Diploma In AI Engineering – Learnatic Academy

About Course

The Diploma in AI Engineering is a professionally designed programme that equips learners with the knowledge, technical expertise, and practical skills required to design, develop, deploy, and manage artificial intelligence solutions in modern professional environments. Artificial Intelligence has emerged as one of the most transformative technologies of the digital age, reshaping industries through automation, intelligent decision making, predictive analytics, natural language processing, computer vision, and advanced machine learning systems. This diploma provides learners with a comprehensive understanding of AI technologies while developing the practical competencies necessary to contribute effectively to AI driven projects and initiatives.

As organizations across sectors increasingly integrate artificial intelligence into their operations, the demand for skilled AI professionals continues to expand globally. Businesses, healthcare institutions, financial organizations, educational providers, technology companies, manufacturing enterprises, and government agencies are actively seeking professionals capable of implementing intelligent systems that improve efficiency, enhance customer experiences, and support innovation. This diploma prepares learners to understand the principles behind artificial intelligence while gaining practical experience with industry relevant tools, methodologies, and development practices.

The programme combines academic foundations with applied technical training to ensure learners acquire both theoretical understanding and practical engineering capabilities. Learners will explore key areas including artificial intelligence fundamentals, machine learning, deep learning, neural networks, natural language processing, computer vision, data preparation, model development, AI deployment, automation systems, and responsible AI practices. Through practical assignments, real world case studies, hands on exercises, and project based learning activities, participants will develop the ability to build and evaluate intelligent solutions for real business and technological challenges.

A strong emphasis is placed on practical application and professional competence. Learners will work on AI engineering projects that mirror real workplace environments, allowing them to gain experience in problem analysis, solution design, model training, testing, optimization, deployment, and performance evaluation. These activities strengthen technical proficiency while enhancing critical thinking, analytical reasoning, problem solving, collaboration, and communication skills that are highly valued by employers.

The diploma follows an industry focused and career oriented learning approach designed to align with current market requirements and emerging developments within artificial intelligence. Learners will gain exposure to professional workflows, industry standards, and best practices used by AI engineers and technology teams worldwide. Internship opportunities may be available as part of professional development initiatives, providing learners with valuable exposure to workplace environments and practical industry experience.

Graduates of this programme may pursue opportunities in artificial intelligence engineering, machine learning support, AI application development, intelligent automation, data driven technology projects, research assistance, software development support, and related technology fields. The diploma can also support career advancement for existing professionals, enable freelance consulting and AI solution development services, assist entrepreneurs in leveraging AI technologies within their businesses, and provide a pathway toward advanced studies in artificial intelligence, machine learning, computer science, or related disciplines.

Learnatic Academy provides career guidance and professional development support throughout the learning journey. Through its Hiring and Placement Network, the academy assists qualified graduates by connecting them with relevant employment opportunities whenever available. This additional support helps learners strengthen their professional profiles, improve employability, and prepare for success in competitive global job markets.

Learnatic Academy is a UK and Singapore Accredited institution and an Approved CPD Provider. The academy is recognized by The CPD Group UK, International CPD Standards Board UK, and Online CPD Accreditation Singapore. These recognitions reflect the academy’s commitment to delivering high quality education and professional development opportunities that align with international standards and industry expectations.

Understand the core concepts, principles, and applications of artificial intelligence in modern industries.
Develop practical skills in designing and implementing AI powered solutions for real world challenges.
Learn the fundamentals of machine learning algorithms and predictive modelling techniques.
Build and train artificial intelligence models using industry relevant tools and methodologies.
Understand neural networks and deep learning architectures used in advanced AI applications.
Explore natural language processing techniques for intelligent language based systems and applications.
Learn computer vision concepts used in image recognition, object detection, and visual analysis systems.
Develop skills in data preparation, feature engineering, and dataset management for AI projects.
Apply professional workflows for AI development, testing, deployment, and maintenance.
Gain hands on experience through practical assignments, case studies, and project based learning activities.
Learn how to evaluate model performance and optimize AI systems for improved accuracy and efficiency.
Understand ethical AI principles, responsible development practices, and regulatory considerations.
Strengthen analytical thinking, problem solving, and decision making skills in technology environments.
Develop professional communication skills for presenting AI solutions and technical findings to stakeholders.
Build a portfolio of artificial intelligence projects that demonstrates practical engineering competence.
Gain workplace readiness and confidence to contribute effectively to AI and technology teams.
Develop career planning, employability, and professional growth skills for the artificial intelligence sector.
Prepare for employment, freelancing, entrepreneurship, internships, and further academic advancement in AI related disciplines.

