Zubaid Rashid Artificial intelligence Machine Learning expert
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As a passionate and results-driven Machine Learning Engineer with a solid foundation in mathematics and Python programming, I am dedicated to helping others understand and apply these powerful concepts. My expertise centers around developing and deploying data-driven models to address complex, real-world problems. With a deep understanding of algorithms, statistics, linear algebra, and calculus, I aim to impart knowledge on how to build robust and scalable machine learning solutions across various fields.
I have hands-on experience with Python and its extensive ecosystem of libraries, including TensorFlow, Keras, PyTorch, and Scikit-Learn. I believe in the importance of blending theoretical concepts with practical implementation and encourage students to explore these tools for real-world applications. My goal is to foster a continuous learning environment, ensuring that we stay updated with the latest advancements in AI/ML and computational techniques.
In my teaching, I emphasize not only the development of machine learning models but also the underlying theories that make them work, with a focus on deep learning, reinforcement learning, and computer vision, among other advanced methodologies. By building a strong conceptual foundation, I aim to equip learners with the skills they need to thrive in the rapidly evolving AI/ML landscape.

Subjects

Chemistry Grade 8-Grade 12
Python Grade 10-Bachelors/Undergraduate
Mathematics Grade 1-Grade 12
Machine Learning Grade 8-Bachelors/Undergraduate
Physics (10+2)

Experience

Physics faculty (Oct, 2023–Sep, 2024) at 1 year experience in Aakash institute

Physics Tutor, teaching Physics for growing students inhysicsa

Education

MTech (Jun, 2024–now) from Islamic University of Scienceand Technology Awantipora J&K–scored NA
Engineering (Jun, 2015–Jul, 2019) from National institute of technology Srinagar Jammu and Kashmir–scored 6.68

Fee details

$6–15/hour (US$6–15/hour)

it depends upon the number of students, fee can decrease.

Courses offered

python
- US$30
- Duration: 1 Month
- Delivery mode: Online
- Group size: 6 - 10
- Instruction language: English
- Certificate provided: Yes
1. Introduction to Python
History and Features: Understanding Python's history, key features, and why it is popular.
Installation: How to install Python and set up the development environment (IDEs like PyCharm, VS Code, or Jupyter Notebook).
2. Python Basics
Syntax: Python syntax and basic commands.
Variables and Data Types: Declaring variables and understanding types like integers, floats, strings, lists, tuples, and dictionaries.
Operators: Arithmetic, logical, comparison, and bitwise operators.
3. Control Flow Statements
Conditional Statements: if, elif, and else statements.
Loops: for and while loops, along with break, continue, and pass statements.
Nested Loops and Conditions: Using loops within loops and complex condition checking.
4. Functions
Defining Functions: Writing reusable functions with parameters and return values.
Scope of Variables: Local and global scope.
Lambda Functions: Anonymous functions and their usage in Python.
5. Data Structures
Lists: Operations on lists like slicing, appending, and sorting.
Tuples: Immutable sequences and how to use them.
Dictionaries: Key-value pairs, dictionary operations.
Sets: Working with unique values and set operations (union, intersection, etc.).
6. Modules and Packages
Importing Modules: How to import and use built-in modules.
Creating Your Own Modules: Writing custom modules and importing them in other scripts.
Python Package Index (PyPI): Installing external libraries using pip.
7. File Handling
Reading and Writing Files: Opening, reading, writing, and closing files.
Exception Handling: try, except blocks for handling errors gracefully.
8. Object-Oriented Programming (OOP)
Classes and Objects: Understanding the basic concepts of OOP.
Constructors: The __init__ method.
Inheritance: Creating subclasses and extending functionalities.
Polymorphism and Encapsulation: Advanced OOP concepts.
9. Advanced Topics (Optional)
List Comprehensions: Shorter syntax for creating lists.
Generators and Iterators: Efficient looping mechanisms.
Decorators: Functions that modify the behavior of other functions.
Regular Expressions: Pattern matching with the re module.
10. Python Libraries for AI/ML
NumPy: Working with arrays, mathematical functions, and basic matrix operations.
Pandas: Data manipulation and analysis, working with DataFrames.
Matplotlib and Seaborn: Plotting graphs and visualizing data.
Scikit-learn: Basic machine learning algorithms and model implementation.
11. Projects and Case Studies
By the end of the course, you likely worked on a small project or case study to apply the learned concepts, possibly involving data analysis, a simple game, or a beginner-level AI/ML model.