Unsupervised Machine Learning Diploma | Arabic

Unlock the Power of Unsupervised Learning with Python: A Professional Journey into Unsupervised ML Algorithms

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Unsupervised Machine Learning Diploma | Arabic

What You Will Learn!

Intro to Unsupervised Machine Learning
Linear and nonlinear Dimensionality Reduction
PCA | SVD | Random Projection
Principle Component Analysis (PCA)
Singular Value Decomposition (SVD)
Isomap | LLE | t-SNE
Isometric mapping (Isomap)
Locally Linear Embedding (LLE)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Anomaly Detection
Clustering
K-Means
K-Means for preprocessing
K-Means for semi-supervised Learning
K-means for Image Segmentation
DBSCAN
Hierarchical Clustering
Gaussian Mixture Models (GMM)
Group Segmentation

Description

Diploma in Unsupervised Machine Learning Using Python. It is a unique diploma that enriches Arabic content in the field of artificial intelligence. It is a comprehensive training course based on interaction, application, detailed explanation, and a thorough breakdown of algorithms from scratch to an excellent understanding of the algorithm. The course emphasizes practical application in coding and building a strong model used in real-life scenarios. Suitable for beginners, professionals, and anyone interested in data science, data analysis, machine learning, and artificial intelligence, including Data Analysts, Data Scientists, Machine Learning Engineers, and AI Engineers.

The diploma qualifies you to master unsupervised machine learning and data science not only through coding but also through a solid understanding of the mathematics related to algorithms, with detailed explanations from both theoretical and practical perspectives.

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What You Will Learn:

Introduction to the Course:
Introduction to Unsupervised Machine Learning
Understanding the fundamentals of unsupervised machine learning.
Linear and Nonlinear Dimensionality Reduction
Principal Component Analysis (PCA)
Incremental Principal Component Analysis (IPCA)
Kernel Principal Component Analysis (Kernel PCA)
Singular Value Decomposition (SVD)
Gaussian & Sparse Random Projection
Isomap Algorithm
Locally Linear Embedding (LLE)
t-SNE Algorithm
Practical Project on Anomaly Detection Using Dimensionality Reduction Methods
Introduction to Clustering
K-Means Algorithm
Use Cases of K-Means
Image Segmentation using K-Means
Data Preprocessing using K-Means
Semi-supervised ML using K-Means
DBSCAN Algorithm
Hierarchical Clustering Algorithm
Gaussian Mixture Models (GMM) Algorithm
Practical Project on Group Segmentation Using Different Techniques of Clustering

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Whether you're an AI enthusiast, developer, or data scientist, this course will empower you with the knowledge and practical skills necessary to excel in unsupervised Machine Learning and its applications in real life of AI.

Join us now and embark on an enriching learning journey that will set you on the path to mastering Unsupervised Machine Learning for cutting-edge AI projects.