Course ID:

2501060102453LUEMT

Course Dates :

06/01/25

Course Duration :

25 Studying Day/s

Course Location:

Course Category:

Executive Diploma

Course Subcategories:

Technology and Innovation

Computer Vision
Data Analytics
Deep Learning
Ethical AI
Machine Learning
Natural Language Processing
Robotics

Course Certified By:

* LondonUni - Executive Management Training

* Executive Diploma Certificate
Leading to:
Executive Mini Masters Certificate
Leading to
Executive Masters Certificate

Certification Will Be Issued From : From London, United Kingdom

Course Information

Introduction

Artificial Intelligence (AI) stands as a transformative force reshaping industries, redefining workflows, and expanding human potential. Mastery in AI development and engineering equips professionals with the ability to design, develop, and deploy intelligent systems that solve complex problems and unlock innovation across various sectors. This comprehensive Executive Diploma delves into the core areas of AI, providing participants with robust technical knowledge and practical expertise to thrive in this rapidly evolving domain.

The program focuses on critical components of AI development, including programming foundations, machine learning principles, neural networks, and natural language processing (NLP). By emphasizing both theory and application, participants gain a holistic understanding of how AI systems operate, from foundational algorithms to advanced deep-learning models. Through hands-on exercises and industry-relevant projects, learners will bridge the gap between conceptual knowledge and real-world implementation, ensuring readiness for advanced AI roles.

Participants will develop proficiency in Python, the industry-standard programming language for AI development, as they navigate the creation of efficient and scalable AI solutions. This is followed by a deep dive into machine learning basics, where learners will explore essential techniques for training models and extracting actionable insights from data. The program progresses to neural networks and deep learning, equipping participants with skills to design, optimize, and deploy cutting-edge AI architectures. The final segment on NLP empowers learners to engineer systems capable of interpreting and generating human language, a pivotal component of modern AI applications.

The Executive Diploma incorporates a rigorous 20-day curriculum, complemented by a 5-day thesis preparation phase. During this final phase, participants consolidate their learning by conducting a comprehensive AI project, producing a 4,500–5,000-word thesis that showcases their technical skills, critical thinking, and innovative problem-solving abilities. Guided by expert mentors, learners will create tangible outputs that demonstrate their mastery of AI development and engineering principles.

This program is meticulously crafted to meet the needs of professionals aiming to excel in AI-centric roles. It is especially suited for software developers, data scientists, engineers, and tech leaders seeking to deepen their understanding of AI and its applications. Managers and business strategists looking to integrate AI solutions into organizational frameworks will also benefit significantly. The Executive Diploma fosters a collaborative learning environment where participants share insights and network with peers, enhancing both individual growth and collective expertise.

By the end of this program, participants will have developed a strategic vision for AI implementation, equipped with the tools to innovate and drive success in the AI-driven world. This Executive Diploma not only builds technical competencies but also instills a forward-thinking mindset essential for navigating the challenges and opportunities of AI advancements.

Course Structure

Programming for AI with Python (5 Days)
Machine Learning Basics (5 Days)
Neural Networks and Deep Learning (5 Days)
Natural Language Processing (NLP) (5 Days)
Thesis Preparation and Submission (5 Days)

This Executive Diploma in AI Development and Engineering is a transformative learning experience, empowering professionals to lead AI innovation with confidence and expertise.

Objectives

By completing this diploma, participants will:
Objectives

Master Programming for AI: Gain in-depth knowledge of Python programming for AI applications, focusing on libraries and tools essential for efficient AI development.
Understand Machine Learning Fundamentals: Learn to build, train, and evaluate machine learning models using diverse datasets and methodologies.
Develop Expertise in Neural Networks: Explore the architecture and functionality of neural networks, with practical experience in designing deep learning models.
Harness Natural Language Processing (NLP): Engineer systems that process, analyze, and generate human language with advanced NLP techniques.
Build Real-World AI Solutions: Apply theoretical knowledge to solve industry-relevant problems, ensuring practical readiness for AI-centric roles.
Thesis Development: Demonstrate mastery by developing a 4,500–5,000-word thesis showcasing technical expertise, research skills, and innovation in AI.

