Machine Learning with Python
Learn the foundations and practice of Machine Learning using Python through a structured, hands-on program. In this course you’ll move from data handling to building and evaluating real ML models, and finish with a complete end-to-end project you can showcase.
What You Will Learn
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Python for ML: Write clean, reproducible code and work efficiently with notebooks (Jupyter) and virtual environments.
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Numerical computing with NumPy: Vectors, matrices, broadcasting, and performance tips for fast computation.
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Data wrangling with Pandas: Import, clean, transform, and join real-world datasets; handle missing values and outliers.
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Data visualization with Matplotlib: Communicate insights with clear charts and plots; best practices for readable visuals.
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Supervised learning – Regression: Train, validate, and tune models (Linear/Polynomial Regression, Regularization, Metrics).
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Supervised learning – Classification: Build and compare classifiers (k-NN, Logistic Regression, Decision Trees, SVM); confusion matrix & ROC-AUC.
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Model selection & evaluation: Train/validation/test splits, cross-validation, overfitting vs. underfitting, feature scaling, pipelines.
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Final project implementation: Build a complete ML solution—from data ingestion and EDA to a production-style notebook with results and report.
Why Take This Course?
Gain practical, industry-relevant skills to analyze data and ship ML solutions. Learn by doing with guided coding, mini-assignments, and a capstone project so you can apply your skills immediately at work or in future studies. You will develop problem-solving and debugging habits essential for real projects and receive focused mentorship from an instructor with academic and industrial background.
Course Syllabus
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Python Basics for Machine Learning (syntax, notebooks, environments)
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NumPy Fundamentals (arrays, broadcasting, performance)
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Pandas for Data Handling (cleaning, joins, grouping)
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Data Visualization with Matplotlib (EDA & storytelling)
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Regression Algorithms (linear, regularization, metrics)
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Classification Algorithms (k-NN, logistic, trees, SVM; evaluation)
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Final Project (end-to-end ML pipeline + report)
Each module includes live coding, short exercises, and a real-world mini-dataset. The final session is dedicated to project review and feedback.
What You’ll Achieve
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Confidence working with real-world datasets and messy data.
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Ability to implement core ML algorithms in Python using NumPy, Pandas, Matplotlib, scikit-learn.
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Skills to evaluate models properly and improve accuracy with sound methodology.
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A complete ML project to showcase in your resume, portfolio, or applications.
Course Information
Instructor: Mohsen Zaker (Computer Engineer & ML Instructor, AICER Lab)
Duration: 16 hours
Start Date: 4 Mehr 1404
Certificate: AICER Lab completion certificate
👉 Register now and bring your ML ideas to life!