Ammar - Software tutor - Montréal
1st lesson free
Ammar - Software tutor - Montréal

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Software lesson.

Ammar

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Software lesson.

  • Rate ₦24383
  • Response 2h
  • Students

    Number of students accompanied by Ammar since their arrival at Superprof

    50+

    Number of students accompanied by Ammar since their arrival at Superprof

Ammar - Software tutor - Montréal
  • 5 (15 reviews)

₦24383/h

1st lesson free

Contact

1st lesson free

1st lesson free

  • Software

Master Machine Learning, AI & Python with a PhD Engineer and Professor | 25+ Years’ Expertise & Professor | Beginner to Advanced Levels

  • Software

Lesson location

Ambassador

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Software lesson.

About Ammar

I am a PhD Engineer, university professor, researcher, and multidisciplinary technical educator with more than 25 years of experience in engineering, mathematics, statistics, programming, data analysis, research methodology, and computational modelling.

I have taught and supported university students, graduate researchers, engineers, analysts, professionals, and career-transition learners. My students range from complete Python beginners to advanced learners working on machine-learning assignments, dissertations, predictive models, artificial-intelligence applications, technical interviews, and professional data projects.

My teaching philosophy is based on a clear principle: machine learning should be understood as a connected system of mathematics, data, algorithms, code, evaluation, and application—not treated as a collection of library commands.

I explain what each model is designed to do, how its mathematical logic works, what assumptions it makes, how Python implements it, how performance should be measured, and how to identify data leakage, overfitting, bias, and interpretation errors. When a concept is difficult, I connect equations, diagrams, code, model outputs, and practical examples.

My expertise includes:

• Python, NumPy, pandas, SciPy, Statsmodels, Matplotlib, Seaborn, and scikit-learn
• Supervised and unsupervised machine learning
• Regression, classification, clustering, dimensionality reduction, anomaly detection, and forecasting
• Neural networks, deep learning, TensorFlow, Keras, and PyTorch
• Natural language processing, computer vision, recommendation systems, and generative-AI foundations
• Model evaluation, feature engineering, hyperparameter tuning, explainability, and responsible AI
• SQL, Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, Excel, and Power BI
• Statistics, linear algebra, optimization, research methodology, and reproducible analytical workflows

Lessons are personalized to your level and objectives. I can help you learn Python and machine learning systematically from the beginning, understand difficult theory, debug code, complete an assignment, analyze research data, prepare for a technical interview, or develop an end-to-end AI project.

My goal is to help you become accurate, confident, and independent. You should leave each lesson understanding what the model does, why it works, how to test it, how to improve it, and how to apply the same reasoning to new problems.

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About the lesson

  • Secondary
  • WASSCE
  • Masters
  • +8
  • levels :

    Secondary

    WASSCE

    Masters

    MBA

    Beginner

    Intermediate

    Advanced

    Professional

    Doctorate

    National Common Entrance Examination (NCEE)

    GCE

  • French
  • English

All languages in which the lesson is available :

French

English

Machine learning and artificial intelligence become much easier when the mathematics, algorithms, Python code, data, and real-world applications are connected clearly.

My lessons help you move beyond copying code or using models as black boxes. You will learn how to define the problem, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret the results responsibly.

Each lesson is personalized to your current level, mathematical background, programming experience, dataset, assignment, research project, interview, or professional objective. We begin by identifying your existing knowledge, software environment, expected output, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first free lesson combines a discussion of your background, objectives and tutoring needs, an initial assessment of your current knowledge, personalized planning and scheduling, and a short trial lesson so that we can determine the most effective way to work together.

A- PYTHON FOUNDATIONS
• Variables, data types, operators, conditions, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

B- 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 quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

C- 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

D- 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
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation

E- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern discovery
• Selecting methods, evaluating structure, and interpreting results without predefined labels

F- MODEL EVALUATION AND IMPROVEMENT
• Training, validation, and test sets; cross-validation; and hyperparameter tuning
• 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, selection, scaling, and regularization

G- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent networks, and transformer foundations
• TensorFlow, Keras, or PyTorch depending on the project and learner’s environment

H- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and language-model foundations
• Computer vision, image classification, object detection foundations, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

I- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention, prompting, retrieval-augmented generation, and model evaluation
• Using AI APIs, vector databases, document retrieval, and structured AI workflows when relevant
• Reliability, hallucination, bias, privacy, responsible use, and appropriate human validation

J- 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 they support the project
• Additional libraries may be introduced according to the selected specialization and dataset

K- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• End-to-end projects involving data preparation, model development, evaluation, interpretation, and presentation
• Academic assignments, dissertations, research studies, portfolio projects, technical interviews, and workplace applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

A typical lesson may include conceptual explanation, mathematical intuition, live coding, guided implementation, model evaluation, troubleshooting, and a concise summary of the next steps.

You may work with your own dataset, assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets adapted to your level.

My objective is not merely 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 and interpretable solution.

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Rates

Rate

  • ₦24383

Pack prices

  • 5h: ₦121915
  • 10h: ₦243830

webcam

  • ₦24383/h

Travel

  • + ₦10

free lessons

This first lesson offered with Ammar will allow you to get to know each other and clearly specify your needs for your next lessons.

  • 1hr

Details

The first free lesson is a structured introductory and trial session. We will briefly introduce ourselves—including your academic or professional background and my relevant expertise—clarify your objectives, deadlines and tutoring needs, and assess your current knowledge through discussion and a short diagnostic activity. We will then establish a focused learning plan and schedule for future lessons. The remaining time will be used for a short trial lesson on a representative concept or problem, allowing you to experience my teaching approach before deciding whether to continue.

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