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For over a decade, our private Computer Science 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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1340 online computer science teachers

Trusted teacher: Python programming. Here's a general outline of what such a tutorial might cover: Introduction to Python: Overview of Python, its history, and its uses. Comparison with other programming languages. Setting up the Python environment and tools for development. Basic Python Syntax: Understanding of Python syntax, keywords, and data types. Introduction to variables, operators, and writing simple Python scripts. Control Structures: Detailed explanations and examples of control structures including if-else statements, for and while loops, and comprehensions. Introduction to error and exceptions handling. Functions and Modules: How to define and call functions, pass arguments, return values, variable scope, and lambda functions. Understanding and creating modules and packages. Data Structures: In-depth look at Python's built-in data structures: strings, lists, tuples, sets, and dictionaries. Operations, methods, and using these structures effectively. Object-Oriented Programming (OOP): Basics of OOP in Python, creating classes and objects, inheritance, polymorphism, encapsulation, and method overloading. File Handling and I/O: Reading from and writing to files, handling file paths, and understanding various file formats. Introduction to I/O operations in Python. Libraries and Frameworks: Overview of popular Python libraries and frameworks like NumPy, Pandas, Matplotlib, Django, and Flask. Examples of how to use these libraries for data manipulation, visualization, web development, etc. Error Handling and Debugging: Techniques for debugging Python code, handling exceptions, and using debugging tools. Advanced Topics: Introduction to more advanced topics like threading, networking, database interaction, and web scraping. Best Practices: Writing clean, readable, and efficient code. Understanding Pythonic concepts and following coding standards. Projects and Practical Applications: Step-by-step guides to building real-world applications or projects to apply the learned concepts in practical scenarios. Each section would contain explanations, code examples, and exercises to help reinforce the learning.
Tutoring · Computer science · Math
Trusted teacher: 🚀 Discover IT with an Expert! 🚀 👨‍💻 Are you looking to become computer literate, learn to code, or strengthen your digital skills? Stop looking around, you're in the right place ! 👩‍💻 🌟 About me: I am an experienced IT engineer, with more than 6 years of experience in large companies, innovative startups and as a freelancer. Currently, I work at the heart of a French unicorn, where innovation is our daily life. My goal is to guide you through the exciting world of computing, no matter your age or level. Can be done in person or by video. 💡 What you can expect from my courses: Lessons adapted to your level, from beginner to advanced. Personalized courses to meet your needs and goals. Hands-on projects for a real-world learning experience. Solutions to your IT problems, whether you are facing blockages or want to deepen your knowledge. A constant update on the latest trends and cutting-edge technologies. 📅 Flexibility: I adapt to your schedule. Choose in-person or online classes, whichever works best for you. 💼 Why learn computer science? Computer science is the key skill of the 21st century. Whether you're looking to enhance your career, create your own startup, or explore a personal passion, IT is the tool to help you achieve your goals. 🤝 Contact: Ready to dive into the world of IT? Contact me today to discuss your specific needs and goals. Together, we will make you an IT expert! Don't wait, take the first step towards an exciting IT adventure! 💻🌐
Computer science · Computer programming · Computer basics
Math · Physics · Computer science
Trusted teacher: Student in 4th year of ENS Ulm in mathematics as well as gluer in PSI* and MP at Lycée Louis-le-Grand, and in PCSI and MP at Lycée Pierre-Gilles-de-Gennes, I put at your disposal my knowledge and my experience from my career. Course location: The courses take place by videoconference, using the Teams software. I use: - my Mac computer (microphone and webcam working) - a Samsung graphic tablet in the format of an A4 sheet allowing me to write the course that I give to the student so that he/she keeps a written record and that I send systematically at the end of the course. The videoconference format offers several advantages: - more availability for me and my student by operating remotely - no risk linked to transport (delay) - possibility to record the course and the explanations - shared folder allowing me to send interesting documents to the student in real time. Course of a lesson: The course begins with a discussion with the student to identify and prioritize the points he wishes to work on. During the session, two formats are possible: - "short lesson" for the student, rereading of his lesson, explanation of definitions and theorems, resumption of demonstrations by highlighting the most important proofs and clarifying the intuitions behind these proofs. Simple but striking examples of my invention make it possible to illustrate and reinforce the memorization of the lesson by the student; - resolution of new exercises, chosen by the student or that I suggest to him. During the time of reflection, I prepare the possible solutions and especially I observe its strong points and its difficulties in the resolution of the problem. If he has difficulties I give him indications and not the complete solution. So I avoid simply giving an answer key that the student will end up "learning by heart". If he finally wants a correction of the exercise, I take care to explain to him the whole process of the solution, even if it means repeating steps that are not clear, and I give a written record of the solution to the student. Within a course, it is of course natural to go back and forth between these two formats. At the end of the session, I discuss with him the points he can improve and make personalized suggestions. The student can ask me for exercises or send me his course documents to prepare for the next session. What I propose: - courses at all levels in mathematics, physics-chemistry and computer science; - preparation for competitions at all levels (X, ENS, Centrale, Mines-Ponts, CCINP or e3a // HEC, ESSEC, ESCP and other business schools); -preparation for the requirements of the preparation from high school ("Prépa à la Prépa" format). Preparation for the Terminale general competition (I obtained a mention there thanks to an independent preparation), for the National Mathematics Olympiads, for the Animath Cup ... I am proficient in Python, OCaml, C and SQL computer languages. Do not hesitate to contact me if you need any information!
Math · Physics · Computer science
Trusted teacher: Hello, I am an experienced machine learning teacher with 5 years of expertise in teaching this discipline at all levels. My expertise using Python and R allows me to teach different machine learning algorithms such as neural networks, decision trees and clustering algorithms. I am also experienced in using popular Python and R libraries such as TensorFlow, Keras, Scikit-learn and ggplot2. In addition to my machine learning skills, I am able to help students read and understand research papers for their presentations, as well as work on projects in Python and R. My commitment to machine learning is passionate and I enjoy sharing my knowledge with my students. If you are interested in my services as a machine learning teacher for all levels, do not hesitate to contact me. In addition to my machine learning skills, I am also able to help you with mathematics, statistics and dissertation writing. I am available to teach the following subjects: 1.Python or R 2. Data exploration 3.Machine learning 3.1. Intro ML 3.2. Linear Model -> Linear Models for Regression and Classification 3.3. kernel -> Kernelization 3.4. Model selection 3.5. model set, -> Bagging / RandomForest, Boosting (XGBoost, LightGBM,...) , Stacking 3.6. Data preprocessing -> Data pre-processing -> Pipelines: choose the right preprocessing steps and models in your pipeline -> Cross validation 3.7. Neural Networks -> Neural architectures -> Training neural nets: Forward pass: Tensor operations and Backward pass: Backpropagation -> Neural network design: Activation functions, weight initialization and Optimizers -> Neural networks in practice: Model selection, Early stopping, Memorization capacity and information bottleneck, L1/L2 regularization, Dropout, Batch normalization 3.8. Convolutional Neural Networks -> Convolved Image -> Convolutional neural networks ->Data increase -> Model interpretation -> Using pre-trained networks (transfer learning) 3.9. Neural Networks for text -> Bag of word representations, Word embeddings, Word2Vec, FastText, GloVe
Math · Statistics · Computer science
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