Course Content

Module 1: Foundations of Artificial Intelligence

Lecture 2: History and Evolution of Artificial Intelligence

04:42:28
Lecture 3: Types of Artificial Intelligence and Intelligence Levels

01:40:57
Lecture 4: AI Engineering Roles, Responsibilities, and Career Paths

06:15:30
Lecture 5: Understanding Machine Learning, Deep Learning, and Generative AI

05:20:12
Lecture 6: AI Applications Across Industries

05:52:20
Lecture 7: AI Development Lifecycle and Engineering Workflow

04:45:20
Lecture 8: Fundamentals of Data for Artificial Intelligence

05:15:30
Lecture 9: Introduction to AI Programming with Python

04:40:10
Lecture 10: Python Environment Setup for AI Development

05:50:08
Lecture 11: Data Types, Variables, and Control Structures for AI Applications

05:08:10
Lecture 12: Functions and Modular Programming for AI Systems

04:20:32
Lecture 13: Object Oriented Programming for AI Engineers

03:10:07
Lecture 14: Working with Files, APIs, and Data Sources

03:07:12
Lecture 15: Mathematics for Artificial Intelligence Engineers

03:40:15
Lecture 16: Probability and Statistics for AI Applications

04:02:27
Lecture 17: Ethics, Bias, and Responsible Artificial Intelligence

04:07:20
Lecture 18: AI Engineering Tools, Frameworks, and Development Environments

