Technical AI Specialist – Master AI Development
Advance your AI expertise with our Technical AI Specialist program. Progress from foundational concepts to advanced machine learning, neural networks, and AI deployment. Work on real-world projects like recommendation engines and NLP chatbots, mastering tools like TensorFlow and PyTorch. Earn industry-recognized certifications at every level and excel in the evolving AI landscape.
- In-Depth Knowledge: Master AI concepts, including machine learning, neural networks, and model deployment.
- Practical Expertise: Gain hands-on experience with tools like TensorFlow and PyTorch by working on real-world projects.
- Industry Certifications: Earn credentials at every level, boosting your career prospects.
- Advanced Skills: Develop cutting-edge skills in NLP, computer vision, and MLOps to tackle complex AI challenges.
- Career-Ready Portfolio: Build a portfolio showcasing your AI projects to stand out in the job market.
Technical AI Specialist Courses
AI Foundation Course
AI Foundation Course Overview
- AI Foundations & Applications – Explore AI evolution, key concepts, Machine Learning, NLP, and Computer Vision.
- Python for AI & Data Analysis – Master Python with NumPy, Pandas, and Matplotlib for data handling and EDA.
- Machine Learning Essentials – Learn ML lifecycle, key algorithms, model evaluation, and overfitting prevention.
- Ethical AI & Bias – Understand AI fairness, transparency, and bias detection using LIME and SHAP.
AI Foundation Course Outline
Module 1: Introduction to Artificial Intelligence
- Explore the evolution of AI and its types (Narrow AI, General AI, Superintelligent AI).
- Understand AI concepts: AI vs. ML vs. DL vs. Data Science.
- Dive into AI subfields like Machine Learning, NLP, and Computer Vision.
- Discuss emerging trends, including Generative AI and Ethical AI.
- Practical: Identify AI use cases in different industries through group discussion.
Module 2: Python for AI and Data Analysis
- Learn Python basics and essential libraries (NumPy, Pandas, Matplotlib).
- Master data handling, cleaning, and exploratory data analysis (EDA).
- Practical: Analyze a dataset (e.g., housing prices or Titanic) to uncover insights.
Module 3: Basics of Machine Learning
- Understand ML types, lifecycle, and core algorithms (linear regression, decision trees).
- Learn model evaluation metrics (accuracy, precision, recall) and address overfitting.
- Practical: Build a simple linear regression model to predict housing prices.
Module 4: Understanding AI Ethics and Bias
- Explore the societal impact of AI and the risks of bias in algorithms.
- Learn ethical principles like fairness, transparency, and accountability.
- Analyze explainable AI tools (e.g., LIME, SHAP).
- Practical: Study real-world cases of AI bias and its impact on decision-making.
- Location – Leeds, UK
- Duration – 8 Days
- Timing – 9:00 to 17:00
- Price – £4660
- Duration – 8 Days
- Timing – 9:00 to 17:00
- Price – £4380
What’s Included in the AI Foundations Course:
- Flexible Learning / Flexible Delivery
- Choose between self-paced modules or live instructor-led sessions with hands-on projects.
- Access recorded lessons and mentor support for flexible, personalized learning.
- Exam Preparation
- Strengthen understanding with structured revision, practice quizzes, and mock tests.
- Focus on AI fundamentals, Python for AI, ML basics, and ethical AI considerations.
- Certification
- Earn an industry-recognized AI Foundation certification upon course completion.
- Validate your knowledge in AI principles, applications, and ethical frameworks.
Prerequisites for AI Foundations Course
- Basic Programming Knowledge
- Familiarity with programming concepts like loops, functions, and variables (Python knowledge is a plus but not mandatory).
- Mathematics Fundamentals
- Understanding of basic linear algebra, probability, and statistics (e.g., mean, median, and standard deviation).
- Analytical Skills
- Ability to analyze and interpret data patterns using simple tools like Excel or Google Sheets.
- Interest in AI
- A keen interest in learning AI concepts and their real-world applications across industries.
- Hardware and Software Requirements
- A laptop or computer with at least 8GB RAM, i5 processor, and a stable internet connection.
- Familiarity with basic online tools like Zoom, Slack, or Google Workspace.
- Optional Bridge Course:
- Beginners can join our AI Foundations Bootcamp, which covers Python basics, math for AI, and data handling techniques.
Each participant of this course will have to attend and pass one project and one exam to complete the module and attain the certification.
Module Project (50%)
- Duration: Throughout the course
- Type: Practical project based on the contents of the module- Build a simple machine learning model (For example: Building a loan eligibility predictor)
- Pass mark: 65%
Certification Exam (50%)
- Duration: 60 minutes
- Type: 40 multiple choice questions
- Pass mark: 65% (26/40)
Machine Learning Engineer Course
Machine Learning Engineer Course Overview
- Comprehensive Curriculum – Covers advanced machine learning, deep learning fundamentals, data preparation, and AI tool mastery.
- Machine Learning Techniques – Master regression, classification, clustering, and ensemble methods like Random Forest and XGBoost.
- Neural Networks & Deep Learning – Understand neural architectures, activation functions, backpropagation, and gradient descent.
