UNLOCK THIS COURSE AND 3000+ OTHER COURSES FOR ONLY £99! FIND OUT MORE
Course Overview
Structured into step-by-step modules, this Data Science & Machine Learning course covers essential topics, including:
- Introduction to data science and machine learning concepts
- Python programming for data analysis
- Working with NumPy, Pandas, and Matplotlib
- Understanding and loading real-world datasets
- Data visualisation and statistical exploration
- Data preprocessing and feature engineering
- Model training, testing, and evaluation techniques
- Classification and regression algorithms
- Model comparison and selection
- Machine learning pipelines and performance improvement
- Parameter tuning and ensemble methods
- Finalising and deploying machine learning models
Each module develops your understanding of the data science lifecycle, from raw data to real-time prediction models.
Whether you want to become a data analyst, explore machine learning careers, or apply data-driven decision-making in business environments, this course provides the structured knowledge required for modern data roles.
By completing this CPD-accredited Data Science & Machine Learning course, you will gain recognised knowledge that supports Continuing Professional Development and career progression in the data and technology sectors.
Enrol Now and Take the First Step Towards Your Data Science Career!
Learning Outcomes
By the end of this CPD-accredited Data Science & Machine Learning course, you will:
- Understand the core concepts of data science and machine learning
- Learn Python for data analysis and modelling
- Work with NumPy and Pandas for data manipulation
- Create data visualisations using Matplotlib
- Understand data preprocessing and feature selection techniques
- Apply classification and regression algorithms
- Evaluate machine learning models using performance metrics
- Compare algorithms and select the best model
- Understand machine learning pipelines and optimisation methods
- Gain knowledge of real-world predictive modelling workflows
Why Enrol in This Course?
By enrolling in this CPD-accredited Data Science & Machine Learning course UK, you will gain structured knowledge aligned with modern industry requirements. This flexible online training allows you to study at your own pace while developing skills in data analysis, machine learning, and predictive modelling.
With 24/7 access to learning materials, this course is ideal for learners preparing for entry-level data roles, business intelligence positions, or technical career progression.
Who is This Course for?
This course is ideal for:
- Beginners in data science and machine learning
- Aspiring data analysts and data scientists
- IT and software professionals
- Business and finance professionals working with data
- Students exploring AI and machine learning careers
- Individuals seeking CPD in data science
Join thousands of learners who are developing their careers with this CPD-accredited programme.
Prerequisites for This Course
There are no specific entry requirements for this course. It is open to:
- Beginners in Python and data science
- Professionals upgrading their technical skills
- Career changers entering the data field
- Anyone interested in machine learning and analytics
Certification
Upon completing the course and passing the final exam, you will receive a CPD QS Accredited Certificate from Apex Learning. This certification demonstrates your qualifications and skills. The Digital version of the certificate is available for a discounted price of £9.99 only, and you can order a hard copy of the certificate for a discounted price of £14.99 only.
📍Note: Discounted certificate pricing is available for a limited time only. Secure yours before the offer ends!
Career Path
Completing the Data Science & Machine Learning course opens up opportunities in data-driven and technology roles, including:
Data Analyst
Analyse datasets to identify trends and support business decisions.
Average UK salary: £30,000 – £50,000 per year.
Junior Data Scientist
Support predictive modelling and machine learning workflows.
Average UK salary: £35,000 – £55,000 per year.
Business Intelligence Analyst
Transform data into actionable insights for organisations.
Average UK salary: £32,000 – £52,000 per year.
Machine Learning Assistant
Assist in data preparation, model evaluation, and performance optimisation.
Average UK salary: £35,000 – £60,000 per year.
Pathway for Progression
With this data science and machine learning course, you can begin your learning journey in data-driven technologies and predictive analytics and develop the knowledge required to work with real-world datasets and machine learning models. With further learning and practical experience, you may progress towards specialist roles in data science, artificial intelligence, business intelligence, or advanced analytics across a wide range of industries.
Data Science & Machine Learning with Python | CPD Accredited Course Online – UK
Looking to build in-demand skills in data science and machine learning and understand how organisations use data for intelligent decision-making? This best-selling, CPD-accredited Data Science & Machine Learning course is designed to help you develop a strong foundation in Python programming, data analysis, data visualisation, and machine learning models—entirely online and at your own pace.
