Mohamed - Artificial Intelligence teacher - Remote
1st class free
Mohamed - Artificial Intelligence teacher - Remote

Mohamed profile and their contact details have been verified by our experts

Mohamed

  • Rate ₹2,380
  • Response 8h
  • Students

    Number of students accompanied by Mohamed since their arrival at Superprof

    50+

    Number of students accompanied by Mohamed since their arrival at Superprof

Mohamed - Artificial Intelligence teacher - Remote
  • 5 (11 reviews)

₹2,380/hr

1st class free

Contact

1st class free

1st class free

  • Artificial Intelligence
  • Machine Learning

Applied AI Research Engineer with 3+ years’ experience teaches machine learning, deep learning, NLP, computer vision, and generative AI

  • Artificial Intelligence
  • Machine Learning

Class location

Super Tutor

One of our best tutors. They have a quality profile, experience in their field, verified qualifications and a great response time. Mohamed will be happy to arrange your first Artificial Intelligence classes.

About Mohamed

Hi, I’m Mohamed, an Applied AI and Research Engineer with a B.Sc. in Communications and Information Engineering from Zewail City of Science and Technology.

I have more than five years of experience across machine learning, deep learning, applied research, AI engineering, and technical projects, alongside over three years of online tutoring experience.

I have supported undergraduate and master’s students, including learners enrolled at Georgia Tech and the University of Maryland, with machine-learning courses, programming assignments, research papers, technical projects, debugging, and academic preparation.

My experience covers the complete AI development process:

Understanding and preparing data
Selecting and implementing appropriate models
Training and evaluating machine-learning systems
Diagnosing poor performance and data leakage
Reading and reproducing research papers
Building NLP, computer-vision, recommendation, and biosignal-processing projects
Developing transformer and generative-AI systems
Building retrieval and agentic AI applications
Deploying AI systems using APIs, databases, caching, queues, and vector search
Designing model evaluations, experiments, and production monitoring

My research experience includes developing customized transformer models for biological sequence generation. My engineering work also includes production AI platforms, retrieval systems, agentic workflows, model evaluation pipelines, and backend infrastructure.

I teach more than library commands. My goal is to help you understand:

Why an algorithm works
Which assumptions it makes
How to select suitable evaluation metrics
Why a model is failing
How to improve an experiment systematically
How research code differs from production systems

Lessons are adapted to your current level and objective. I work with beginners learning Python, university students studying ML or deep learning, researchers working through papers, and engineers building production AI applications.

Lessons are available in English and Arabic.

See more

About the class

  • Class 10
  • Classes 11 & 12
  • Class 12
  • +10
  • levels :

    Class 10

    Classes 11 & 12

    Class 12

    Adult Literacy

    Class 11

    BTech

    Masters

    PhD

    MBA

    Beginner

    Intermediate

    Advanced

    Professional

  • English

All languages in which the class is available :

English

Machine learning is not learned by copying notebooks or memorizing framework functions. In our lessons, you will build a clear understanding of the underlying concepts and then apply them through code, experiments, debugging, and practical projects.

I can help you with a complete learning path, a specific university topic, a research paper, an existing project, or a technical problem blocking your work.

How lessons are structured

The first session begins with an assessment of your current knowledge, target, and technical gaps. We then create a focused plan instead of following a generic course.

A typical lesson includes:

Reviewing the problem and required background
Explaining the concept visually and mathematically
Implementing it together in Python
Testing the implementation and interpreting results
Debugging mistakes or model-performance issues
Summarizing the lesson and defining the next practical step

You will write and reason about the code during the lesson. I will guide you through the process rather than simply providing finished answers.

Machine-learning foundations

We can cover:

End-to-end machine-learning workflows
Data cleaning and preprocessing
Exploratory data analysis
Feature engineering and feature selection
Regression and classification
Decision trees and ensemble methods
Support vector machines and nearest-neighbour methods
Clustering and dimensionality reduction
Cross-validation
Hyperparameter optimization
Regularization
Class imbalance
Data leakage
Error analysis
Model interpretation
Mathematics and statistics for AI

Topics can include:

Linear algebra for machine learning
Probability and distributions
Statistics and hypothesis testing
Calculus and gradients
Loss functions
Optimization
Gradient descent and backpropagation
Bias and variance
Overfitting and generalization
Understanding evaluation metrics correctly

The mathematics is taught at the depth required for your goal. Beginners can focus on intuition, while advanced learners can work through derivations and implementations.

