Unsupervised Machine Learning Diploma | Arabic

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

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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.

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

Who Should Attend!

  • Students & Any one with passion about Unsupervised Machine Learning
  • Machine Learning Engineers
  • Artificial Intelligence Engineers
  • Statisticians
  • Data Analysts & Scientists