Tamil Language – Learn Tamil Online with Expert Guidance

From 45 £ /h

I'm Robin Selvam, and I hold an MCA degree. Tamil is my native language, and I am passionate about sharing the richness of this ancient language, one of the oldest in the world. As a friendly teacher, I'm here to support your journey in learning Tamil—covering reading, writing, and speaking. Additionally, I can teach Computer Science and programming as well.

Extra information

The course materials will be sent via email.

Location

Online from India

About Me

I'm Vijaya from Tamil Nadu, India, with a background in D.T.Ed, MA, and B.Ed. in education. With over 2 years of experience teaching Tamil, I have had the pleasure of teaching students from around the world.

As a friendly and enthusiastic teacher, I’m here to support anyone interested in learning Tamil, from beginners to advanced learners. I provide engaging lessons in reading, writing, and speaking Tamil, focusing on grammar and advanced writing skills. My teaching approach is simple and effective, making it easy for you to grasp the language at your own pace.

Whether you're a complete beginner or looking to improve your fluency, I’m here to guide you in becoming a confident Tamil speaker. Join me, and let's enjoy the journey of learning Tamil together!

Education

M.A and B.Ed. in Madurai Kamaraj University, Madurai
I hold a D.T.Ed, MA, and B.Ed. in education.

I specialize in teaching grammar and advanced writing skills, using straightforward methods to make learning Tamil easy and accessible for you.

Experience / Qualifications

I have over two years of experience teaching the Tamil language and have had the opportunity to teach students from around the world. As a friendly and enthusiastic instructor, I'm dedicated to helping anyone interested in learning Tamil. I offer lessons at all levels—beginner, intermediate, and advanced—and provide well-rounded practice in reading, writing, and speaking.

Age

Children (7-12 years old)

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Advanced

Duration

30 minutes

60 minutes

The class is taught in

English

Tamil

Skills

Tamil

Computer science

Computer programming

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Tamil Language Mastery – Learn Tamil Online with Expert Guidance

23 £ /h

Dive into the beauty of the Tamil language, one of the oldest classical languages in the world! This online Tamil class offers personalized lessons designed for beginners to advanced learners. Whether you want to improve your conversational skills, gain literacy in Tamil script, or deepen your understanding of Tamil culture, this course has you covered.

What You’ll Learn:

Basics of reading and writing in Tamil script
Pronunciation, vocabulary, and grammar essentials
Conversational Tamil for everyday interactions
Insights into Tamil literature, traditions, and cultural heritage
Each lesson is tailored to meet your unique learning goals, helping you to build fluency and confidence. Join us and embark on a rewarding journey into the heart of Tamil language and culture!

4.85

(13)

Hello, I am a doctoral student in electrical engineering and associate professor in engineering sciences, experienced in the field of electrical engineering, I offer support courses in the subjects of engineering sciences (Electronics, automatics, electrical engineering, automation, programming).

Digital electronics
Analog electronic
electromagnetism (propagation of high frequency waves)
Automatic (continuous, sampled)
electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
engineering Sciences
RDM
Python,VHDL
PIC Microprocessor and Microcontroller
Signal processing and data acquisition
Engineering Sciences

These courses allow the student to get up to speed and regain confidence in all scientific subjects, just as they prepare him effectively for the Baccalaureate, the Preparatory Classes or various examinations of the engineering classes.

COURSE OBJECTIVES AND PEDAGOGICAL APPROACH

Resumption and deepening of fundamental concepts through exercises with course reminders.

Put the student in a situation of questioning and research.

Respond to individual issues and questions

Exercise training in order to achieve real mastery of the content.

Learn to build theoretical reasoning from observable facts or hypotheses.

Specific preparation for higher education requirements (in-depth content, increase in work capacity, enrichment of scientific background)

This educational approach is effective since it has often led me to interesting results with my students.

Associate professor provides support courses in electrical engineering

Robert

4.85

(13)

As a Franco-Belgian management teacher, I give Excel lessons with passion!
Whether remotely or face-to-face, I offer many examples and exercises to accompany you.
I travel without problem throughout the region of Brussels and its surroundings, for lessons of at least 2 hours. For France, courses are only given remotely.