With the UK data economy contributing over £300 billion annually, professionals with data science and machine learning skills are in high demand across finance, healthcare, retail, marketing, cybersecurity, and technology sectors. Businesses rely on data scientists and machine learning workflows to identify trends, build predictive models, and drive innovation.
Gain industry-relevant knowledge in Python for data science, NumPy, Pandas, Matplotlib, data preprocessing, feature selection, model evaluation, classification and regression algorithms, and performance optimisation techniques—everything you need to understand modern data science processes.
If you’re searching for a data science and machine learning course online, a machine learning with Python course, or a data analyst and data science training programme in the UK, this comprehensive course provides the complete theoretical and software-based foundation.
Enrol today and start building your future in data science and machine learning.
Data Science & Machine Learning Course Overview
Structured into step-by-step modules, this Data Science & Machine Learning course covers essential topics, including:
- Introduction to data science and machine learning concepts
- Python programming for data analysis
- Working with NumPy, Pandas, and Matplotlib
- Understanding and loading real-world datasets
- Data visualisation and statistical exploration
- Data preprocessing and feature engineering
- Model training, testing, and evaluation techniques
- Classification and regression algorithms
- Model comparison and selection
- Machine learning pipelines and performance improvement
- Parameter tuning and ensemble methods
- Finalising and deploying machine learning models
Each module develops your understanding of the data science lifecycle, from raw data to real-time prediction models.
Whether you want to become a data analyst, explore machine learning careers, or apply data-driven decision-making in business environments, this course provides the structured knowledge required for modern data roles.
By completing this CPD-accredited Data Science & Machine Learning course, you will gain recognised knowledge that supports Continuing Professional Development and career progression in the data and technology sectors.
Enrol Now and Take the First Step Towards Your Data Science Career!
Learning Outcomes of This Data Science & Machine Learning Course
By the end of this CPD-accredited Data Science & Machine Learning course, you will:
- Understand the core concepts of data science and machine learning
- Learn Python for data analysis and modelling
- Work with NumPy and Pandas for data manipulation
- Create data visualisations using Matplotlib
- Understand data preprocessing and feature selection techniques
- Apply classification and regression algorithms
- Evaluate machine learning models using performance metrics
- Compare algorithms and select the best model
- Understand machine learning pipelines and optimisation methods
- Gain knowledge of real-world predictive modelling workflows
Why Enrol in This Data Science & Machine Learning Course?
By enrolling in this CPD-accredited Data Science & Machine Learning course UK, you will gain structured knowledge aligned with modern industry requirements. This flexible online training allows you to study at your own pace while developing skills in data analysis, machine learning, and predictive modelling.
With 24/7 access to learning materials, this course is ideal for learners preparing for entry-level data roles, business intelligence positions, or technical career progression.
Who Is This Data Science & Machine Learning Course For?
This course is ideal for:
- Beginners in data science and machine learning
- Aspiring data analysts and data scientists
- IT and software professionals
- Business and finance professionals working with data
- Students exploring AI and machine learning careers
- Individuals seeking CPD in data science
Join thousands of learners who are developing their careers with this CPD-accredited programme.
Prerequisites for This Data Science & Machine Learning Course
There are no specific entry requirements for this course. It is open to:
- Beginners in Python and data science
- Professionals upgrading their technical skills
- Career changers entering the data field
- Anyone interested in machine learning and analytics
Assessment Method
Assessment for the course is conducted through an automated multiple-choice exam. A score of 60% is required to pass and earn the CPD Accredited Certificate. Reflective assignments are provided to enhance your learning, with expert tutor feedback available.
Certification
Upon completing the course and passing the final exam, you will receive a CPD QS Accredited Certificate from Apex Learning. This certification demonstrates your qualifications and skills. The Digital version of the certificate is available for a discounted price of £9.99 only, and you can order a hard copy of the certificate for a discounted price of £14.99 only.
📍Note: Discounted certificate pricing is available for a limited time only. Secure yours before the offer ends!
Career Path
Completing the Data Science & Machine Learning course opens up opportunities in data-driven and technology roles, including:
Data Analyst
Analyse datasets to identify trends and support business decisions.
Average UK salary: £30,000 – £50,000 per year.
