تعلم الآلة باستخدام بايثون
Machine Learning using Python

Course Overview:

This hands-on course provides a practical and comprehensive introduction to Machine Learning using Python. Designed for learners eager to move beyond theory, the course focuses on building and evaluating real-world ML models through live, interactive sessions and actual datasets — ensuring a deep understanding of both concepts and implementation.

Learning Objectives:

By the end of the course, participants will be able to:

- Understand core machine learning principles and workflows

- Acquire and preprocess real-world datasets

- Apply supervised and unsupervised learning techniques

- Evaluate model performance using appropriate metrics

- Optimize model performance and avoid common pitfalls

- Build and present a complete machine learning project

What You Will Learn:

1. Introduction to Machine Learning

- What is ML and why it matters

- Types of ML: Supervised, Unsupervised, Reinforcement

- Real-world use cases and career applications

2. Data Acquisition & Understanding

- Characteristics of a good dataset

- Finding and loading datasets

- Exploring data: structure, features, labels

3. Data Preprocessing

- Handling missing data

- Encoding categorical variables

- Feature scaling and normalization

- Outlier detection and treatment

- Feature selection and dimensionality reduction

- Handling imbalanced data 

4. Learning Paradigms & Problem Types

- Supervised vs. Unsupervised learning

- Regression vs. Classification

- Selecting the right approach for different problem types

5. Model Development

- Ensemble Learning Techniques:

  - Bagging and Boosting

- Core algorithms (theory + practice):

- Linear & Logistic Regression

- Decision Trees

- K-Nearest Neighbors

- Random Forest

- Support Vector Machines

- XGBoost, AdaBoost

- K-Means Clustering

- Building models using Scikit-learn

- Visualizing and interpreting results

6. Model Evaluation & Validation

- Splitting data

- Cross-validation techniques

- Evaluation metrics

- Identifying overfitting & underfitting

7. Model Optimization

- Hyperparameter tuning 

- Bias-variance tradeoff

- Improving model generalization

8. Capstone Project

- A full-cycle machine learning project including:

- Data acquisition and preprocessing

- Model selection, training, and evaluation

- Optimization and presentation of results