Machine Learning with Python – Classroom

London School of Emerging Technology

LSET Validated Python Machine Learning Engineer

£100

The LSET Machine Learning Engineer with Python course follows a six-module curriculum, progressing from foundations and data preparation to model development, evaluation and deployment.

• Introduction to machine learning and its applications
• Supervised, unsupervised and reinforcement learning
• Python essentials: data types, loops and functions
• Jupyter Notebooks
• Environment setup with Anaconda, virtual environments and pip
• Overview of the machine learning pipeline

• Loading data from CSV files, Excel and databases
• Handling missing values, outliers and duplicates
• Data transformation and preprocessing
• Exploratory data analysis
• NumPy arrays and matrices
• Descriptive statistics with pandas and NumPy
• Feature selection and feature engineering basics

• Introduction to supervised learning
• Linear regression and its use cases
• Logistic regression for classification
• k-nearest neighbours
• Decision trees and random forests
• Support vector machines
• Model selection and the bias-variance trade-off

• Introduction to unsupervised learning
• K-means and hierarchical clustering
• Principal component analysis (PCA)
• t-SNE for visualisation
• Applications of clustering and dimensionality reduction
• Evaluating unsupervised models

• Training and test splits and cross-validation
• Confusion matrices, precision, recall and F1 score
• ROC curves and AUC
• Regression metrics: MAE, MSE, RMSE and R-squared
• Overfitting and underfitting
• Hyperparameter tuning with grid search and random search
• Building and validating scikit-learn pipelines

• Saving and loading models with joblib and pickle
• Building REST APIs with Flask or FastAPI
• Integrating machine learning models into web applications
• Docker for containerised deployment
• Git and GitHub version control
• CI/CD basics in MLOps
• Model monitoring and retraining considerations

The course combines guided practical sessions, coding exercises, assignments and a capstone project. Industry mentor support, group activities and presentations form part of the learning experience.

• Data cleaning, manipulation and feature engineering
• Supervised and unsupervised learning
• Model training, evaluation and hyperparameter tuning
• Machine learning pipelines
• REST API development for models
• Container deployment and MLOps fundamentals

The curriculum is subject to updates. LSET communicates the final curriculum during course induction.

Learners are responsible for obtaining and maintaining any required software subscriptions, licences or tools. These costs are not included in the course fees.

This Machine Learning with Python course is suitable for:

• Software developers interested in machine learning engineering.
• Data analysts seeking to apply machine learning to analytics.
• Python programmers interested in data science and AI development.
• IT professionals seeking knowledge of model deployment and MLOps.
• Recent graduates in computer science, software engineering or statistics.
• Technology enthusiasts interested in practical machine learning.
• Entrepreneurs and start-up founders interested in integrating machine learning into products.

Learners should meet the published Python and mathematics entry requirements.

• Basic understanding of Python programming and Python libraries.
• At least secondary-school-level mathematics and willingness to learn machine learning.
• Access to a personal laptop.
• Ability to participate in group activities.
• Applicants whose first language is not English must meet one of LSET’s stated requirements: IELTS 5.5, NCC Test or GCE O Level English C6.

LSET identifies related career paths:

• Machine Learning Engineer
• Data Scientist
• MLOps Engineer
• AI/ML Developer
• Data Analyst (Advanced Analytics)
• ML Research Engineer

Progression depends on individual skills, experience and further professional development.