Junior Data Scientist
Support predictive modelling and machine learning workflows.
Average UK salary: £35,000 – £55,000 per year.
Business Intelligence Analyst
Transform data into actionable insights for organisations.
Average UK salary: £32,000 – £52,000 per year.
Machine Learning Assistant
Assist in data preparation, model evaluation, and performance optimisation.
Average UK salary: £35,000 – £60,000 per year.
Pathway for Progression
With this data science and machine learning course, you can begin your learning journey in data-driven technologies and predictive analytics and develop the knowledge required to work with real-world datasets and machine learning models. With further learning and practical experience, you may progress towards specialist roles in data science, artificial intelligence, business intelligence, or advanced analytics across a wide range of industries.
Frequestly Asked Questions
Is this Data Science & Machine Learning course recognised in the UK?
Yes, this course is CPD QS accredited and recognised for Continuing Professional Development. It demonstrates industry-relevant knowledge used in data science, analytics, and machine learning roles.
Do I need programming experience to enrol?
No prior programming experience is required to join this course. It starts with Python fundamentals and gradually introduces data science and machine learning concepts.
Can I study this data science course online at my own pace?
Yes, the course is delivered fully online with flexible access. You can learn at your own pace and revisit the materials anytime.
What career paths can this course support?
This course supports progression into data analyst, junior data scientist, business intelligence, and machine learning support roles depending on your experience and further learning.
How is the course assessed?
The course is assessed through an automated multiple-choice examination. You must achieve a 60% pass mark to complete the course.
How long does it take to complete the course?
The course is self-paced, so completion time depends on your learning schedule and availability.
Course Curriculum
- 4 sections
- 8 lectures
- 00:00:00 total length
-
Course Overview & Table of Contents 00:09:00 -
Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types 00:05:00
-
Introduction to Machine Learning – Part 2 – Classifications and Applications 00:06:00
-
System and Environment preparation – Part 1 00:08:00
-
System and Environment preparation – Part 2 00:06:00
-
Learn Basics of python – Assignment 1 00:10:00
-
Learn Basics of python – Assignment 2 00:09:00
-
Learn Basics of python – Functions 00:04:00
-
Learn Basics of python – Data Structures 00:12:00
-
Learn Basics of NumPy – NumPy Array 00:06:00
-
Learn Basics of NumPy – NumPy Data 00:08:00
-
Learn Basics of NumPy – NumPy Arithmetic 00:04:00
-
Learn Basics of Matplotlib 00:07:00
-
Learn Basics of Pandas – Part 1 00:06:00
-
Learn Basics of Pandas – Part 2 00:07:00
-
Understanding the CSV data file 00:09:00
-
Load and Read CSV data file using Python Standard Library 00:09:00
-
Load and Read CSV data file using NumPy 00:04:00
-
Load and Read CSV data file using Pandas 00:05:00
-
Dataset Summary – Peek, Dimensions and Data Types 00:09:00
-
Dataset Summary – Class Distribution and Data Summary 00:09:00
-
Dataset Summary – Explaining Correlation 00:11:00
-
Dataset Summary – Explaining Skewness – Gaussian and Normal Curve 00:07:00
-
Dataset Visualization – Using Histograms 00:07:00
-
Dataset Visualization – Using Density Plots 00:06:00
-
Dataset Visualization – Box and Whisker Plots 00:05:00
-
Multivariate Dataset Visualization – Correlation Plots 00:08:00
-
Multivariate Dataset Visualization – Scatter Plots 00:05:00
-
Data Preparation (Pre-Processing) – Introduction 00:09:00
-
Data Preparation – Re-scaling Data – Part 1 00:09:00
-
Data Preparation – Re-scaling Data – Part 2 00:09:00
-
Data Preparation – Standardizing Data – Part 1 00:07:00
-
Data Preparation – Standardizing Data – Part 2 00:04:00
