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Since July 2023
Instructor since July 2023
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Data Structures and Algorithms classes, Preparation for Interviews at Google, Microsoft, among others
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From 30 € /h
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Master in Computer Science from the State University of Campinas (Brazil). He has participated in various programming contests and has been a coach of undergraduate teams for the ACM-ICPC contest. Some of the achievements:

- First place in Codesignal in Peru.
- Third place in LeetCode in Peru (+1000 problems solved).
- Top 1% of registered users on InterviewBit.
- More than 1000 problems solved in online judges like UVA, Spoj, HDU, etc.

I have participated in Topcoder, CodeJam and other international competitions that require knowledge of algorithms and data structures. I am the author of the blog "Algorithms and More" where I have written tutorials in Spanish with more than 300 daily views.

I have experience preparing interviews to apply to companies such as Google, Facebook, Microsoft, among others.

The methodology is didactic with a step-by-step explanation of the algorithms and coding. I can also give advice on studies abroad, job opportunities in large technology companies (Google, Microsoft, etc.) and many other opportunities.

If you want to improve your abstraction|logic and make your university projects easy to program, this is your opportunity.

I do not solve exams.

PS: If you are interested in artificial intelligence, you can look for my other ad about it.

Knowledge of C, C++, Python.
Location
location type icon
Online from Peru
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
Spanish
English
Portuguese
Availability of a typical week
(GMT -04:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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.
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---

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As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

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verified badge
This course is designed for beginners who want to get started with programming in Python, one of the most popular and accessible languages today. No previous experience is necessary. In one session, students will learn the basics of programming and will be able to create their first Python applications. With clear explanations, practical exercises, and real-world examples, this course is an ideal gateway to exploring computer science and coding.

Online Course: Introduction to Python - Programming Basics and Simple Applications

Duration :
- 60 minutes: intensive format to learn fundamental concepts.
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---

Educational objectives
At the end of the course, participants will be able to:
1. Understand the basics of programming, including the concepts of variables, data types, conditional structures, and loops.
2. Write and run simple Python scripts.
3. Manipulate inputs and outputs to interact with the user.
4. Solve practical problems with short, functional programs.

---

Course syllabus
1. Introduction (10-15 min)
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- Installation and configuration of a programming environment (IDLE, Visual Studio Code, or Jupyter Notebook).
- First program: *"Hello, World!"*.

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- Variables and data types (integers, strings, lists).
- Mathematical operations and data manipulation.
- Conditional structures (if/else) and loops (for/while).

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- Create a simple application, like a currency converter or password generator.
- Bonus for 90 min format: Fixed a more complex issue involving lists or loops.

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- Summary of the concepts covered.
- Suggestions for resources to continue learning.
- Feedback on the course.

---

Teaching methodology
- Interactive and engaging: the course combines theoretical explanations with practical exercises.
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- Learning by doing: examples and exercises are designed to help you assimilate concepts quickly.

---

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---

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- Interactive sessions adapted to your learning pace.
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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.
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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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* Learning programming will allow students to develop their skills and will certainly allow them to meet the expectations of the future working world and emerging careers.

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Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

Ongoing Support: Get unlimited email support for any questions you have between sessions.

As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

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The course combines theory and practical exercises for effective, practical progress. No prior programming experience is necessary: we'll start with the basics to build solid, usable skills quickly.
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► How do I teach?

►I attach great importance to ensuring that my students truly understand statistical concepts, data analytics methods, machine learning, and AI models, rather than just applying them mechanically – this is how we achieve sustainable success in statistics, data analytics, and modern technologies.

►My success is based on my ability to flexibly adapt my teaching style to the individual needs of students in statistics, data analytics, machine learning and AI, including topics such as regression methods, logistic regression and other machine learning algorithms.

►I use practical, lively examples from statistics, data science and AI to clearly explain abstract concepts such as hypothesis testing, analysis of variance, linear and nonlinear models and to anchor them deeply in the memory.

►With patience and clarity, I break down complex statistical analyses, machine learning models, and data processes into their essential components so that no student is overwhelmed and can build a solid foundation in statistics, data analytics, and AI.

►I am convinced that trusting collaboration is crucial to reducing uncertainty in statistics or data science and creating a productive learning atmosphere.

►I offer intensive exam preparation and project support in statistics, data analytics, machine learning and AI, develop strategies to combat exam anxiety with my students and train them on real data sets and complex algorithms.

►As an experienced online tutor for statistics, data analytics, machine learning, and AI, I use modern tools such as interactive whiteboards and live coding sessions in R to make teaching as efficient and practice-oriented as possible.

►Locations: I teach statistics, data analytics, machine learning, and AI at your home, online, or by appointment – flexible, professional, and tailored precisely to your needs.
verified badge
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In this course, I will guide you step by step according to your level:

Beginner: basics of the language (variables, loops, conditions, functions).

Intermediate: data manipulation (Pandas, NumPy), file management, object-oriented programming.

Advanced: practical projects (data analysis, machine learning, automation, API, web scraping).

My goal is to make learning clear, practical, and motivating. You'll not only learn how to code in Python, but also how to structure your projects and apply your knowledge to real-life scenarios.
verified badge
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Based on the personalized roadmap developed in the first session, the 5-week program delivers structured, project-based learning tailored to your child's cognitive strengths and interests, specifically addressing any learning barriers identified during the diagnostic phase.

What we achieve in 5 weeks:

Week 1-2: Foundations of Machine Learning & Critical Thinking: We move beyond definitions, using interactive projects to understand how AI learns (data, bias, pattern recognition). This builds critical thinking about the technology they use daily.

Week 3-4: Ethical Generative AI for Creativity: Students learn to master prompt engineering to create digital art, stories, or game concepts using generative AI tools. Safety and Ethics are paramount: we focus on responsible usage, digital citizenship, and copyright basics.

Week 5: The Final AI Creator Project: personalized mini-project (e.g., training a simple image classifier or writing a fully co-authored AI story) to demonstrate autonomy and mastery of the core concepts.

As your specialized 10+ year tech educator and coach, I ensure:

SEN Integration: Continuous adaptation of project requirements and delivery methods to ensure students with Special Educational Needs (SEN) maintain confidence and measurable progress.

Skill Transfer: We teach skills that translate directly to school projects, not just AI theory, but advanced digital literacy and structured problem-solving.
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This advanced course builds on programming fundamentals using Python (programming language) and is designed for students who want to deepen their programming knowledge.

The course continues from functions and introduces advanced programming concepts including object-oriented programming principles such as polymorphism, inheritance, abstraction, and encapsulation.

Students will also learn data handling techniques, working with Python libraries, and developing structured programs using complex loops and data collections.

The course covers practical implementation of nested loops, nested lists, tuples, and dictionaries, as well as an introduction to data structures and algorithmic thinking.

Additional topics include graphical user interface development using libraries such as Tkinter, along with introductory concepts in data science and machine learning using Python.

Teaching combines theoretical explanation with real coding exercises to help students develop strong practical programming skills.

• Review of programming fundamentals and functions
• Object-Oriented Programming (OOP) concepts
• Polymorphism, inheritance, abstraction, and encapsulation
• Data structures basics
• Nested loops and complex data handling
• Lists, tuples, and dictionaries
• Introduction to algorithms
• Working with Python libraries
• GUI development using Tkinter
• Introduction to data science and machine learning concepts
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