04:50:12
Project: Build an AI Fundamentals Portfolio

Module 2: Data Engineering and Machine Learning Foundations

Lecture 19: Data Collection and Data Acquisition Techniques

04:17:32
Lecture 20: Data Cleaning and Preprocessing Workflows

03:45:47
Lecture 21: Feature Engineering and Data Transformation Methods

04:45:30
Lecture 22: Exploratory Data Analysis for AI Projects

04:32:12
Lecture 23: Introduction to NumPy for AI Engineering

04:37:50
Lecture 24: Data Manipulation with Pandas

04:38:45
Lecture 25: Data Visualization with Matplotlib

05:07:57
Lecture 26: Advanced Data Visualization with Seaborn

04:50:25
Lecture 27: Introduction to Machine Learning Concepts

05:17:20
Lecture 28: Supervised Learning Fundamentals

04:50:18
Lecture 29: Unsupervised Learning Fundamentals

05:32:17
Lecture 30: Training and Testing Data Strategies

04:37:42
Lecture 31: Model Evaluation Metrics and Performance Analysis

05:52:17
Lecture 32: Overfitting, Underfitting, and Model Optimization

04:47:15
Lecture 33: Introduction to Scikit Learn

03:05:17
Lecture 34: Building Classification Models

04:22:50
Lecture 35: Building Regression Models

01:55:45
Lecture 36: Professional Machine Learning Project Workflow

01:28:05
Project: Develop a Predictive Machine Learning Solution

Module 3: Deep Learning and Neural Network Engineering

Lecture 37: Introduction to Deep Learning Architecture

03:12:58
Lecture 38: Artificial Neural Networks and Learning Mechanisms

03:52:17
Lecture 39: Activation Functions and Optimization Algorithms

02:45:17
Lecture 40: Forward Propagation and Backpropagation Techniques

05:50:12
Lecture 41: Training Neural Networks with TensorFlow

05:47:32
Lecture 42: Deep Learning Development with Keras

06:30:28
Lecture 43: Hyperparameter Tuning and Model Optimization

05:50:27
Lecture 44: Regularization Techniques for Deep Learning Models

05:25:35
Lecture 45: Convolutional Neural Networks Fundamentals

05:17:37
Lecture 46: Computer Vision and Image Processing Applications

04:10:22
Lecture 47: Object Detection and Image Classification Systems

03:57:42
Lecture 48: Transfer Learning for AI Applications

03:42:55
Lecture 49: Recurrent Neural Networks and Sequence Modelling

02:30:12
Lecture 50: Long Short Term Memory Networks

04:18:07
Lecture 51: Time Series Forecasting with Deep Learning

04:48:10
Lecture 52: Deep Learning Performance Evaluation

05:08:27
Lecture 53: Industry Deep Learning Case Studies

03:32:45
Lecture 54: Production Workflow for Deep Learning Systems

05:40:32
Project: Build and Train a Deep Learning Image Recognition System

Module 4: Natural Language Processing and Generative AI

Lecture 55: Introduction to Natural Language Processing

04:47:57
Lecture 56: Text Processing and Data Preparation Techniques

05:02:25
Lecture 57: Tokenization and Text Representation Methods

05:07:25
Lecture 58: Bag of Words and TF IDF Models

06:07:59
Lecture 59: Word Embeddings and Semantic Understanding

04:42:18
Lecture 60: Named Entity Recognition Systems

06:20:59
Lecture 61: Sentiment Analysis and Opinion Mining

06:10:52
Lecture 62: Text Classification Workflows

05:55:42
Lecture 63: Topic Modelling and Knowledge Discovery

05:05:17
Lecture 64: Language Modelling Fundamentals

05:27:30
Lecture 65: Transformer Architecture Explained

04:02:40
Lecture 66: Attention Mechanisms in Modern AI Systems

01:05:22
Lecture 67: Introduction to Generative Artificial Intelligence

05:30:57
Lecture 68: Large Language Model Architecture Fundamentals

05:12:40
Lecture 69: Foundation Models and Their Applications

01:32:55
Lecture 70: Text Generation and Summarization Systems

02:02:40
Lecture 71: Generative AI Engineering Best Practices

05:52:08
Lecture 72: Enterprise Applications of Generative AI

04:30:17
Project: Develop a Generative AI Content Intelligence Platform

Module 5: AI Systems Engineering and Deployment

Lecture 73: Introduction to AI Systems Architecture

01:48:30
Lecture 74: Cloud Computing for AI Engineers

04:45:20
Lecture 75: Data Pipelines and AI Infrastructure

04:15:30
Lecture 76: MLOps Fundamentals and Engineering Workflow

01:12:07
Lecture 77: Version Control and Collaboration with Git

04:37:22
Lecture 78: Model Deployment Fundamentals

04:40:52
Lecture 79: Containerization with Docker

02:17:22
Lecture 80: API Development for AI Applications

01:57:07
Lecture 81: Building AI Services with FastAPI

04:22:25
Lecture 82: AI Model Serving Techniques

04:22:12
Lecture 83: Monitoring and Logging AI Systems

05:02:18
Lecture 84: Performance Optimization for AI Applications

03:28:15
Lecture 85: Security Best Practices for AI Systems

03:58:22
Lecture 86: Scalable AI Infrastructure Design

03:59:10
Lecture 87: AI Governance and Compliance Requirements

02:50:12
Lecture 88: Production AI Lifecycle Management

02:45:32
Lecture 89: Enterprise AI Case Studies

03:40:42
Lecture 90: Building Production Ready AI Solutions

03:07:15
Project: Deploy an Enterprise Grade AI Application

Module 6: Professional AI Chatbot Engineering and Deployment

Lecture 91: Introduction to Large Language Models for Chatbot Development

07:58
Lecture 92: Understanding GPT Architecture and Modern LLM Ecosystems

02:59
Lecture 93: Prompt Engineering Fundamentals for AI Chatbots

04:02
Lecture 94: Advanced Prompt Engineering Techniques and Workflows

01:15
Lecture 95: OpenAI API Integration for Intelligent Applications

07:22
Lecture 96: Building AI Chatbots with OpenAI APIs

05:70
Lecture 97: Introduction to AI Agents and Autonomous Workflows

10:12
Lecture 98: Developing Agentic Systems with LangChain

12:02
Lecture 99: Embeddings and Semantic Search Fundamentals

07:40
Lecture 100: Vector Databases for AI Applications

12:25
Lecture 101: Retrieval Augmented Generation Architecture

05:45
Lecture 102: Building Knowledge Based Chatbots with RAG

02:55
Lecture 103: Memory Systems and Conversational Context Management

03:45
Lecture 104: Fine Tuning Concepts for Large Language Models

05:02
Lecture 105: Multi Agent Systems and Collaborative AI Architectures

05:22
Lecture 106: Voice AI and Speech Enabled Chatbots

02:07
Lecture 107: Production Deployment of AI Chatbots

02:57
Lecture 108: Monitoring, Security, and Governance for Chatbot Systems

12:28
Lecture 109: End to End Enterprise AI Chatbot Development Workflow

08:59
Lecture 110: Building a Production Ready Business AI Chatbot

02:05
Project: Develop, Train, Optimize and Deploy a Production-Ready AI Chatbot for Business Use

Certificates Awarded To Students For Successfully Completing Courses At Learnatic Academy

About Course

What Will You Learn?

