Machine Learning course
Machine Learning course at Itisalat Academy: Learn Python, build models, and master algorithms like Linear Regression, KNN, and K-Means in 4 weeks.
English
Last updated
Wed, 24-Sep-2025
Machine Learning course at Itisalat Academy: Learn Python, build models, and master algorithms like Linear Regression, KNN, and K-Means in 4 weeks.
English
Last updated
Wed, 24-Sep-2025
The Hidden Engine Behind Smart Recommendations
The” Machine Learning course” offered by Itisalat Academy is one of the most in-demand fields in the job market. It is used in big data analysis, prediction, building intelligent systems, and AI solutions that change how companies and individuals operate.
This course is designed to be your practical bridge towards building machine learning models with Python using the most famous libraries like Scikit-learn, TensorFlow, and Keras.
Over 4 intensive weeks, you will learn how to handle data, select appropriate algorithms, and the mechanism of each model.
In addition to training models and evaluating their performance to solve real-world problems.
This course is also ideal for those interested in artificial intelligence and wishing to enter the job market in this growing field.
This course aims to provide trainees with the theoretical foundations and practical skills necessary to understand and build machine learning models using Python. Its prominent objectives are:
Recognizing the mechanism of the most famous algorithms in classification, clustering, and regression.
Building models using algorithms: Linear Regression, Decision Tree, KNN, Naive Bayes.
Applying Clustering algorithms like: K-Means, DBSCAN, Hierarchical Clustering.
Evaluating models using performance metrics: Accuracy, Precision, Recall, F1 Score.
Understanding Overfitting / Underfitting problems and how to address them via Regularization.
Gaining practical skills in using Scikit-learn and TensorFlow libraries to develop smart solutions.
Developing analytical thinking and selecting the appropriate algorithm for each problem.
The topics of this course are designed to take you on a gradual journey starting from theoretical basics, then moving to the most common algorithms, leading to practical application using real data.
Introduction to Machine Learning
Initially, we will get acquainted with the fundamental pillars of machine learning, explaining the difference between Artificial Intelligence (AI), Machine Learning, and Deep Learning.
Then we move to the most important basic terms that will accompany you throughout your journey, such as:
Features representing the input data.
Labels.
The concept of Train-Test Split, meaning splitting data into a training set and a test set.
Supervised Learning
After establishing the concepts, we will move to supervised learning where labeled data is used to train models.
We start with the Regression algorithm, specifically Linear Regression, and review how it can be used to predict numerical values like house prices or future sales.
Then we move to Classification, which is used to distinguish between categories like predicting spam email (Spam-Not Spam).
During this, we will learn about the most famous algorithms like:
Decision Tree
K-Nearest Neighbors (KNN)
Naive Bayes
And we will discuss the mechanism of each algorithm, its advantages, limitations, in addition to the best cases where they can be used.
Unsupervised Learning
In this part, we move to unsupervised learning, where the data is not labeled. Here we focus on Clustering algorithms, which are among the most important tools in data analysis.
We will learn about K-Means and how to use it to cluster customers based on their purchasing behavior.
We will also cover DBSCAN, which excels at discovering clusters with complex shapes.
In addition to Hierarchical Clustering which allows viewing data hierarchically.
We will also discuss practical applications like customer segmentation, analyzing hidden patterns, and anomaly detection.
Model Evaluation and Improvement
After building models, it is necessary to evaluate their performance. Therefore, we will learn about the most important performance metrics (Evaluation Metrics) like:
Accuracy.
Precision.
Recall.
F1 Score.
In addition to the main challenges like Overfitting and Underfitting, and we explain Regularization techniques that help improve model performance and maintain their balance.
Practical Projects and Real Applications
In this part, the course transitions from the theoretical side to practical application. The practical application will include:
Creating and executing real projects.
Building a Classification Model using the Scikit-learn library.
Applying Clustering algorithms on big data and analyzing the results.
A simple experiment using TensorFlow and Keras to explore the capabilities of deep learning.
These projects ensure that you will not leave the course with only theoretical understanding, but with practical experience directly applicable in various fields like business, healthcare, and e-commerce.
This course is directed towards:
Graduates and students of scientific disciplines (Computer Science, Statistics, Mathematics, Economics).
Data analysts who want to transition to building machine learning models.
Software developers seeking to acquire skills in artificial intelligence.
Academic researchers who need to apply ML techniques to big data.
Beginners in artificial intelligence who have basic knowledge of Python.
Eager to provide you with the utmost benefit, here are some common questions you might have about this course:
No, the course starts from the basics. Basic knowledge of Python and some mathematical concepts is sufficient.
ML: Focuses on algorithms like regression, classification, and clustering.
DL: An advanced branch of ML that relies on deep neural networks to process images, audio, and text.
Yes, the course relies on applied learning through practical projects using Python libraries.
You can apply for jobs like: Advanced Data Analyst, Entry-Level Machine Learning Engineer, or AI Research Assistant.
Advance your career with My-Communication Academy. Get job-ready skills in 5G, Fiber Optics, and Data Science with expert-led online courses, lifetime access, and career support.
