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Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance. Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one. What we can cover depending on your goals: Python fundamentals: variables, loops, functions, data structures - Object-oriented programming (OOP) - Data manipulation with pandas and NumPy - Introduction to machine learning with scikit-learn - Database management with SQL - C and Java upon request - MATLAB and R available for engineering/science students Why learn with me? I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now. Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.

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Hello, I'm doing a PhD in AI and ML using Python and am an Oracle-certified trainer with 350+ reviews and ratings [with proof attached], I will be able to teach you Python better than any of my competition. Why choose me? 1. 300 + reviews and ratings 2. Certified tutor 3. More than 5 years of teaching experience 4. Worked as a Software engineer in companies like Virtusa Corp and DIGIDEZ DIGITAL SYSTEMS 5. Hold B.tech and M.tech in Computer Science Featured Review : Been trying to learn Java on my own for about 1 year and I couldn't get a grasp on it. Aniket make learning Java a fun experience and challenges you to think for yourself to reinforce the concepts you've learned. I am truly excited for our meetings and he makes time go by so fast that I'm upset when they end. Great teacher and he is genuinely passionate about your success. If I could give him more stars I would!!! Thanks Aniket

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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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Personalized support for students in their academic difficulties Design and delivery of courses adapted to the needs of learners Monitoring and evaluating progress to ensure continuous improvement Encouragement of an interactive and motivating educational approach

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Having graduated with a master's degree in industrial engineering, with a major in computer science at Polytechnique Montréal, I would like to give math and/or computer science courses to students in a university program, at CEGEP or at secondary school. During my studies at Polytechnique Montréal, I gave classroom lessons, practical work (around 50 people), as well as mathematics reinforcement for all types of profiles (individual help). I also have previous private tutoring experience. It is always a real pleasure for me to witness the success of the students and to see their progress session after session. I insist on stimulating students' thinking so that they are as effective as possible during their exams. It would be a pleasure to have a first meeting!

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Double-degree student at ÉTS Montreal/INSA Rennes (France) in aerospace/computer-electronics, I studied for 4 years in France subjects such as maths, physics, as well as computer science and electronics. I am willing to give lessons at all levels, preferably by videoconference. In France, I gave maths/physics lessons at all levels. I can also speak in English

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I give science lessons for all levels. I have already given courses in an agency (Completeness) from the 6th to post-baccalaureate. I am currently a master's student in information technology at ETS and McGill. I think I am close to young people and I have a good pedagogy. I also regularly help my brother and my sister who are respectively in 4th and 1st. As part of my lessons, I ask the student in advance what concept he needs to work on. Then, I start the lesson by asking the student what he learned from his lesson, what he understood, what he didn't understand. Then, we resume the course together and then we move on to the exercises. At first we redo the exercises already done, then I invent similar exercises. I can teach at home or in my apartment.

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I offer math or computer courses up to the Bachelor's, combining rigor and enthusiasm. I am doing a master's degree in mathematics at the University of Paris-Saclay (#1 in the world in mathematics). Being the eldest of a family of three children, I have a sense of pedagogy

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Photography, video, manipulation of images and sounds of all kinds, robotics, circuit bending, programmed art, electronic music or web art ... these workshops will allow young and old to become familiar with the technologies that we surround while creatively exploring our relationship to the world. Adapted to the desires and needs of all, they can take different forms to match the personalities, favorite mediums, themes and interests of each.es (creation of synthesizers, welding, introduction to arduino, programming, javascript) , photoshop, audio and video recording, multidisciplinary creative workshops ...) Graduate of the National School of Arts and Crafts in Paris in multimedia graphic design and a Bachelor of Visual Arts and Media at UQAM Marion works in the emerging digital art circuit. She presents her work in several venues in Montreal and is involved with Perte de Signal and the Eastern Bloc on an ad hoc basis. His committed and conceptual work revolves around poetics and feeds on themes such as the environment, nature, gender, data or even virtuality.

