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Since August 2018
Instructor since August 2018
AngularJS with HTML and CSS - Frontend / UI development
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From 83 C$ /h
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HTML works perfectly fine as far as static documents are concerned. If a web application requires dynamic views however, it's less than ideal. That's where AngularJS comes in; an open-source framework that extends HTML vocabulary for you application, resulting in intuitive, expressive single page applications.

AngularJS HOME
AngularJS Intro
AngularJS Expressions
AngularJS Modules
AngularJS Directives
AngularJS Model
AngularJS Data Binding
AngularJS Controllers
AngularJS Scopes
AngularJS Filters
AngularJS Services
AngularJS Http
AngularJS Tables
AngularJS Select
AngularJS SQL
AngularJS DOM
AngularJS Events
AngularJS Forms
AngularJS Validation
AngularJS API
AngularJS W3.CSS
AngularJS Includes
AngularJS Animations
AngularJS Routing
AngularJS Application
Location
location type icon
Online from United States
About Me
I am Professional Software Developer from last 7 years. I can teach you all cool Stuff about Computers and Computer Programming. You can learn from basic to professional level.Following Topics Which I can teach you.

Front End: HTML, HTML5, CSS, CSS3, BootStrap,JavaScript, Angular JS, Angular 2, React Js,Jquery,Ajax
Back End: PHP
Services: REST
Database: MySql
OS: Windows, Linux, Ubntu, Unix, Mac OS
Others: Artificial Intelligence(API AI/Dialog-flow), Web Crawling, Web Scrapping, Maths, Science
Education
4 Year B.Tech - Information technology Course at Anna University- Chennai, Tamil nadu, India.

Prior 2+2 year in Higher Secondary in Computer Basics and programming
Experience / Qualifications
Ionixx Technologies
- Genband - 3 years

TATA Consultancy Services - 5 years
- Liberty Mutual - 2 years
- ABN AMRO - 1 year
- The Staples - 1 year
- Macys - 1 year
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
60 minutes
The class is taught in
English
Tamil
Reviews
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
React JS for Beginners

JS Frameworks Comparison / Introduction
React Intro
React Get Started
React ES6
React Render HTML
React JSX
React Components
React Props
React State
React Lifecycle
React Events
React Forms
React CSS
Redux Basics
Redux Examples
Read more
HTML Introduction
HTML Editors
HTML Basic
HTML Elements
HTML Attributes
HTML Headings
HTML Paragraphs
HTML Styles
HTML Formatting
HTML Quotations
HTML Comments
HTML Colors
HTML CSS
HTML Links
HTML Images
HTML Tables
HTML Lists
HTML Blocks
HTML Classes
HTML Id
HTML Iframes
HTML JavaScript
HTML File Paths
HTML Head
HTML Layout
HTML Responsive
HTML Computercode
HTML Entities
HTML Symbols
HTML Charset
HTML URL Encode
CSS Syntax
CSS How To
CSS Colors
CSS Backgrounds
CSS Borders
CSS Margins
CSS Padding
CSS Height/Width
CSS Box Model
CSS Outline
CSS Text
CSS Fonts
CSS Icons
CSS Links
CSS Lists
CSS Tables
CSS Display
CSS Max-width
CSS Position
Read more
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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
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• 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
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4- MATHEMATICAL FOUNDATIONS
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5- SUPERVISED MACHINE LEARNING
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• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
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6- UNSUPERVISED LEARNING
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This course provides a foundational understanding of Information Technology, data centers, covering architecture, power & cooling, networking, storage, virtualization, security and lots more. Learn best practices for efficiency, scalability, and reliability while exploring emerging data center solutions. Ideal for IT professionals, engineers, and facility managers involved in data center deployment or management.

This course offers a comprehensive exploration of Information Technology, data center infrastructure, guiding students through the entire lifecycle—from initial design and planning to day-to-day operations and long-term performance optimization. Students will learn the critical components of data center design, including site selection, power and cooling systems, space planning, networking, and physical security. The course also covers operational best practices, monitoring tools, energy efficiency strategies, disaster recovery planning, and emerging trends. By integrating technical, environmental, and management perspectives, students will gain the knowledge and skills required to build and maintain high-performance, cost-effective, and sustainable data center environments.
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Python is today one of the most widely used programming languages in the world, both in Data Science, Artificial Intelligence, Web Development and for task automation.
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.
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As a Digital Transformation student, I know that programming is a fundamental building skill—whether you are a future engineer or a curious young learner. I designed this course as a practical guide to mastering essential tools and, above all, to developing the creative mindset of a programmer.

You will learn how to break down complex problems into logical steps, turn your ideas into functional code, and view errors (“bugs”) as stimulating challenges rather than obstacles.
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Python and algorithm courses designed for high school and university students wishing to improve their programming skills and succeed in their computer science studies.

The course allows you to:

understanding algorithmic logic,
learn to solve problems step by step,
mastering the basics of Python,
prepare for exams, assignments and projects
progress quickly with practical exercises tailored to the student's level.

We will be working on:

variables and types,
terms,
loops,
functions,
tables/lists,
classical algorithms
programming logic,
Exercises and detailed solutions.

A clear, progressive method suitable for beginners as well as students with difficulties in computer science.

Perfect for:

students in NSI (Digital Science and Technology),
high school students,
students in BTS/BUT/Bachelor's degree programs in computer science,
beginners in programming.

Courses available in French, English or Arabic.
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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

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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.
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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
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