-
Data Preparation – Normalizing Data 00:08:00
-
Data Preparation – Binarizing Data 00:06:00
-
Feature Selection – Introduction 00:07:00
-
Feature Selection – Uni-variate Part 1 – Chi-Squared Test 00:09:00
-
Feature Selection – Uni-variate Part 2 – Chi-Squared Test 00:10:00
-
Feature Selection – Recursive Feature Elimination 00:11:00
-
Feature Selection – Principal Component Analysis (PCA) 00:09:00
-
Feature Selection – Feature Importance 00:07:00
-
Refresher Session – The Mechanism of Re-sampling, Training and Testing 00:12:00
-
Algorithm Evaluation Techniques – Introduction 00:07:00
-
Algorithm Evaluation Techniques – Train and Test Set 00:11:00
-
Algorithm Evaluation Techniques – K-Fold Cross Validation 00:09:00
-
Algorithm Evaluation Techniques – Leave One Out Cross Validation 00:05:00
-
Algorithm Evaluation Techniques – Repeated Random Test-Train Splits 00:07:00
-
Algorithm Evaluation Metrics – Introduction 00:09:00
-
Algorithm Evaluation Metrics – Classification Accuracy 00:08:00
-
Algorithm Evaluation Metrics – Log Loss 00:03:00
-
Algorithm Evaluation Metrics – Area Under ROC Curve 00:06:00
-
Algorithm Evaluation Metrics – Confusion Matrix 00:10:00
-
Algorithm Evaluation Metrics – Classification Report 00:04:00
-
Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction 00:06:00
-
Algorithm Evaluation Metrics – Mean Absolute Error 00:07:00
-
Algorithm Evaluation Metrics – Mean Square Error 00:03:00
-
Algorithm Evaluation Metrics – R Squared 00:04:00
-
Classification Algorithm Spot Check – Logistic Regression 00:12:00
-
Classification Algorithm Spot Check – Linear Discriminant Analysis 00:04:00
-
Classification Algorithm Spot Check – K-Nearest Neighbors 00:05:00
-
Classification Algorithm Spot Check – Naive Bayes 00:04:00
-
Classification Algorithm Spot Check – CART 00:04:00
-
Classification Algorithm Spot Check – Support Vector Machines 00:05:00
-
Regression Algorithm Spot Check – Linear Regression 00:08:00
-
Regression Algorithm Spot Check – Ridge Regression 00:03:00
-
Regression Algorithm Spot Check – Lasso Linear Regression 00:03:00
-
Regression Algorithm Spot Check – Elastic Net Regression 00:02:00
-
Regression Algorithm Spot Check – K-Nearest Neighbors 00:06:00
-
Regression Algorithm Spot Check – CART 00:04:00
-
Regression Algorithm Spot Check – Support Vector Machines (SVM) 00:04:00
-
Compare Algorithms – Part 1 : Choosing the best Machine Learning Model 00:09:00
-
Compare Algorithms – Part 2 : Choosing the best Machine Learning Model 00:05:00
-
Pipelines : Data Preparation and Data Modelling 00:11:00
-
Pipelines : Feature Selection and Data Modelling 00:10:00
-
Performance Improvement: Ensembles – Voting 00:07:00
-
Performance Improvement: Ensembles – Bagging 00:08:00
-
Performance Improvement: Ensembles – Boosting 00:05:00
-
Performance Improvement: Parameter Tuning using Grid Search 00:08:00
-
Performance Improvement: Parameter Tuning using Random Search 00:06:00
-
Export, Save and Load Machine Learning Models : Pickle 00:10:00
-
Export, Save and Load Machine Learning Models : Joblib 00:06:00
-
Finalizing a Model – Introduction and Steps 00:07:00
-
Finalizing a Classification Model – The Pima Indian Diabetes Dataset 00:07:00
-
Quick Session: Imbalanced Data Set – Issue Overview and Steps 00:09:00
-
Iris Dataset : Finalizing Multi-Class Dataset 00:09:00
-
Finalizing a Regression Model – The Boston Housing Price Dataset 00:08:00
-
Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00
-
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00
-
Real-time Predictions: Using the Boston Housing Regression Model 00:08:00
Course Overview & Table of Contents -
Resources – Data Science & Machine Learning with Python 00:00:00
-
Claim Your Certificate 00:00:00
-
Thank you for completing your course with us! We’d appreciate your feedback! 00:00:00
£21.99
£419
Save 95% - Sale Ends in
TAKE ALL COURSES FOR £99
This course includes:
-
Duration:10 hours, 24 minutes
-
Access:1 year access
-
Level:
-
CPD Points:10