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AI & Machine Learning (Advanced)

Go beyond the basics with hands-on machine learning models, algorithms, and real-world applications.

No reviews yet55hFlexibleAll Levels
$45 USD/hr
55h of instructionFlexible formatAll Levels

What you’ll learn

  • Understand core machine learning algorithms and when to apply
  • Build, train, and evaluate models using real datasets
  • Apply concepts like overfitting, regularization, and model tuning
  • Work with common ML tools and libraries

    • About this course

    This all levels programming & technology course offers 55 hours of instruction. By the end, you'll be able to understand core machine learning algorithms and when to apply; build, train, and evaluate models using real datasets; apply concepts like overfitting, regularization, and model tuning; work with common ML tools and libraries. Every course on TutorA is reviewed by our team before it's published, and every request is matched by a person — not an open marketplace where anyone can pitch you.

    Structured to work for both newcomers and learners with prior experience in programming & technology.

    What this course covers

    • overfitting: training vs. validation performance gap
    • regularization, dropout, train/val/test splits
    • hyperparameter tuning: learning rate, batch size, depth
    • reading loss curves and attention-based architectures

    A model that trains without errors but performs badly is one of the most common walls advanced ML students hit, and it's rarely obvious from the code alone why it's happening. Overfitting — where a model learns the training data too specifically, including its noise, and then performs worse on new data — is the usual suspect, and diagnosing it means looking at the gap between training and validation performance, not just one number in isolation.

    Regularization techniques, dropout, and careful train/validation/test splits are the standard tools for addressing it, but knowing which one actually fits your specific situation takes more judgment than a course video tends to convey. Hyperparameter tuning is a related sticking point — learning rate, batch size, and network depth all interact with each other, and a change that helps one architecture can hurt another, which is part of why so much applied ML work is empirical rather than purely theoretical.

    Beyond training itself, advanced sessions often get into architecture-specific questions: why a particular layer type suits a particular kind of data, how attention-based approaches handle relationships across a sequence differently than earlier methods did, or how to read a loss curve to tell whether a model is still learning, has plateaued, or is diverging.

    Because this course assumes real background already, sessions work best when they start from something concrete — your actual notebook, a paper you're trying to implement, or a specific error your training run is producing — rather than a general lecture, since the sticking points at this level tend to be specific enough that generic explanation doesn't help much.

    Why a TutorA tutor

    Advanced AI and machine learning courses on Coursera, Udemy, or standalone MOOCs can walk you through the math and code at a fixed pace, but they can't debug why your specific model isn't converging or answer a follow-up question about your actual project. A TutorA tutor works with you live and 1:1 on the material you're actually stuck on — a specific architecture, a paper, a project — rather than a one-size-fits-all syllabus. Every tutor is reviewed before being matched, and pricing is shown before you book. Most AI & Machine Learning tutoring on TutorA comes from India-based tutors with real, verifiable programming experience listed on their own profile.

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    AI & Machine Learning (Advanced) tutoring FAQ

    This course is aimed at more advanced learners — sessions are matched to your existing background so you can go deeper into specific topics rather than repeating fundamentals.

    Yes — live 1:1 sessions can focus on the actual project, paper, or problem you're working on.

    It depends on your goals and background — let your tutor know what you're working on (a specific architecture, a research area, applied projects) so sessions can be matched to it.

    No — TutorA courses are live 1:1 tutoring, not self-paced video lessons, so there's no certificate of completion.

    Pricing varies by tutor and is shown on this page before you request a session.

    For AI & Machine Learning (Advanced): Generally, yes — most of TutorA's coding tutors are based in India. Each one is reviewed by our team before being matched, and their profile lists their actual programming background rather than a generic bio.

    It depends on your background and coursework — let your tutor know which framework you're actually using, since implementation details, syntax, and debugging patterns differ between them even when the underlying concepts, like backpropagation or gradient descent, are the same. Sessions work best when they start from your real code rather than a generic framework overview.

    Comfort with linear algebra, calculus, and probability generally helps, since those areas underpin most machine learning concepts. That said, sessions can also work through the specific math behind a topic as it comes up in your actual project or coursework, rather than requiring you to have mastered it all beforehand in isolation.