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Machine Learning in Practice

Designed for professionals eager to grasp machine learning essentials, blends core algorithms, practical exercises, and real-world applications to provide participants with the knowledge and confidence to apply AI methods effectively in diverse business and technical contexts.

 

 

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    Why Bakkah?

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    Flexible Learning

    What to Expect From This Machine Learning in Practice Course?

    By the end, participants will be able to:

    • Understand and explain key machine learning algorithms and their applications.
    • Apply supervised and unsupervised learning techniques to real-world datasets.
    • Build and evaluate basic neural networks and deep learning models.
    • Split datasets effectively for training, testing, and validation purposes.
    • Assess model performance using accuracy, precision, and recall.
    • Develop problem-solving skills to choose suitable ML approaches for different contexts.

    Who Should Enroll in this Machine Learning in Practice?

    • Data analysts and professionals looking to expand into machine learning.
    • Software engineers and developers seeking to integrate ML into applications.
    • Business professionals interested in leveraging ML for data-driven decisions.
    • Researchers and students aiming to build foundational skills in ML.
    • Anyone curious about applying machine learning in practical scenarios.

    What are the acquired skills from this Machine Learning in Practice?

    • Understanding of core machine learning algorithms and principles.
    • Ability to implement supervised and unsupervised learning techniques.
    • Practical knowledge of neural networks and deep learning basics.
    • Skills in data preparation, splitting, training, and testing.
    • Evaluating models using accuracy, precision, and recall.
    • Applying ML methods to solve real-world problems.

    Machine Learning in Practice Course  Self-Study

    • Reading Learning Materials. 
    • Pre-Reading file. 
    • Pre and Post Course Assessments. 
    • Modules Exercises. 
    • The language will be English.
    • Module 1: Introduction to Machine Learning Algorithms 
    • Module 2: Supervised Learning (Linear Regression, Logistic Regression) 
    • Module 3: Unsupervised Learning (Clustering, Dimensionality Reduction) 
    • Module 4: Neural Networks and Deep Learning Basics 
    • Module 5: Data Splitting, Training & Testing 
    • Module 6: Model Evaluation: Accuracy, Precision, Recall 

    Course Inclusions

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