Here are some key words that will be covered in my classes:
Scenario analysis, Year, Rounding, Today, Bdnb, Bdnbval, Bdsum, Search, Column, Copy/paste in values, Copy/paste with transposition, Consolidation, Date, Datedif, Determat, Dollar, Right, Righterg, Equiv, Esterror, Estna, Frequency, Filter (simple and advanced), Format of cells, Left, Large.Value, Printing of documents, Index, Indirect, Inversemat, Day, Weekday, Line, Matrix, Max, Maxa, Max.Si, Min , Mina, Mina.If, Formatting of cells and ranges, Month, Average, Average.If, Nb, Nb.If, Nbval, Naming of cells and ranges, No, Small.value, Product, Productmat, Protection of cells, Lookup (Lookup), Lookupv (VLookup), Lookuph (HLookup), If (If), If.Not.Disp, If.Conditions, Iferror, Sum, Sumproduct, Sum.If, Sum.If.Set, Substitute , Pivot tables, Sorting, Cell locking

Do not hesitate to contact me to organize your lessons according to your needs and availability. Together, we will develop your Excel skills in an efficient and personalized way.

Khalil

4.85

(13)

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.

Adam

4.85

(13)

doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me

Amin

4.85

(13)

Are you a university student, engineer, or professional who needs to actually use data — not just learn theory about it?
This course is built around real problems and real code. We skip the textbook formulas and go straight to applying statistics and data science the way professionals do: with Python (pandas, NumPy, scikit-learn, matplotlib) and R (RStudio).
What we cover, adapted to your level and goals:
- Descriptive and inferential statistics (the ones that actually matter)
- Data cleaning, exploration, and visualization
- Regression, classification, and intro to machine learning
- Time series and forecasting basics
- R for statistical analysis and academic research

Who this is for:
- University students in statistics, economics, engineering, or biology
- Professionals wanting to move into data analysis or data science
- Researchers who need to process and present data properly

I use Python and R professionally as a working engineer — everything I teach comes from real application, not just academic exercises.

Salah

4.85

(13)

Master Python with Personalized Courses

Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

Why Choose My Courses?

Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

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.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

Florian

4.85

(13)

Experienced and patient teacher of logic for computer science.

I have taught logic, formal languages and automata theory to undergraduates for six years. My tutoring is adapted to the student's level and goals. Whether you need to learn logic for your studies, or you would simply like to know more about the subject, I will be more than happy to help you improve your understanding and skills.

Logic
The sciences presuppose a certain standard of rationality. An ability to distinguish between correct reasoning and claims that do not follow from the assumptions. In this class we study the basic principles of logic and apply mathematical techniques to the study thereof.
Topics include:
Propositional and Predicate Logic
Syntax and semantics
Natural deduction
Semantic tableaux
Correctness and soundness
Completeness

Formal languages and automata
A formal language is an abstraction of general characteristics of programming languages. Such a languages consists of a set of symbols together with some rules to determine whether a string made up out of those symbols is a member of the language.

Topics include:
Regular languages, context-free languages
Finite automata, pushdown automata, Turing machines
Regular expressions
Regular grammar, context-sensitive grammar
Pumping lemmas for regular and context-free languages

Raouf

4.85

(13)

Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?

Laroussi

4.85

(13)

Session 1: Revolutionizing your Scientific Writing with LaTeX & AI
Duration: 2 Hours | Level: Beginner | Tools: Overleaf + AI**

First Hour: Foundations and Cloud Environment (60 min)

1. Introduction to LaTeX Philosophy (15 min)

- The "WYSIWYM" concept:** Explain the difference between Word (*What You See Is What You Get*) and LaTeX (*What You See Is What You Mean*). Why content takes precedence over form.
- Key advantages:** Unrivaled typographic quality, automatic reference management, stability on long documents (theses), and free of charge.
- The structure of a file:** Distinction between the **preamble** (the brain: settings and packages) and the **body of the document** (the heart: text).

2. Immersion in Overleaf (25 min)

- Configuration:** Creation of an account and first project "Blank Project".
- Exploring the interface:** The file panel (left), the code editor (middle) and the PDF preview (right).
- Real-time collaboration:** How to share a project and leave comments (like on Google Docs).
- History and versions:** How to revert to a previous version in case of a compilation error.

