Certificate

MLx Fundamentals

Oxford Machine Learning School

This program offered a comprehensive 5-hour course that included theory and practical sessions covering key areas in modern machine learning, such as Optimisation for Machine Learning, Fundamentals (Mathematics for Machine Learning, Reinforcement Learning, Bayesian Optimisations), and Understanding the Basic Pillars Behind GPT4.

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Curriculum

Inside the MLx Fundamentals plan.

5 modules · 5 units · ~5h of structured prep.

  1. 01

    Optimisation for Machine Learning

    1 unit

    In this module, students will explore foundational concepts of machine learning, including key terminology, methodologies, and applications. Through engaging lectures, learners will gain insights into the principles that drive machine learning technologies and their relevance in various industries.

    1. 1. Talk
  2. 02

    Fundamentals of Mathematics for Machine Learning

    1 unit

    In this module, students will explore foundational concepts of machine learning, including key terminology, methodologies, and applications. Through engaging video lectures, learners will gain insights into the principles that drive machine learning technologies and their relevance in various industries.

    1. 1. Talk
  3. 03

    Fundamentals of Reinforcement Learning

    1 unit · ~3h

    In this module, students will delve into the core principles of reinforcement learning, including Markov Decision Processes, value functions, and the exploration-exploitation trade-off. Through practical coding exercises, learners will gain hands-on experience with both model-based and model-free algorithms, enhancing their decision-making optimization skills.

    1. 1. Talk
  4. 04

    Fundamentals of Bayesian Optimisation

    1 unit

    In this module, students will explore foundational concepts of machine learning, including key terminology, methodologies, and applications. Through engaging lectures, learners will gain insights into the principles that drive machine learning technologies and their relevance in various industries.

    1. 1. Talk
  5. 05

    Basic pillars of GPT4: Understanding DL fundamentals for a better comprehension of LLM

    1 unit · ~3h

    In this module, students will delve into the core concepts of deep learning, focusing on key models such as variational autoencoders, diffusion models, and generative pre-trained transformers. Through hands-on activities, learners will gain practical experience with TensorFlow and Jupyter notebooks, enhancing their understanding of deep learning architectures, hyperparameter tuning, and real-world applications.

    1. 1. Talk

Certification

How you certify for MLx Fundamentals.

Pass the certification exam to earn your certificate.

30 questions
45 minutes
70% to pass
Single select (Radio): 4 options

Issued by

  • Oxford Machine Learning School

    OxML

Certificate type

Certificate of Completion

CPD accredited

CPD Certified

Certification number

47392

Question formats in the certification exam:

  • Single select (Radio) (100%)

Ready when you are

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