Our Machine Learning course covers supervised and unsupervised learning, linear and logistic regression, decision trees and ensembles, support vector machines, k-nearest neighbours, clustering, dimensionality reduction, neural networks, model evaluation and overfitting, with Python, NumPy and scikit-learn. Lessons are live and one-to-one with a verified subject expert, who explains each concept with worked examples, solves problems from your own module and helps you prepare for exams and coursework. Suitable for computer science and engineering students, following university syllabi in the UK, US, Australia and India (including AKTU, VTU and Anna University style courses).
Reviewed by K. Sardar, Bachelor's degree in Data Science; data science and AI specialistLast updated
$14 – $36
per hour
Free demo class · pay only if you continue
What you will learn
Prerequisites
Tags
Course structure
In short: this course gives one-to-one online tutoring in machine learning. A verified tutor explains supervised and unsupervised learning, model evaluation, regularisation and neural networks, with code in Python and the maths behind each algorithm.
Machine learning (ML) is a branch of artificial intelligence in which computers learn patterns from data to make predictions or decisions without being explicitly programmed for each case. A machine learning course covers supervised learning (regression and classification), unsupervised learning (clustering), probabilistic models and reinforcement learning, together with how to evaluate and improve models.
This online course teaches Machine Learning one to one with a verified tutor who has studied or taught it at university level, at your pace and aligned with your own module.
Related subject: Machine learning help.
Before machine learning, many students take our Artificial Intelligence course; for image-based models, see the Computer Vision course.
| Topic | Key content |
|---|---|
| Foundations | Supervised learning, regression, model selection, generalisation, Bayesian decision theory |
| Data and parametric methods | Pre-processing, maximum likelihood estimation, bias and variance, multivariate data |
| Classification | Decision trees, pruning, rule extraction, linear discrimination, logistic regression, support vector machines |
| Bayesian and graphical models | Bayesian estimation, graphical models, conditional independence, belief propagation |
| Hidden Markov models | Markov processes, the three HMM problems, Viterbi and Baum-Welch |
| Reinforcement learning | Elements of RL, model-based learning, temporal difference learning |
| Clustering and non-parametric methods | K-means, EM and mixture models, hierarchical clustering, k-nearest neighbours |
A model overfits when it performs well on training data but poorly on unseen data, because it has learned noise rather than the underlying pattern.
| Symptom | Likely cause | Fix |
|---|---|---|
| Low training error, high test error | Overfitting (high variance) | More data, regularisation, simpler model, cross-validation |
| High training and test error | Underfitting (high bias) | More features, more complex model |
| Unstable results between runs | High variance | Cross-validation, ensembles |
Here is a typical question: classify the point (1, 1) with k = 3, using A(1, 2), A(2, 2), B(3, 3) and B(4, 1).
| Point | Class | Distance to (1, 1) |
|---|---|---|
| (1, 2) | A | 1.00 |
| (2, 2) | A | 1.41 |
| (3, 3) | B | 2.83 |
| (4, 1) | B | 3.00 |
The three nearest are A, A and B, so the prediction is A.
Students most often lose marks in Machine Learning through the habits below. Each is easy to fix with practice and feedback on your own answers.
| Mark-loser | What to do instead |
|---|---|
| Data leakage | Split data before scaling or encoding |
| Accuracy on imbalanced data | Report precision, recall and F1 |
| No baseline | Compare against a simple model |
| Hyperparameters tuned on test data | Use validation sets or cross-validation |
| Unscaled features for distance models | Scale features for k-NN and SVMs |
Machine learning modules are usually assessed by a written exam on algorithms and derivations, plus coursework or projects implementing models in Python with libraries such as NumPy, pandas and scikit-learn.
Tutors explain concepts, work through practice problems with you and give feedback on your approach. Graded work you submit must be your own, as set out in our academic integrity policy. For deadline-driven guidance, see assignment help.
Lessons are live and one to one on video with screen sharing and a shared whiteboard. You can follow a full course or book sessions on the topics you find hardest before exams. See also Python for Data Science, Artificial Intelligence and our Machine Learning subject page.
A machine learning tutor online gives you live 1-to-1 lessons on regression, classification, clustering and reinforcement learning, working through the maths and building each model in Python.
This course suits university students on computer science and data courses, and working learners moving into data roles.
Prerequisites: basic Python, data structures, linear algebra, probability and calculus.
Course length: 40 one-hour lessons across 7 modules.
| Stage | Topics | What you can do afterwards |
|---|---|---|
| 1 | Introduction; data pre-processing and parametric methods | Prepare data and fit regression models |
| 2 | Classification techniques | Train and compare classifiers |
| 3 | Bayesian estimation and graphical models; hidden Markov models | Model uncertainty and sequences |
| 4 | Clustering and non-parametric methods; reinforcement learning | Group unlabelled data and explain reward-based learning |
Where it leads: see the Python for Data Science course, the Artificial Intelligence course or our machine learning tutoring page.
A field of AI in which computers learn patterns from data to make predictions or decisions.
Supervised learning uses labelled data; unsupervised learning finds structure in unlabelled data.
Python, with libraries such as NumPy, pandas and scikit-learn.
Linear algebra, calculus and probability help you understand the algorithms.
When a model learns noise in the training data and performs poorly on new data.
A method that tests a model on several train-test splits to estimate performance reliably.
Tutors explain methods and review your code; the project you submit must be your own.
Yes. Tutors practise derivations, algorithm questions and interpretation.
The price depends on the tutor and session length. You see the hourly rate before booking, with no subscription.
No. Tutors explain, guide and give feedback so you can complete your own work, in line with our academic integrity policy.
It predicts a point's class from the majority class of its k closest training points.
Distance-based and gradient-based models are sensitive to feature ranges.
A penalty on large weights that reduces overfitting.
Classification predicts categories; regression predicts numbers.
An optimisation method that updates parameters opposite to the gradient of the loss.
Most beginners need a few months of steady study to understand the core ideas and build simple models in Python, provided they already know basic programming and school-level maths. Becoming confident with deep learning and real projects takes longer. Your tutor sets a weekly plan around your course, exam or project deadline.
Machine learning feels hard at first because it combines programming, statistics, linear algebra and calculus. It becomes manageable when you learn the maths alongside small coded examples rather than in isolation. Most students find model evaluation and the maths behind gradient descent the hardest parts, so a tutor focuses on them.
Yes. Lessons are online, so you can book times that suit your time zone.
Yes, you need Python fundamentals, because the course builds models in code. You should also know basic linear algebra, probability and calculus. If any of these are weak, your tutor can revise them first or suggest starting with Python for Data Science.
Yes. You can book one session to see if the tutor suits you, with no subscription.
Yes. You can learn with a 1-to-1 machine learning tutor online through live lessons with a shared screen. Your tutor explains the maths behind each model, helps you debug your Python code and shows you how to approach assignments, so you can complete them yourself.
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