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Machine Learning and Statistical Computing

Learn core machine learning algorithms, statistical computing techniques, and data analysis tools to build predictive models and solve real-world problems
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2 months to complete
at 2-3 hours a week

Overview

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Learning outcomes

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Course content

1

Supervised Learning

2

Unsupervised Learning

3

Regression Analysis

4

Time Series Forecasting

5

Neural Networks

Career Path

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Key facts

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Why this course

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People also ask

Everything you need to know before you start

Straight answers — no waiting on a reply. Most learners are enrolled within 60 seconds of finding what they need below.

60 sec
From enrol to start
24/7
Course access
Self-paced
Learn on your time
Certificate
Included in fee

We offer immediate access to our course materials through our open enrollment system. This means:

  • The course starts as soon as you pay the course fee, instantly
  • No waiting periods or fixed start dates
  • Instant access to all course materials upon payment
  • Flexibility to begin at your convenience

This self-paced approach allows you to begin your professional development journey immediately, fitting your learning around your existing commitments.

We offer two flexible learning paths to suit your schedule:

  • Fast Track: Complete in 1 month with 3-4 hours of study per week
  • Standard Mode: Complete in 2 months with 2-3 hours of study per week

You can progress at your own pace and access the materials 24/7.

There are no formal entry requirements for this course. You just need:

  • A good command of English language
  • Access to a computer/laptop with internet
  • Basic computer skills
  • Dedication to complete the course
Ready when you are
Most learners finish reading the FAQs and enrol in the same minute.
Self-paced · Certificate included · 24/7 access · 60-second start.
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Assessment is done through:

  • Multiple-choice questions at the end of each unit
  • You need to score at least 60% to pass each unit
  • You can retake quizzes if needed
  • All assessments are online

Upon successful completion, you will receive:

  • A digital certificate from London School of Business and Administration
  • Option to request a physical certificate
  • Transcript of completed units
  • Certification is included in the course fee
Open enrolment · Start today

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Why people choose us for their career

Trusted by professionals worldwide

Verified outcomes from learners who finished the course and put it to work.

4.5
Based on 4 learner reviews · 4 countries
98%
Would recommend
100%
Verified learners
2026
Cohort active
Completed from United States
MC
Michael Carter
US · Course completed

I just completed the 'Machine Learning and Statistical Computing' course at Stanmore School of Business and I'm blown away! The course content was incredibly comprehensive, covering everything from the fundamentals of machine learning to advanced statistical modeling techniques. The instructors were knowledgeable and provided excellent support throughout the course. I gained practical skills in data preprocessing, feature engineering, and model evaluation, which I've already applied to my current project at work. The course materials were top-notch, with relevant examples and case studies that made the learning experience engaging and fun. I highly recommend this course to anyone looking to upskill in machine learning and statistical computing!

LH
Leila Hassan
EG · Course completed

I found the 'Machine Learning and Statistical Computing' course to be a great introduction to the field. The course covered a wide range of topics, from supervised and unsupervised learning to neural networks and deep learning. I appreciated the emphasis on practical applications and the use of real-world examples to illustrate key concepts. The course materials were well-organized and easy to follow, although I did find some of the assignments to be a bit challenging. Overall, I'm satisfied with the course and feel that it's helped me achieve my learning goals. One area for improvement could be the addition of more interactive elements, such as discussions or group projects, to enhance the learning experience.

KN
Kaito Nakamura
JP · Course completed

Wow, what an amazing course! I was a bit skeptical at first, but the 'Machine Learning and Statistical Computing' course at Stanmore School of Business exceeded my expectations in every way. The instructors were passionate and knowledgeable, and the course materials were incredibly comprehensive. I loved the hands-on approach, with plenty of opportunities to practice and apply the concepts to real-world problems. I gained a deep understanding of machine learning algorithms, including regression, classification, and clustering, and I'm now confident in my ability to apply these skills to my own projects. The course was also well-structured, with clear deadlines and expectations, which made it easy to stay on track. Overall, I'm thrilled with the course and would highly recommend it to anyone interested in machine learning and statistical computing!

RS
Rafael Silva
BR · Course completed

I recently completed the 'Machine Learning and Statistical Computing' course at Stanmore School of Business and I'm really pleased with the experience. The course provided a solid foundation in machine learning and statistical computing, covering topics such as data visualization, hypothesis testing, and confidence intervals. I appreciated the use of case studies and examples to illustrate key concepts, which made the learning experience more engaging and interactive. The course materials were also well-organized and easy to follow, although I did find some of the mathematical concepts to be a bit tricky at times. Overall, I feel that the course helped me achieve my learning goals and I'm now more confident in my ability to apply machine learning and statistical computing techniques to my work. One suggestion I might make is to include more feedback opportunities, such as peer review or instructor feedback, to help students gauge their progress and identify areas for improvement.





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Recently updated!

April 2026