Machine Learning

Course Description

Machine Learning focuses on the knowledge, techniques and practical skills required to develop systems that can learn from data, identify patterns and make predictions or informed decisions.

The course introduces participants to fundamental Machine Learning concepts, data preparation, feature engineering, model development, training, evaluation and deployment.

Participants learn how to apply Python and relevant Machine Learning libraries to develop and evaluate models using real-world datasets, with practical applications in areas such as prediction, classification, clustering and business decision-making.

Course Overview

Machine Learning provides participants with the knowledge and practical skills required to understand how machines learn from data and how Machine Learning models can be developed to solve real-world problems.

The Machine Learning training at Renzy Academy introduces participants to the foundations of Machine Learning, including supervised and unsupervised learning, data preparation, feature engineering, model selection, model training, evaluation and optimisation.

Participants will use Python and commonly used Machine Learning libraries to work with datasets, build models and evaluate their performance.

The programme covers practical Machine Learning techniques including regression, classification, clustering and model evaluation. Participants will also be introduced to important concepts such as overfitting, underfitting, feature selection and model optimisation.

Through hands-on exercises, practical datasets, case studies and a Machine Learning project, participants will develop experience applying Machine Learning techniques to realistic problems.

The programme is suitable for individuals with a foundation in Python and Data Analysis who want to progress into Machine Learning and related technology fields.

Who This Course Is For

The Machine Learning programme is designed for individuals who want to develop practical skills in building and applying Machine Learning models to real-world problems.

It is suitable for

• Aspiring Machine Learning Engineers
• Data Analysts
• Aspiring Data Scientists
• Python Developers
• Software Developers
• Data Engineers
• Business Analysts
• Technology Professionals
• Researchers and Academics
• AI and Technology Enthusiasts
• Professionals working with data
• Graduates in technology, science, mathematics or related fields
• Professionals transitioning into Data Science or Machine Learning
• Individuals with a foundation in Python and Data Analysis

Key Learner Outcomes

By the end of the programme, participants should be able to

• Explain fundamental Machine Learning concepts and terminology.
• Prepare and transform datasets for Machine Learning.
• Distinguish between supervised and unsupervised learning approaches.
• Build and train basic Machine Learning models using Python.
• Apply regression, classification and clustering techniques.
• Evaluate model performance using appropriate metrics.
• Identify and address issues such as overfitting and underfitting.
• Select relevant features and improve model performance.
• Interpret Machine Learning results and communicate findings.
• Apply Machine Learning techniques to practical real-world problems.
• Develop a practical Machine Learning project for their portfolio.

Learning Objectives

By the end of the course, participants will be able to

• Explain fundamental Machine Learning concepts and understand how Machine Learning is applied to real-world problems.
• Prepare, clean and transform datasets for Machine Learning using appropriate data preparation techniques.
• Build and train Machine Learning models using Python and relevant Machine Learning libraries.
• Apply supervised and unsupervised learning techniques, including regression, classification and clustering.
• Evaluate and interpret model performance using appropriate metrics and validation techniques.
• Identify and address common Machine Learning challenges, including overfitting, underfitting and poor-quality data.
• Develop a practical Machine Learning solution that demonstrates the ability to apply Machine Learning techniques to a real-world problem.

Course Modules

Practical Project

Participants will work on a realistic Machine Learning problem and apply the concepts, tools and techniques covered throughout the programme.

Working individually or in teams, participants will take a dataset through the Machine Learning workflow, from problem definition and data preparation to model development, evaluation and presentation.

The practical project will involve

• Defining a real-world problem suitable for Machine Learning
• Identifying the required dataset
• Cleaning and preparing the data
• Performing exploratory data analysis
• Selecting and engineering relevant features
• Selecting an appropriate Machine Learning approach
• Training a Machine Learning model
• Evaluating model performance
• Comparing models where appropriate
• Improving model performance
• Interpreting the results
• Developing a basic model deployment approach
• Preparing technical documentation
• Presenting the Machine Learning solution
• Practical Deliverables
• By the end of the project, participants will have developed a portfolio of practical Machine Learning deliverables, which may include:
• Problem Definition
• Dataset
• Data Cleaning and Preparation
• Exploratory Data Analysis
• Feature Engineering
• Machine Learning Model
• Model Evaluation
• Model Comparison
• Performance Metrics
• Model Improvement Report
• Python Notebook/Source Code
• Technical Documentation
• Final Presentation
• The practical project is designed to help participants move from understanding Machine Learning concepts to applying them to a realistic problem.

Duration & Format

Duration

8 Weeks

Delivery Format

The programme is available through

• Live Online Instructor-Led Training
• In-Person Classroom Training
• Instructor-led demonstrations
• Hands-on coding exercises
• Practical datasets
• Guided Machine Learning exercises
• Case studies
• Model development workshops
• Individual and group activities
• Machine Learning tools and libraries
• Practical assignments
• Hands-on Machine Learning project
• Code review and facilitator feedback
• Final project presentation

Entry Requirements

The Machine Learning programme is designed for learners who already have some technical foundation.

Participants should have

• Basic computer literacy
• Basic understanding of Data Analysis
• Basic Python programming knowledge
• Basic understanding of statistics is recommended
• A willingness to practise programming and work with datasets
• Previous professional Machine Learning experience is not required.

Participants without Python or Data Analysis experience are encouraged to complete the relevant foundational programmes before starting Machine Learning.

Certification

• Participants who successfully complete the programme will receive a Renzy Academy Certificate of Completion in Machine Learning.
• The certificate recognises successful completion of the training programme and practical Machine Learning project.
• Participants will also develop a practical Machine Learning project that can form part of their professional technology portfolio.

Career Opportunities

Machine Learning skills can support career pathways across technology, finance, healthcare, telecommunications, research, e-commerce, consulting and other data-driven industries.

Potential roles include

• Machine Learning Engineer
• Junior Machine Learning Engineer
• Machine Learning Developer
• Data Scientist
• Junior Data Scientist
• Data Analyst
• Data Engineer
• AI Developer
• AI/ML Analyst
• Research Analyst
• Predictive Analytics Specialist
• Business Intelligence Analyst
• Data Science Consultant
• Machine Learning Consultant

The specific career pathway will depend on the participant's additional knowledge and experience in areas such as Python, statistics, Data Analysis, software development, databases, cloud computing and Artificial Intelligence.