Course Title:

Executive Diploma in AI Development and Engineering

Course ID:

02 030225 0102 676LUI

Course Dates :

03/02/25

 To

07/03/25

Course Duration :

25 Studying Day/s

Course Location:

London

UK

Course Fees GBP:

£23,047.33

• Vat Not Included in the price.
• VAT may vary depending on the country where the course or workshop is held.
  

Course Category:

Executive Diploma

Artificial intelligence

Course Certified By:

* LondonUni
For Executive Management Training

* Executive Diploma Certificate

==> Leading to : Executive Mini Masters Certificate
==> Leading to : Executive Masters Certificate

Certification Will Be Issued From :

United Kingdom

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.

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.

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.

4 Courses and Thesis
Part 2

2 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

4 Courses and Thesis
Part 3

3 Neural Networks and Deep Learning

Day 1: Introduction to Neural Networks and Deep Learning

Overview of Neural Networks and their Importance in AI.
Understanding the Biological Inspiration behind Neural Networks.
Fundamentals of Perceptrons and Multilayer Perceptrons (MLPs).
Key Concepts: Activation Functions, Weights, and Bias.

Day 2: Training Neural Networks

Forward Propagation and Backpropagation Algorithms.
Loss Functions and Optimization Techniques (e.g., Gradient Descent).
Avoiding Overfitting: Regularization Techniques and Dropout.
Evaluating Model Performance with Metrics.

Day 3: Deep Learning and Advanced Architectures

Introduction to Deep Neural Networks (DNNs).
Convolutional Neural Networks (CNNs) for Image Processing.
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks.
Autoencoders and their Applications in Dimensionality Reduction.

Day 4: Tools and Frameworks for Neural Networks

Overview of Popular Deep Learning Libraries: TensorFlow, Keras, and PyTorch.
Building and Training Neural Networks with TensorFlow and Keras.
Practical Implementation: Case Study on Image Recognition.
Debugging and Optimizing Deep Learning Models.

Day 5: Applications and Future Trends

Neural Networks in Natural Language Processing (NLP).
Leveraging Neural Networks for Predictive Analytics.
Ethical Considerations and Challenges in Deep Learning.
Future Trends in Neural Networks and AI.

4 Courses and Thesis
Part 4

4 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.

Thesis

4,500 - 5,000 Words

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.