Course Content
- Statistics and Data Fundamentals
- Why statistics matters in AI and machine learning
- Types of data
- Numerical vs categorical
- Continuous vs discrete
- Features vs target variables
- Understanding datasets
- Business examples of data
- Descriptive Statistics
- Mean
- Median
- Mode
- Range
- Variance
- Standard deviation
- Understanding data spread
- Outliers and their impact
- Data Distribution and Probability Basics
- What probability means
- Probability examples in business
- Independent vs dependent events
- Normal distribution
- Skewed distribution
- Understanding distributions with histograms
- Z-score concept
- Relationships Between Variables
- Scatterplots
- Correlation
- Positive vs negative correlation
- Strong vs weak correlation
- Correlation vs causation
- Introduction to regression concept
- Statistics for Machine Learning
- Train vs test data
- Why models make mistakes
- Overfitting vs underfitting
- Feature scaling concept
- Why data preparation matters
- Model Evaluation Metrics
- Classification metrics
- Confusion matrix
- True Positive (TP)
- True Negative (TN)
- False Positive (FP)
- False Negative (FN)
- Accuracy
- Precision
- Recall
- F1-score
- Regression metrics
- MAE
- MSE
- RMSE
- R² (R-squared)
Course Dates, Prices & Enrolment
Scroll right for more details
| Delivery Method | Dates & Times | Hours | Price | Enrolment |
Classroom Training
|
09 Jun 2026 - 11 Jun 2026
Tuesday & Thursday
10:00 AM - 01:00 PM
BT
| 6-hour over 2-day |
| Enrol Now |
Classroom Training
|
08 Jul 2026 - 15 Jul 2026
2 Wednesdays
10:00 AM - 01:00 PM
BT
| 6-hour over 2-day |
| Enrol Now |
Training venue: Unit 15, Boardman House, 64 Broadway, Stratford, London E15 1NT, United Kingdom
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Frequently Asked Questions
Do I need advanced maths knowledge for this course?
No, advanced mathematics is not required. This course focuses on practical statistics and mathematical concepts needed for machine learning and AI, explained in a beginner-friendly way.
Do I need programming knowledge for this course?
No programming knowledge is required. This course focuses on understanding concepts rather than coding and can be followed using Microsoft Excel or simple calculations.
What will I learn in this course?
You will learn key mathematical and statistical concepts commonly used in machine learning and AI, such as averages, probability, distributions, correlation, regression, model evaluation concepts, and data interpretation.
Why are statistics and maths important for machine learning and AI?
Statistics and mathematics help you understand data, identify patterns, evaluate model performance, interpret predictions, and understand how machine learning systems make decisions.
Is this course suitable for beginners?
Yes, the course is designed for beginners and explains concepts step by step using practical examples rather than heavy mathematical theory.
Will I learn probability in this course?
Yes, probability concepts commonly used in machine learning and AI are covered, including understanding uncertainty and making predictions from data.
Will I learn about machine learning evaluation concepts?
Yes, the course introduces concepts commonly used to evaluate machine learning models, helping you understand how model performance is measured.
Do I need Microsoft Excel for this course?
Microsoft Excel may be used for calculations and practical exercises, but many concepts can also be understood with simple examples and pen-and-paper exercises.
Will there be practical exercises during the course?
Yes, the course includes practical examples and exercises designed to help learners apply concepts in real-world machine learning and AI scenarios.
Is this course useful before learning Data Science or AI?
Yes, this course provides a strong foundation before progressing to areas such as data science, machine learning, deep learning, and AI application development.
How is the course delivered?
The course is instructor-led and can be attended live online, in person at our London training centre, or delivered on-site at your organisation for corporate training.
Will I receive a certificate after completing the course?
Yes, participants will receive a certificate of completion after successfully finishing the course.
What should I learn after completing this course?
After completing this course, you can progress to data analysis, machine learning, artificial intelligence, deep learning, and other advanced data and AI-related topics.
Can this course help me understand AI concepts better?
Yes, understanding statistical and mathematical foundations can make AI and machine learning concepts easier to understand and apply in real-world scenarios.