- Data Preparation & Feature Engineering – Learn encoding, outlier handling, scaling methods, and effective feature extraction.
- AI Tools: TensorFlow & PyTorch – Gain hands-on experience in model building, training, and optimization using leading AI frameworks.
Machine Learning Engineer Course Outline
Module 1: Advanced Machine Learning Techniques
- Explore regression, classification, and clustering algorithms.
- Learn ensemble methods like Bagging (Random Forest) and Boosting (XGBoost).
- Dive into dimensionality reduction with PCA.
- Practical: Build a Random Forest classifier for customer churn analysis.
Module 2: Neural Networks and Deep Learning Basics
- Understand neural network architecture, layers, and activation functions.
- Learn backpropagation and gradient descent principles.
- Practical: Implement a basic neural network using TensorFlow or PyTorch.
Module 3: Data Preparation and Feature Engineering
- Master feature engineering techniques like encoding and outlier handling.
- Learn scaling methods like Min-Max and z-score normalization.
- Practical: Engineer features from a real-world dataset.
Module 4: AI Tools: TensorFlow and PyTorch
- Get hands-on with TensorFlow’s computational graphs and PyTorch’s dynamic operations.
- Build and train models with both frameworks.
- Practical: Develop a classification model using TensorFlow and PyTorch.
- Location – Leeds, UK
- Duration – 12 Days
- Timing – 9:00 to 17:00
- Price – £6300
- Duration – 12 Days
- Timing – 9:00 to 17:00
- Price – £2880
What’s Included in the Machine Learning Engineer Course:
- Flexible Learning / Flexible Delivery
- Self-paced online modules with instructor-led sessions for deeper understanding.
- Hands-on projects and real-world applications accessible anytime, anywhere.
- Exam Preparation
- Practice tests and mock exams simulating real certification assessments.
- Detailed review sessions covering key concepts, problem-solving strategies, and exam techniques.
- Certification
- Industry-recognized certification upon successful course completion.
- Guidance on official Machine Learning certifications, including exam registration and preparation resources.
Prerequisites for Machine Learning Engineer Course
- Programming Knowledge
- Familiarity with Python programming, including concepts like loops, functions, and data structures.
- Mathematics and Statistics
- Understanding of linear algebra, calculus, and probability.
- Familiarity with statistical concepts such as mean, variance, and standard deviation.
- Basic Machine Learning Concepts
- Knowledge of supervised and unsupervised learning.
- Awareness of machine learning lifecycle and algorithms.
- Data Handling Skills
- Experience with data manipulation and analysis using tools like Pandas, NumPy, or Excel.
- Basic Computer Science Knowledge
- Understanding of file systems, data storage, and algorithms.
- Hardware and Software Requirements
- A computer with at least 8GB RAM and an i5 processor.
- Stable internet connection for online tools and resources.
- Optional but Helpful
- Prior experience with any machine learning frameworks or libraries (e.g., TensorFlow, PyTorch).
- Exposure to data visualization tools like Matplotlib or Seaborn.
- Bridge Course (Optional for Beginners)
- A preparatory “AI Foundations Bootcamp” covering Python basics, math for AI, and introductory data handling.
Each participant of this course will have to attend and pass one project and one exam to complete the module and attain the certification.
Module Project (50%)
- Duration: Throughout the course
- Type: Practical project based on the contents of the module- Create a recommendation engine for a retail dataset.
- Pass mark: 65%
Certification Exam (50%)
- Duration: 60 minutes
- Type: 40 multiple choice questions
- Pass mark: 65% (26/40)
AI Specialist Course
AI Specialist Course Overview
- Comprehensive Curriculum – Covers CNNs, RNNs, Transformers, NLP, Computer Vision, AI deployment, and MLOps.
- Deep Learning & NLP – Includes CNNs, LSTMs, GRUs, Transformers, Word2Vec, BERT, and GPT.
- Computer Vision & Object Detection – Focuses on YOLO, Mask R-CNN, and real-world dataset training.
- AI Deployment & MLOps – Covers API creation, Docker, cloud deployment, and CI/CD pipelines.
AI Specialist Course Outline
Module 1: Advanced Neural Networks: CNNs, RNNs, and Transformers
- CNNs for Image Data: Explore filters, pooling, and feature maps.
- RNNs for Sequential Data: Dive into LSTMs and GRUs.
- Transformers: Master self-attention and positional encoding.
- Practical: Build a CNN for image classification (e.g., MNIST dataset).
Module 2: Natural Language Processing (NLP)
- Text Processing: Tokenization, stemming, and lemmatization.
- Language Models: Understand Word2Vec, BERT, and GPT.
- Practical: Develop a sentiment analysis model.
Module 3: Computer Vision Applications
- Image Segmentation and Object Detection: Learn YOLO and Mask R-CNN.
- Practical: Implement object detection on a custom dataset.
Module 4: AI Model Deployment and MLOps
- Model Deployment: Create APIs, work with Docker, and utilize cloud platforms.
- MLOps Principles: Implement Continuous Integration and Deployment (CI/CD).
- Practical: Deploy a chatbot using Flask and Docker.
