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Discover the Best Private Computer Science Classes in Sn Klw

For over a decade, our private Computer Science tutors have been helping learners improve and fulfil their ambitions. With one-on-one lessons at home or in Sn Klw, you’ll benefit from high-quality, personalised teaching that’s tailored to your goals, availability, and learning style.

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28 computer science teachers in Sn Klw

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28 computer science teachers in Sn Klw

Trusted teacher: Young student in License 3 in computer science at the Faculty of Créteil, I propose to give courses in computer office automation, namely Microsoft Word, Microsoft Excel, Microsoft Power Point, and Computer Communication with software Adobe and Microsoft. Office Automation: As for desktop computing, the student will have the basics of applications, if necessary, then it will evolve from an intermediate level to an advanced level with a mastery of office tools for professional use. - With Microsoft Word, the student will be able to layout a document containing images, shapes, tables and graphs; Manage advanced formatting and be able to connect the Word document to a database to manage mailings. - With Microsoft Excel, the student will be able to manipulate the basic functions, namely digital operations, format tables, insert graphics and connect tables, etc. The idea of this course is to change the student from a beginner level to an intermediate level. - With Microsoft Power Point, the student will be able to prepare successful good presentations in design and special effects, use multimedia content in presentations. If the student is strong enough, he will be introduced to the use of the formidable tool, in terms of creation of interactive animation, which is Adobe Flash, to make outstanding presentations. Communication Computer Graphics: For graphic design classes, students will have a good introduction to Adobe Photoshop Tools, Adobe Illustrator, and Microsoft Expression Design, which is relatively similar to Adobe Illustrator, but much easier than Adobe Illustrator. - With Adobe Illustrator and Microsoft Expression Design, students will learn how to use the various tools for creating shapes and objects, learn how to trace and vector images, how to use different color profiles and different special effects, etc. This course is designed to bring a student from a beginner level to an intermediate level (or rather advanced, but not very thorough) - With Adobe Photoshop, the student will learn to use the tools for retouching images and creations, understand the concept of layers and understand how to manage color profiles in the different objects in the workspace. This course is really designed for beginners.
Computer graphics · 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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mathematics tutoring - English - computer science (Paris)
Asma
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