Course Content

Module 1: Foundations of Artificial Intelligence

Lecture 2: History and Evolution of Artificial Intelligence

Lecture 3: Types of Artificial Intelligence and Intelligence Levels

Lecture 4: AI Engineering Roles, Responsibilities, and Career Paths

Lecture 5: Understanding Machine Learning, Deep Learning, and Generative AI

Lecture 6: AI Applications Across Industries

Lecture 7: AI Development Lifecycle and Engineering Workflow

Lecture 8: Fundamentals of Data for Artificial Intelligence

Lecture 9: Introduction to AI Programming with Python

Lecture 10: Python Environment Setup for AI Development

Lecture 11: Data Types, Variables, and Control Structures for AI Applications

Lecture 12: Functions and Modular Programming for AI Systems

Lecture 13: Object Oriented Programming for AI Engineers

Lecture 14: Working with Files, APIs, and Data Sources

Lecture 15: Mathematics for Artificial Intelligence Engineers

Lecture 16: Probability and Statistics for AI Applications

Lecture 17: Ethics, Bias, and Responsible Artificial Intelligence

Lecture 18: AI Engineering Tools, Frameworks, and Development Environments

Project: Build an AI Fundamentals Portfolio

Module 2: Data Engineering and Machine Learning Foundations

Lecture 19: Data Collection and Data Acquisition Techniques

Lecture 20: Data Cleaning and Preprocessing Workflows

Lecture 21: Feature Engineering and Data Transformation Methods

Lecture 22: Exploratory Data Analysis for AI Projects

Lecture 23: Introduction to NumPy for AI Engineering

Lecture 24: Data Manipulation with Pandas

Lecture 25: Data Visualization with Matplotlib

Lecture 26: Advanced Data Visualization with Seaborn

Lecture 27: Introduction to Machine Learning Concepts

Lecture 28: Supervised Learning Fundamentals

Lecture 29: Unsupervised Learning Fundamentals

Lecture 30: Training and Testing Data Strategies

Lecture 31: Model Evaluation Metrics and Performance Analysis

Lecture 32: Overfitting, Underfitting, and Model Optimization

Lecture 33: Introduction to Scikit Learn

Lecture 34: Building Classification Models

Lecture 35: Building Regression Models

Lecture 36: Professional Machine Learning Project Workflow

Project: Develop a Predictive Machine Learning Solution

Module 3: Deep Learning and Neural Network Engineering

Lecture 37: Introduction to Deep Learning Architecture

Lecture 38: Artificial Neural Networks and Learning Mechanisms

Lecture 39: Activation Functions and Optimization Algorithms

Lecture 40: Forward Propagation and Backpropagation Techniques

Lecture 41: Training Neural Networks with TensorFlow

Lecture 42: Deep Learning Development with Keras

Lecture 43: Hyperparameter Tuning and Model Optimization

Lecture 44: Regularization Techniques for Deep Learning Models

Lecture 45: Convolutional Neural Networks Fundamentals

Lecture 46: Computer Vision and Image Processing Applications

Lecture 47: Object Detection and Image Classification Systems

Lecture 48: Transfer Learning for AI Applications

Lecture 49: Recurrent Neural Networks and Sequence Modelling

Lecture 50: Long Short Term Memory Networks

Lecture 51: Time Series Forecasting with Deep Learning

Lecture 52: Deep Learning Performance Evaluation

Lecture 53: Industry Deep Learning Case Studies

Lecture 54: Production Workflow for Deep Learning Systems

Project: Build and Train a Deep Learning Image Recognition System

Module 4: Natural Language Processing and Generative AI

Lecture 55: Introduction to Natural Language Processing

Lecture 56: Text Processing and Data Preparation Techniques

Lecture 57: Tokenization and Text Representation Methods

Lecture 58: Bag of Words and TF IDF Models

Lecture 59: Word Embeddings and Semantic Understanding

Lecture 60: Named Entity Recognition Systems

Lecture 61: Sentiment Analysis and Opinion Mining

Lecture 62: Text Classification Workflows

Lecture 63: Topic Modelling and Knowledge Discovery

Lecture 64: Language Modelling Fundamentals

Lecture 65: Transformer Architecture Explained

Lecture 66: Attention Mechanisms in Modern AI Systems

Lecture 67: Introduction to Generative Artificial Intelligence

Lecture 68: Large Language Model Architecture Fundamentals

Lecture 69: Foundation Models and Their Applications

Lecture 70: Text Generation and Summarization Systems

Lecture 71: Generative AI Engineering Best Practices