facebook

Discover the Best Private Computer programming Classes in Saint‑Gilles

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

search-teacher-icon

Find Your Perfect Teacher

Explore our selection of Computer programming tutors & teachers in Saint‑Gilles and use the filters to find the class that best fits your needs.

chat-icon

Contact Teachers for Free

Share your goals and preferences with teachers and choose the Computer programming class that suits you best.

calendar-icon

Book Your First Lesson

Arrange the time and place for your first class together. Once your teacher confirms the appointment, you can be confident you are ready to start!

44 computer programming teachers in Saint‑Gilles

0 teachers in my wish list
|
+

44 computer programming teachers in Saint‑Gilles

Math · Computer programming
Science · Computer programming · Math
Math · Computer programming
Trusted teacher: Computers are very powerful machines, that do incredible work. But how do they work? How can it tell the difference between a 1 and a 7? How can it tell the difference between reading a Word document and a PowerPoint? If you understand programmatic theory, then you can write in any programming language. This class is designed to unravel the mysteries surrounding these machines. Ranging from programming theory (what is a pointer?) to actual computational challenges (what is the correct data structure to use based on the Big O requirements?) No specific language is covered in this course, it will be purely theoretical knowledge and 'pseudocode'. The topics covered are listed below Programmatic Theory: Literals, Operators, Keywords Variables, Data Types, Generics Memory Usage, Pointers, Arrays Branching, Iteration, Iterators Functions, Control Flow, Scope, Closures, Enumerations, OOP Classes and Instances, Immutability, Inheritance Polymorphism, Exception Handling, Composition Asynchronous Programming, Multithreading, Multiprocessing Internal Workings: Binary, Hexadecimal, Number Systems Extrinsic vs Intrinsic Data, Coupling, MVC Architecture CPU, FED Cycle, Spatial Locality Caching, Performance, Memory Types Storing Complex Data (Text), Storing Complex Data (Sound), Storing Complex Data (Images) Data Structures and Algorithms: Performance, Big O, Measuring Performance Searching, Sorting, Decomposition Data Structures, Arrays, Linked Lists Single Buffers (Pools), Double Buffers, Ring Buffers Stacks, Queues, Priority Queues Heaps, Hash Tables, Graphs Trees, Binary Trees, Vectors
Computer science · Computer programming
Trusted teacher: Master in Computer Science from the State University of Campinas (Brazil) and university professor in Peru. He has participated in the most important Artificial Intelligence conferences including ACL, NeurIPS, ICML, ICLR, KDD, ICCV and CVPR, summer schools such as Machine Learning (MLSS), Deep Learning (DLRL) and Probabilistic ML (ProbAI). He has also participated in various programming contests and has experience preparing interviews for applications to companies such as Google, Meta, Microsoft, among others. He has extensive experience in the areas of Machine Learning and Deep Learning applied mainly to computer vision and natural language processing. He has experience in teaching, providing illustrative explanations for a better understanding of both the theoretical and practical parts. Some examples of presentations given: He has also advised students from different countries in their graduation and master's theses, providing them with a theoretical and practical base with examples that they can then use to continue their development. Some of the things I can help you with: - Machine Learning: Linear regression, logistic regression, regularization, LDA, QDA, SVMs, decision trees, random forest, boosting, PCA, clustering (K-means, DBSCAN, hierarchical, GMM), neural networks, model selection, metrics evaluation, MLE, Bayesian learning, data preprocessing, etc. - Deep Learning: Multilayer Perceptron (MLP), backpropagation, activation functions, multiclass classification, optimizers (SGD, Adam, RMSProp, etc.), CNNs, architectures (ResNet, DenseNet, EfficientNet, Siamese, etc.), RNNs, LSTMs, Seq2seq, Attention, Transformers (BERT, GPT, ViT, etc.), autoencoders, generative models (VAE, GAN, Diffusion, etc.), etc. - Languages: Python, C++ - Libraries and frameworks: PyTorch, Tensorflow, Keras, Huggingface, numpy, pandas, scikit-learn, sympy, etc.
Computer programming · Python
Showing results 151 - 175 of 1424151 - 175 of 1424
map iconMap