- Location – Leeds, UK
- Duration – 12 Days
- Timing – 9:00 to 17:00
- Price – £6300
- Duration – 12 Days
- Timing – 9:00 to 17:00
- Price – £2880
What’s Included in the AI Specialist Course:
- Flexible Learning / Flexible Delivery
- Access course materials anytime with self-paced learning and live instructor-led sessions.
- Learn through interactive labs, real-world projects, and expert mentorship.
- Exam Preparation
- Comprehensive practice tests and mock exams aligned with industry certifications.
- Hands-on assignments and real-world case studies to reinforce concepts.
- Certification
- Earn an industry-recognized AI Specialist certification upon successful completion.
- Gain credibility with certification aligned with AI industry standards.
Prerequisites for AI Specialist Course
- Foundational AI Knowledge:
- Basic understanding of AI concepts, including Machine Learning and Neural Networks.
- Programming Skills:
- Proficiency in Python, including familiarity with libraries like NumPy, Pandas, and Matplotlib.
- Mathematical Foundations:
- Knowledge of linear algebra, calculus, probability, and statistics is highly recommended.
- Experience with Machine Learning Models:
- Prior experience building and evaluating simple ML models such as regression or classification.
- Curiosity and a Growth Mindset:
- Eagerness to explore complex AI topics and apply them to real-world problems.
Each participant of this course will have to attend and pass one project and one exam to complete the module and attain the certification.
Module Project (50%)
- Duration: Throughout the course
- Type: Practical project based on the contents of the module- Build and deploy an NLP chatbot or computer vision application.
- Pass mark: 65%
Certification Exam (50%)
- Duration: 60 minutes
- Type: 40 multiple choice questions
- Pass mark: 65% (26/40)
AI Mastery Course
AI Mastery Course Overview
- Comprehensive Curriculum – Covers advanced AI topics, including Generative AI, cloud-based AI deployment, model optimization, and explainable AI.
- Cutting-Edge AI Tools – Gain hands-on experience with GANs, GPT, AWS SageMaker, Google Cloud AI, Azure AI, and SHAP for model fairness.
- Hands-On Learning – Train and deploy AI models, fine-tune large language models, and optimize deep learning solutions for real-world applications.
- Scalability & Efficiency – Master techniques like quantization and pruning to enhance AI performance for cloud and mobile deployment.
- Ethics & Explainability – Understand AI decision-making, evaluate bias, and implement fairness strategies using industry-leading tools.
AI Mastery Course Outline
Module 1: Generative AI
- Explore GANs (Generative Adversarial Networks): Learn how discriminators and generators create synthetic data.
- Dive into LLMs (Large Language Models): Fine-tune models like GPT.
- Practical: Train a simple GAN to generate synthetic images.
Module 2: AI in Cloud
- Gain expertise in cloud platforms: AWS SageMaker, Google Cloud AI, and Azure AI.
- Learn to train and deploy AI models seamlessly in cloud environments.
- Practical: Deploy an AI model using AWS SageMaker.
Module 3: Scaling and Optimizing AI Models
- Master optimization techniques: Quantization and pruning for efficient AI models.
- Learn to scale deep learning models for real-world deployment.
- Practical: Optimize a deep learning model for mobile use.
Module 4: Advanced Ethics and Explainable AI
- Delve into explainability: Tools and techniques to understand AI decisions.
- Analyze real-world applications and case studies.
- Practical: Evaluate model fairness using tools like SHAP.
- Location – Leeds, UK
- Duration – 14 Days
- Timing – 9:00 to 17:00
- Price – £7300
- Duration – 14 Days
- Timing – 9:00 to 17:00
- Price – £3310
What’s Included in the AI Mastery Course:
- Flexible Learning / Flexible Delivery
- Access self-paced modules, live expert-led sessions, and on-demand resources.
- Learn through interactive projects, case studies, and real-world AI applications.
- Exam Preparation
- Comprehensive guidance with mock tests, key concept reviews, and practice exercises.
- Certification
- Earn an industry-recognized AI Mastery certification upon successful completion.
Prerequisites for AI Mastery Course
- Strong Foundation in AI:
- Completed earlier courses in AI, such as AI Foundations, Machine Learning, or equivalent experience.
- Proficiency in Python:
- Comfortable with Python programming and using libraries like NumPy, Pandas, TensorFlow, or PyTorch.
- Mathematical Knowledge:
- A solid understanding of linear algebra, calculus, probability, and statistics.
- Experience with Neural Networks:
- Familiarity with neural network architectures, including CNNs, RNNs, and Transformers.
- Knowledge of Cloud Platforms:
- Experience with cloud platforms like AWS, GCP, or Azure for deploying AI models is a plus.
- AI Model Development:
- Prior experience in building and training machine learning or deep learning models.
Each participant of this course will have to attend and pass one project and one exam to complete the module and attain the certification.
Module Project (50%)
- Duration: Throughout the course
- Type: Practical project based on the contents of the module- Build a production-ready AI application addressing a real-world problem.
- Pass mark: 65%
Certification Exam (50%)
- Duration: 60 minutes
- Type: 40 multiple choice questions
- Pass mark: 65% (26/40)