master course on Python and is one of the best lecturers that I’ve had. Your passion and dedication is beyond words! Thank you for getting me through this hard quick semester, I honestly would have never passed if it was not for your help! Thank you so much once again!

To ensure the quality of our Python teachers, we ask our students to review them.

Only reviews of students are published and they are guaranteed by Apprentus. Rated 4.9 out of 5 based on 216 reviews.

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