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Do you want to learn Office 365, SharePoint, Flow, PowerBI, PowerApps or Advanced Excel? I am an Experienced computer science engineer and expert who can give you great tips to become rapidly effective. Do you want to learn Office 365, SharePoint, Flow, PowerBI, PowerApps or advanced Excel? I am an experienced computer engineer who can give you great tips and methods to become efficient quickly.

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Hello, I am currently a tutor at the University of Sherbrooke and I am in Montreal on weekends. After having followed a French training in preparatory class, then having studied at the Ecole Normale Supérieur of Paris-Saclay, I am currently doing an internship at the computer laboratory of the University of Sherbrooke. I have the knowledge required to support a student who would like to train in mathematics or computer science. Do not hesitate to contact me by message for more information. I give lessons at my studio but can also move around.

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I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work. Topics include: Python fundamentals through a finance lens (data structures, functions, control flow). Working with financial data and datasets. Performance benchmarking and writing efficient code. Applying concepts from Hilpisch's Python for Finance.

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Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance. Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one. What we can cover depending on your goals: Python fundamentals: variables, loops, functions, data structures - Object-oriented programming (OOP) - Data manipulation with pandas and NumPy - Introduction to machine learning with scikit-learn - Database management with SQL - C and Java upon request - MATLAB and R available for engineering/science students Why learn with me? I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now. Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.

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6Students

If you’ve ever felt that science and math are difficult, it’s probably because no one showed you how to think like a problem solver. In my classes, you’ll learn not just formulas or code but how to truly understand concepts, apply them, and build strong logical intuition. I teach: • 🔢 Mathematics: From algebra and calculus to applied problem-solving for real-world use. • 💻 Computer Science: Coding fundamentals (Python, C++), algorithms, and logical thinking for beginners and intermediate learners. • ⚛️ Physics: Mechanics, thermodynamics, and practical examples that make abstract ideas simple and visual. As a Software Engineer and Master’s student in Engineering at Nagoya University, I bring both academic knowledge and hands-on experience from real projects. My teaching approach is interactive, visual, and deeply focused on understanding over memorization. Let’s turn complex problems into clear, step-by-step insights — and make learning something you genuinely enjoy.

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1Students

I teach machine learning, AI and Python online to university students, postgraduates, career changers and serious beginners across the UK and Europe. Lessons are in English. I hold an MSc in Electronics and Electrical Engineering with Distinction and I teach as a Visiting Lecturer on a Master's level module covering data analytics, machine learning and generative AI at a UK university. I also have two accepted international conference papers on deep learning for image classification. I set and mark postgraduate assignments myself, so I know where marks are won and lost on this kind of work. Who this is for Undergraduates and postgraduates on AI, ML, data science or computer science modules at any European university. Final year, Master's and thesis students working on a machine learning project. IB and A Level students moving into computing or engineering. Professionals retraining for data roles. Complete beginners who want to learn Python properly rather than copying it from videos. I work with students on UK, IB and continental European programmes. I have tutored engineering students in Germany and international school students across several countries, so an unfamiliar syllabus or a module taught in a different structure is not a problem. Send me the material and I will work from it. What we cover Python for data science with NumPy, Pandas, Matplotlib and scikit-learn. Deep learning using TensorFlow and Keras. Core theory including regression, classification, clustering, decision trees, random forests, neural networks and CNNs, together with the linear algebra, calculus and statistics underneath them. Computer vision and image classification, which is my published research area. Model evaluation, overfitting and hyperparameter tuning. Writing machine learning work up to academic standard, covering methodology, results and critical evaluation. How lessons work Send me your module handbook, assignment brief, thesis spec or the code that will not run, and I plan the session around it before we meet. Nothing generic. In the lesson I explain the concept with a worked example, then you take the keyboard while I watch and correct, because you learn far more doing it than watching me do it. You finish with annotated notes and a clear next step, and you can message me between sessions with questions. Practical details Online over Google Meet or Zoom with screen sharing and a shared whiteboard. Sessions run 60 or 90 minutes. I am based in the UK and teach across GMT and Central European time, with evening and weekend slots that suit students anywhere in Europe. Tell me your course, your deadline and exactly where you are stuck, and I will come back with a plan for the first session.