3. Practical Workshop: My First Document (20 min)

* Writing basic commands: `\documentclass`, `\usepackage[french]{babel}`, `\title`, `\author`.
* Compilation of the document and observation of the result.
* Structuring: Use of `\section` and `\subsection`.

Second Hour: Mathematics and the Magic of AI (60 min)

4. The Power of Mathematics (20 min)

- Mathematical modes:** Difference between the text (`$...$`) and the centered block (`\[...\]`).
- Essential syntax:** Fractions `\frac{}{}`, exponents `^`, indices `_`, and roots `\sqrt{}`.
- Introduction to AMS packages: Why amsmath and amssymb are essential for professional rendering.

5. From hand to screen: AI at the service of LaTeX (30 min)

- Presentation of OCR tools:** Use of **Mathpix Snip** (the leader) or models like Gemini/ChatGPT to transform a photo into code.
- Concrete demonstration:
1. Take a picture of a complex handwritten formula (e.g., an integral with matrices).
2. Use AI to generate the corresponding LaTeX code.
3. Correction and insertion: Learn to check the AI-generated code before copying and pasting it into Overleaf.

6. Conclusion and Q&A (10 min)

* Summary of achievements.
* Resources for further exploration
* Definition of the exercise for the next session.

Verónica

4.85

(13)

Primary School Teacher. I offer personalized guidance in developing lesson plans and learning activities adapted to current educational regulations. During our sessions, you will learn to structure and write a complete lesson plan, including specific competencies, assessment criteria, core knowledge, methodology, addressing diversity, evaluation, and learning situations. My goal is to help you understand each section so you can independently develop a coherent, rigorous, and high-quality lesson plan.

You will learn to design practical, creative proposals, developing the necessary skills to apply them in the classroom or in competitive examination processes.

Ammar

4.85

(13)

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.

Baia

4.85

(13)

I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
pyRevit fundamentals and setup
Automating repetitive Revit tasks (model checks, plan generation, data extraction)
Writing custom scripts for your firm's specific workflows
Applying Python automation to real MEP/BIM projects

Shahd

4.85

(13)

This cohort is designed for young people who want to learn in an affordable, flexible, and enjoyable way without having to dedicate a huge amount of time each week or even just extra support.

This beginner-friendly course introduces students to the world of computers and computer science through simple explanation.

Students will learn how computers work, including hardware, software, memory, storage, data, and how a computer processes information. They will then explore how applications are used to create and organize information, with practical experience using tools such as Microsoft Word, PowerPoint, and Excel.

As the course progresses, students will be introduced to important computer science concepts including binary numbers, algorithms, programming, databases, networks, the Internet, and cybersecurity.

By the end of the course, students will have a good foundation in computer science and improved digital skills.

Laith

4.85

(13)

Want to break into systems, legally? Learn penetration testing from someone who does it for a living.

I'm a cybersecurity engineer with 150+ penetration tests and vulnerability assessments for government, financial, and telecom organizations. I hold CRTP, eWPT, and eJPT certifications, and I'll teach you the same methodology I use on real engagements. No boring theory dumps, just live, hands-on practice.

You'll learn:

The pentesting process: scoping, recon, scanning, exploitation, and reporting
Network and web application testing basics
Common vulnerabilities like SQL injection, XSS, and broken authentication
Core tools: Nmap, Burp Suite, Nessus, Metasploit, and Wireshark
How to document findings and write reports that clients actually act on
How to build a home lab and keep practicing on your own

Perfect for: beginners, students, career changers, developers, IT staff, and anyone preparing for eJPT, PNPT, OSCP, or CEH.

You'll walk away with a clear pentesting workflow, real hands-on skills, and a solid roadmap for a career in offensive security.

Basic computer skills are enough, and networking knowledge is a plus. Book your first session and let's start hacking (ethically)!

Shraddha

4.85

(13)

Additional materials + practice qs | Lesson plans + regular feedback | Recording + session notes available

A common theme running through our lessons will be to simplify - even topics like electromagnetic induction can be reduced to small bits :)

Together, we'll:
1. Find learning gaps
2. Break concepts into small bits
3. Apply to real world and exam questions
4. Work on exam technique - like the difference between "explain" and "describe" questions
5. Build mark-scheme friendly language

“It's not that I'm so smart, it's just that I stay with problems longer”
- Albert Einstein, while studying superposition of waves, I’m sure :)