How We Teach
A Considered Approach to Online Learning
Each Logicrove course is built around the same conviction: that learning technical subjects works best when concepts arrive in sequence, practice follows theory closely, and a qualified person is available to answer questions in real time.
Every course week pairs a recorded lecture — available to watch and rewatch at any time — with a live discussion session where teaching assistants walk through exercise solutions and address participant questions. Practice exercises use the same tools participants will encounter in applied work: Python, scikit-learn, and PyTorch.
Progress is measured through exercises, written milestones, and in the capstone programme, a project of the participant's own design. Outcomes depend on each participant's effort and prior preparation — the course provides the structure; the learning is genuinely the participant's own.
Recorded Lectures
Watch at any time, pause and rewatch as needed throughout the course window.
Live Discussion
Weekly or twice-weekly live sessions with teaching assistants for questions and review.
Practice Exercises
Hands-on exercises in Python using the tools that appear in applied ML work.
Completion Records
Participants who complete the coursework receive a course completion record from Logicrove.
Foundations of Machine Learning
An eight-week online course covering the foundations of supervised and unsupervised machine learning. Each week includes one recorded lecture, one live discussion session, and a set of practice exercises in Python and scikit-learn. Topics move progressively from regression through clustering to a brief introduction to neural networks.
- Linear and logistic regression in Python
- Decision trees and basic ensemble methods
- Clustering techniques and unsupervised approaches
- Model evaluation: metrics, cross-validation, and bias-variance
- Introduction to neural network concepts
Suitable for: Adults with a basic Python background and comfort with high-school level mathematics.
Duration: 8 weeks — one recorded lecture and one live session per week.
Completion record: Issued to participants who complete the practice exercises.
RM 500
per participant
Deep Learning Practitioner Track
A twelve-week intermediate track for learners who have completed a foundations course or have done equivalent self-study. The track focuses on practical deep learning: designing and training networks, working with vision and language architectures, and completing a substantial final project in PyTorch. Participants set their own pace within the twelve-week window.
- Neural network design and training loops in PyTorch
- Convolutional architectures for vision tasks
- Sequence and language modelling fundamentals
- Three written milestones across the track
- Final project review with a senior teaching assistant
Suitable for: Learners who have completed a foundations course or have equivalent self-study experience.
Duration: 12 weeks — weekly lectures, twice-weekly live discussion sessions, three milestones, and a final project review.
Pacing: Participants manage their own schedule within the twelve-week window.
RM 1,580
per participant
Applied AI Engineering Capstone
A sixteen-week capstone programme for learners who have completed an intermediate track or have equivalent professional experience. Participants design and complete a capstone project of their own choosing, supported by weekly lectures, project-based exercises, and a senior mentor assigned for the full duration of the programme.
- Model deployment and serving strategies
- Evaluation pipelines and monitoring practices
- Applied case studies drawn from publicly available work
- Participant-designed capstone project
- Senior mentor paired for the full 16-week programme
Suitable for: Learners who have completed an intermediate deep learning track or hold equivalent professional experience.
Duration: 16 weeks — weekly lectures, project-based learning, and a mentor-guided capstone.
Capstone: The final project is designed and owned by the participant.
RM 2,940
per participant
Choosing a Course
Which Course Is Right for You?
The three courses form a natural progression, though each can also be entered based on where your current preparation sits.
Best for
Starting Out in ML
You can write basic Python and understand high-school algebra, but you haven't worked with machine learning libraries before.
Foundations Course →Best for
Moving Into Deep Learning
You've covered ML foundations and want to build and train neural networks for practical vision and language tasks.
Deep Learning Track →Best for
Applied AI Engineering
You have intermediate deep learning experience and want to work through a significant project with senior mentorship and applied engineering focus.
Capstone Programme →| Feature | Foundations RM 500 |
Deep Learning RM 1,580 |
Capstone RM 2,940 |
|---|---|---|---|
| Duration | 8 weeks | 12 weeks | 16 weeks |
| Recorded lectures | |||
| Live discussion sessions | Weekly | Twice-weekly | Weekly |
| Python practice exercises | |||
| Written milestones | — | 3 milestones | |
| Participant-designed capstone | — | — | |
| Senior TA project review | — | ||
| Assigned senior mentor | — | — | |
| Completion record | |||
| Primary library | scikit-learn | PyTorch | PyTorch + deployment tools |
Shared Across All Courses
What Every Course Has in Common
Data and Privacy
Participant information is handled according to Malaysia's Personal Data Protection Act 2010. No personal data is shared with third parties for marketing purposes.
Qualified Teaching Staff
Live sessions are led by teaching assistants with hands-on ML or AI engineering experience, not automated systems or contracted freelancers.
Current Tooling
Course materials are reviewed at the start of each cohort to reflect stable, current versions of Python, scikit-learn, and PyTorch.
Responsive Support
Questions submitted outside live sessions receive a written response from a teaching assistant within one business day during the course window.
Transparent Content
Each course listing describes topics, weekly structure, and prerequisites clearly, so participants can make an informed decision before enrolling.
Honest Expectations
Outcomes depend on each participant's effort and preparation. Logicrove provides structure and support; it does not make claims about employment or earnings.
Pricing
Course Fees
All prices are in Malaysian Ringgit (RM) and cover the full course duration. Payment details are shared upon enquiry.
Track 1
Foundations of Machine Learning
RM 500
per participant • 8 weeks
- 8 recorded lectures
- 8 live discussion sessions
- Practice exercises in Python & scikit-learn
- Course completion record
Track 2
Deep Learning Practitioner Track
RM 1,580
per participant • 12 weeks
- 12 recorded lectures
- Twice-weekly live sessions
- Three written milestones
- Final project + TA review
- Self-paced within 12 weeks
Track 3
Applied AI Engineering Capstone
RM 2,940
per participant • 16 weeks
- 16 recorded lectures
- Project-based weekly sessions
- Participant-designed capstone
- Senior mentor for full 16 weeks
- Applied case studies
Get in Touch
Not Sure Where to Start?
Send us a message describing your background and what you'd like to work toward. We'll suggest the most appropriate course and answer questions about structure, pacing, or prerequisites.