Who Should Attend?

This diploma is ideal for:

Software developers seeking specialization in AI development.
Data scientists aiming to expand their skill set with advanced AI techniques.
Engineers transitioning to AI-focused roles or seeking to enhance their technical capabilities.
Business leaders and strategists looking to incorporate AI-driven solutions into their organizations.
Tech enthusiasts and professionals committed to staying ahead in the AI revolution.

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

Each 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

Part 2 / 5

Machine Learning Basics

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

Part 3 / 5

Neural Networks and Deep Learning

Day 1:
Foundations of Neural Networks

Introduction to Artificial Intelligence and Machine Learning
Anatomy of a Neural Network: Layers, Nodes, and Weights
Understanding Activation Functions and Loss Functions
Hands-On Exercise: Building a Simple Perceptron Model


Day 2:
Deep Learning Architectures

Overview of Feedforward Neural Networks (FNNs)
Convolutional Neural Networks (CNNs): Theory and Applications
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Models
Case Study: Image Recognition Using CNNs


Day 3:
Advanced Techniques and Tools

Transfer Learning: Leveraging Pre-Trained Models
Hyperparameter Tuning and Optimization Strategies
Introduction to Generative Adversarial Networks (GANs)
Practical Session: Fine-Tuning a GAN for Image Generation


Day 4:
Evaluation and Deployment

Metrics for Assessing Model Performance
Cross-Validation and Overfitting Prevention Techniques
Deploying Deep Learning Models in Production Environments
Workshop: End-to-End Project Development


Day 5:
Ethics and Future Trends

Ethical Considerations in AI: Bias, Fairness, and Transparency
Interpretable AI and Explainable Models
Emerging Trends in Neural Networks and Deep Learning
Panel Discussion: Real-World Challenges and Opportunities

Part 4 / 5

Natural Language Processing (NLP)

Day 1: Introduction to NLP and Text Preprocessing

Overview of Natural Language Processing and its real-world applications.
Understanding text data: tokens, vocabulary, and corpora.
Text preprocessing: tokenization, stemming, lemmatization, and stopword removal.
Hands-on session: Cleaning and preparing textual datasets using Python and libraries like NLTK and SpaCy.

Day 2: Core NLP Techniques

Word embeddings: Word2Vec, GloVe, and FastText.
Feature extraction methods: TF-IDF and Bag of Words (BoW).
Text classification techniques: Naïve Bayes and Support Vector Machines (SVM).
Practical exercise: Building a spam detection model.

Day 3: Advanced NLP Techniques

Introduction to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM).
Sentiment analysis using deep learning models.
Sequence-to-sequence models for language translation.
Project implementation: Building an LSTM-based sentiment analysis model.

Day 4: Transformers and Modern NLP Architectures

Understanding transformers and attention mechanisms.
Exploring BERT, GPT, and other pre-trained models.
Fine-tuning transformers for specific tasks.
Hands-on activity: Fine-tuning BERT for text classification.

Day 5: NLP Applications, Trends, and Ethics

Applications of NLP in chatbots, summarization, and language generation.
Ethical considerations in NLP: Bias, fairness, and responsible AI use.
Future trends in NLP and large language models.
Capstone project: Building an end-to-end NLP solution.

Part 5 / 5 Executive Diploma Thesis

As part of this Programme, participants are required to complete and submit an Executive Diploma in AI Development and Engineering Thesis as a critical component of their learning journey.

This thesis serves as a demonstration of the participant’s ability to apply theoretical knowledge to real-world challenges and propose innovative solutions.

To ensure depth and rigor in analysis, the thesis must now be between 6,000 and 7,000 Words (excluding references, appendices, and cover pages).

This updated word count reflects the program’s commitment to fostering comprehensive research and advanced problem-solving skills while remaining achievable within the allocated study time.

Submissions that fall outside this range will not be accepted.

The Executive Mini Masters program Thesis is a mandatory requirement for graduation, and without its successful submission and approval, participants will not be eligible to graduate from the program or receive their Executive Programme certificate .