Machine Learning with Python - Classroom

London School of Emerging Technology

Certificate of completion

Digital certificate - Included

LSET Validated Python Machine Learning Engineer

£100

Machine learning is no longer optional - it is a core driver of innovation across finance, healthcare, retail, cyber security, and emerging technologies. This classroom-based Machine Learning Engineer with Python course provides the structured pathway you need to enter or advance within this high-growth field.

You will learn how to:

• Write clean, efficient Python code for data-driven applications
• Prepare and analyse real-world datasets
• Build supervised and unsupervised machine learning models
• Evaluate and optimise model performance
• Understand the complete ML development lifecycle
• Apply industry best practices used by modern engineering teams

Unlike purely theoretical programmes, this course focuses on applied learning through guided labs and structured exercises. By the end, you will have developed a complete capstone project demonstrating your ability to solve practical machine learning problems.

Duration: 12 weeks
Teaching Hours: 24 hours
Practice Hours (Optional): 120 hours
Lab Hours: 24 hours
Intake: 1st Day of Every Month

This flexible structure allows you to combine instructor-led learning with significant hands-on development time.

• Python for Machine Learning
• Data Cleaning and Preprocessing
• Exploratory Data Analysis
• Supervised Learning (Regression & Classification)
• Unsupervised Learning (Clustering Techniques)
• Feature Engineering
• Model Evaluation & Performance Metrics
• Cross Validation & Optimisation
• Introduction to ML Workflow & Deployment Concepts
• Version Control & Basic MLOps Foundations

To enhance employability and professional readiness, learners benefit from:

• Git & Version Control Workshop
• Agile & Project Workflow Fundamentals
• Interview Preparation for ML Roles
• CV & LinkedIn Optimisation
• Presentation & Technical Communication Skills

Your progress is assessed through:

• 18 Coding Exercises
• 5 Assignments
• 5 Quizzes
• Capstone Project
• Group Activities
• Presentations

This ensures you graduate with demonstrated technical capability, not just theoretical knowledge.

Module 1: Python Foundations for Data Science
Module 2: Data Analysis & Visualisation
Module 3: Core Machine Learning Algorithms
Module 4: Model Validation & Performance Tuning
Module 5: Applied Machine Learning Project
Module 6: ML Workflow & Deployment Foundations

The final capstone project integrates all core competencies into a portfolio-ready solution.

By completing this programme, you will gain:

• Professional-level Python skills for ML
• Practical experience building machine learning models
• Data preprocessing and feature engineering expertise
• Analytical problem-solving skills
• Understanding of model optimisation techniques
• Experience delivering technical presentations
• A portfolio-ready capstone project

Completing the Machine Learning Engineer with Python course opens doors to high-demand roles in AI, data science, and advanced analytics.

Machine Learning Engineer

Design, develop, and deploy scalable ML systems. Work on model optimisation, pipeline automation, and production-level implementation.

Data Scientist

Analyse large datasets, build predictive models, and generate actionable insights aligned with business goals.

MLOps Engineer

Automate ML workflows, implement CI/CD pipelines, monitor deployed models, and ensure reliability in production systems.

AI/ML Developer

Integrate machine learning models into software applications and develop intelligent features for digital products.

Advanced Data Analyst

Enhance traditional analytics with predictive modelling and machine learning techniques.

ML Research Engineer

Work on experimental models, deep learning architectures, and innovative AI solutions.

With the growing adoption of AI across industries such as FinTech, Healthcare, E-commerce, Cybersecurity, and Logistics, skilled Machine Learning Engineers are among the most sought-after professionals globally.

Students must have at least high school knowledge in maths and must be willing to learn Machine Learning.

Basic Understanding of English

Basic Proficiency with Computers

Ability to work in Group

Machine Learning Engineer

Business Intelligence (BI) Developer

Data Scientist

Human-Centered Machine Learning Designer

Computational Linguist

Software Developer