Deep learning

We can study and implement:

Neural-network fundamentals
Forward and backward propagation
Optimization algorithms
Activation and loss functions
Convolutional neural networks
Recurrent neural networks
Attention mechanisms
Transformers
Transfer learning
Fine-tuning
Parameter-efficient fine-tuning
Embeddings and representation learning
Training stability and experiment debugging

PyTorch is my primary framework for advanced deep-learning work, but I can also support TensorFlow-based courses and projects.

Natural language processing and generative AI

Topics include:

Text preprocessing and tokenization
Word and sentence embeddings
Text classification
Semantic similarity
Information retrieval
Sequence-to-sequence models
Attention and transformers
BERT, T5 and related architectures
Language-model fundamentals
Prompt design and structured outputs
Model fine-tuning
Retrieval-augmented generation
Vector databases and embedding search
Tool-calling and agentic workflows
LLM evaluation
Hallucination and reliability analysis
Guardrails and AI-system safety
Computer vision

We can cover:

Image preprocessing
Image classification
Convolutional neural networks
Transfer learning
Data augmentation
Object-detection fundamentals
Segmentation fundamentals
Vision transformers
Model evaluation
Handling limited or imbalanced image datasets
Debugging training and generalization problems
Reinforcement learning

I can support reinforcement-learning foundations, including:

Agents, environments, states, and actions
Rewards and return
Markov decision processes
Value-based learning
Q-learning
Policy-based methods
Exploration versus exploitation
Implementing introductory environments and agents

For advanced reinforcement-learning research, contact me with the specific topic or paper before booking.

Research and academic support

I can help you:

Understand difficult research papers
Break equations and architectures into manageable steps
Reproduce published methods
Design controlled experiments
Establish suitable baselines
Select metrics
Conduct ablation studies
Diagnose invalid experimental conclusions
Organize a thesis or research project
Review methodology and technical writing
Prepare research presentations

Support is educational and collaborative. I will help you understand and improve your work rather than complete graded work on your behalf.

AI engineering and production systems

For learners moving beyond notebooks, lessons can cover:

Structuring maintainable ML projects
Building inference APIs
FastAPI-based model serving
Batch and asynchronous processing
PostgreSQL and Redis integration
Vector databases and retrieval systems
Dockerized deployment
Experiment tracking
Model and prompt versioning
Evaluation pipelines
Logging, monitorin,g and observability
Testing AI applications
Moving from a prototype to a production-ready system
Tools and technologies

Depending on the project, we may work with:

Python
NumPy and pandas
scikit-learn
PyTorch
TensorFlow
Hugging Face
OpenCV
Matplotlib
Jupyter and Google Colab
FastAPI
PostgreSQL
Redis
Docker
Vector databases and retrieval frameworks

Tools are selected based on the problem. The objective is not to memorize libraries, but to develop skills that remain useful when frameworks change.

Who these lessons are for

Lessons are suitable for:

Beginners entering AI or machine learning
University students
Master’s students and researchers
Software engineers transitioning into AI
Professionals preparing for ML interviews
Learners developing portfolio projects
Teams prototyping AI applications
Researchers implementing or evaluating papers

Standard lessons are 60 minutes. Coding sessions, project reviews, and research deep dives can be scheduled for 90 minutes.

Before the first lesson, send me your current level, the topic or project, relevant code or materials, and the outcome you want to achieve. I will use that information to prepare a focused session.

See more

Rates

Rate

  • ₹2,380

Pack prices

  • 5h: ₹10,950
  • 10h: ₹20,947

online

  • ₹1,904/hr

free classes

This first free class with Mohamed will allow you to get to know each other and to specify the exact learning requirements for the upcoming classes.

  • 30mins

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