Course ID:
2505260101267ESH
Course Dates :
26/05/25
Course Duration :
5 Studying Day/s
Course Location:
London
UK
Course Category:
Professional and CPD Training Programs
Course Subcategories:
Finance and Accounting
Health, Safety, and Wellbeing
Leadership and Management
Technology and Innovation
Artificial Intelligence
Automation and Robotics
Business Analytics
Cybersecurity
Data Science
Financial Modeling
Healthcare Technology
Course Certified By:
* ESHub CPD
&
* LondonUni - Executive Management Training
* Professional Training and CPD Programs
Leading to:
Executive Diploma Certificate
Leading to:
Executive Mini Masters Certificate
Leading to
Executive Masters Certificate
Certification Will Be Issued From : From London, United Kingdom
Course Fees:
£5,120.30
Please Note :
Your £250.00 Deposit will be deducted from the total invoice Amount.
To commence the registration process for your training course, please follow the link provided and proceed with; Upon successful payment, we will promptly contact you to finalize your enrollment and issue a confirmation of your guaranteed placement.
- Vat Not Included in the price.
- VAT may vary depending on the country where the course or workshop is held.
Course Information
Introduction
Machine learning is transforming industries, enabling businesses to make smarter decisions by leveraging data. This course provides a comprehensive introduction to the fundamentals of machine learning, focusing on practical applications and key concepts. Participants will gain insights into algorithms, tools, and techniques essential for building predictive models and solving real-world problems.
By the end of the course, attendees will have a foundational understanding of machine learning concepts and be equipped with the skills to start exploring machine learning projects confidently.
This course provides a strong foundation for those starting their journey in machine learning while incorporating practical, hands-on learning to ensure immediate applicability in professional scenarios.
Objectives
By the end of this course, participants will:
Understand the core principles and techniques of machine learning.
Gain hands-on experience in building, training, and evaluating machine learning models.
Learn to identify and apply appropriate machine learning algorithms for specific problems.
Understand data preprocessing techniques and their importance in model accuracy.
Explore ethical considerations and best practices in machine learning projects.
Who Should Attend?
This course is designed for:
Professionals and practitioners who want to understand the basics of machine learning.
Data analysts and IT professionals looking to expand their skillset.
Business leaders and managers interested in leveraging machine learning for strategic decision-making.
Students or individuals seeking a career in data science or AI.
Anyone curious about machine learning and its practical applications.
Training Method
• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a 7” Tablet containing a copy of the presentation, slides and handouts
• Post-assessment
Program Support
This program is supported by:
* Interactive discussions
* Role-play
* Case studies and highlight the techniques available to the participants.
Daily Agenda
The course agenda will be as follows:
• Technical Session 08.30-10.00 am
• Coffee Break 10.00-10.15 am
• Technical Session 10.15-12.15 noon
• Coffee Break 12.15-12.45 pm
• Technical Session 12.45-02.30 pm
• Course Ends 02.30 pm
Course Outlines
Week 1
Day 1: Introduction to Machine Learning
Overview of Machine Learning and its Applications
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Concepts: Features, Labels, and Datasets
Setting Up a Machine Learning Environment
Day 2: Data Preprocessing and Exploration
Importance of Data Preprocessing in Machine Learning
Techniques: Cleaning, Normalization, and Feature Scaling
Data Visualization and Insights Extraction
Splitting Data: Training, Validation, and Testing Sets
Day 3: Core Machine Learning Algorithms
Linear Regression and Logistic Regression
Decision Trees and Random Forests
K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)
Hands-on Session: Building a Simple Predictive Model
Day 4: Model Evaluation and Optimization
Metrics for Evaluating Model Performance (Accuracy, Precision, Recall, F1 Score)
Understanding Overfitting and Underfitting
Hyperparameter Tuning Techniques
Cross-Validation Methods
Day 5: Practical Applications and Ethical Considerations
Real-World Applications of Machine Learning (e.g., Healthcare, Finance, Marketing)
Tools and Libraries: Scikit-learn, TensorFlow, and PyTorch
Ethical Issues in Machine Learning: Bias, Fairness, and Privacy
Final Project: Developing and Evaluating a Basic Machine Learning Model
Overview of Machine Learning and its Applications
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Concepts: Features, Labels, and Datasets
Setting Up a Machine Learning Environment
Day 2: Data Preprocessing and Exploration
Importance of Data Preprocessing in Machine Learning
Techniques: Cleaning, Normalization, and Feature Scaling
Data Visualization and Insights Extraction
Splitting Data: Training, Validation, and Testing Sets
Day 3: Core Machine Learning Algorithms
Linear Regression and Logistic Regression
Decision Trees and Random Forests
K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)
Hands-on Session: Building a Simple Predictive Model
Day 4: Model Evaluation and Optimization
Metrics for Evaluating Model Performance (Accuracy, Precision, Recall, F1 Score)
Understanding Overfitting and Underfitting
Hyperparameter Tuning Techniques
Cross-Validation Methods
Day 5: Practical Applications and Ethical Considerations
Real-World Applications of Machine Learning (e.g., Healthcare, Finance, Marketing)
Tools and Libraries: Scikit-learn, TensorFlow, and PyTorch
Ethical Issues in Machine Learning: Bias, Fairness, and Privacy
Final Project: Developing and Evaluating a Basic Machine Learning Model
