CURRICULUM

• Learn the fundamentals about AI and ML!

• 5x days of intense, quality and fun learning.

• Aesthetically pleasing in print, digital and audio formats.

• Horizontal learning experience for more immersive study.

• 75+ Hours of learning rich, targeted and in-depth content without the fluff.

• Content, demos, labs and exercises prepared by our professionals, at every stage and topic of the course for beginners and advanced levels.

• Official and only learning courseware, for the C|AIMLP™ online and on-site course.

Course Formats

• Digital Courseware Book: for instructor-led courses (online or on-site) and for eLearning platform.

• Print Courseware Book: for instructor-led courses (online or on-site) only and on-demand.

• Course delivery:

  • Online self-paced self-study (via our Obi.Academy eLearning audio/visual platform)

  • Online course delivery (via our Obi.Academy eLearning audio/visual platform)

  • Onsite course delivery (via our partners Startel B.V. in the Netherlands)

Course Delivery:

The Certified Artificial and Machine Learning Professional™ - (C|AIMLP™) course is available online as a self-paced self-study course or as an instructor-led online and onsite, with our approved training partners (ATPs) in the UK and Europe. Obipixel Ltd is actively in discussions with potential partners in the USA, Africa and Asia. To enquire about becoming an approved training provider and affiliate or a learner/attendee on our courses with our partners with Obi.Academy please contact us for further details.

Module 1: Introduction to Artificial Intelligence and Machine Learning

• 1.1 What is Artificial Intelligence?

• 1.2 Understanding Machine Learning and its Applications

• 1.3 The Role of AI and ML in Today's Business Landscape

• 1.4 Key Terminologies and Concepts in AI and ML

• 1.5 Overview of AI Tools and Frameworks

• 1.6 Ethical Considerations in AI and ML

• 1.7 Real-World Examples of AI and ML Success Stories

• 1.8 Challenges and Limitations of AI and ML

• 1.9 Trends and Future Directions in AI and ML

Module 2: Data Preprocessing and Exploratory Data Analysis

• 2.1 Importance of Data Quality in AI and ML

• 2.2 Data Collection and Storage

• 2.3 Data Cleaning and Transformation Techniques

• 2.4 Feature Extraction and Selection

• 2.5 Exploratory Data Analysis (EDA) Techniques

• 2.6 Visualization and Data Representation

• 2.7 Dealing with Missing Data and Outliers

• 2.8 Handling Imbalanced Data

• 2.9 Best Practices for Data Preprocessing and EDA

Module 3: Supervised Learning Algorithms

• 3.1 Introduction to Supervised Learning

• 3.2 Linear Regression and Logistic Regression

• 3.3 Decision Trees and Random Forests

• 3.4 Support Vector Machines (SVM)

• 3.5 Naive Bayes Classifier

• 3.6 k-Nearest Neighbors (k-NN)

• 3.7 Neural Networks and Deep Learning

• 3.8 Model Evaluation Metrics

• 3.9 Hyperparameter Tuning and Model Selection

Module 4: Unsupervised Learning Algorithms

• 4.1 Introduction to Unsupervised Learning

• 4.2 Clustering Techniques: K-means, Hierarchical, DBSCAN

• 4.3 Dimensionality Reduction: Principal Component Analysis (PCA)

• 4.4 Association Rule Mining

• 4.5 Anomaly Detection

• 4.6 Recommender Systems

• 4.7 Self-Organizing Maps (SOM)

• 4.8 Evaluation Metrics for Unsupervised Learning

• 4.9 Applications and Use Cases of Unsupervised Learning

Module 5: Natural Language Processing (NLP)

• 5.1 Introduction to NLP and its Applications

• 5.2 Text Preprocessing and Tokenization

• 5.3 Text Representation: Bag-of-Words, TF-IDF

• 5.4 Sentiment Analysis

• 5.5 Named Entity Recognition (NER)

• 5.6 Text Classification

• 5.7 Topic Modeling: Latent Dirichlet Allocation (LDA)

• 5.8 Neural Networks for NLP: Word Embeddings, Recurrent Neural Networks (RNNs)

• 5.9 Recent Advances in NLP: Transformer Models, BERT, Llama and GPT

Module 6: Reinforcement Learning and Deep Reinforcement Learning

• 6.1 Introduction to Reinforcement Learning (RL)

• 6.2 Markov Decision Processes (MDPs)

• 6.3 Q-Learning and Temporal Difference Learning

• 6.4 Deep Q-Networks (DQN)

• 6.5 Policy Gradient Methods

• 6.6 Actor-Critic Methods

• 6.7 Deep Reinforcement Learning Algorithms: DDPG, A3C

• 6.8 Applications of Reinforcement Learning

• 6.9 Challenges and Future Directions in RL

Module 7: Deployment and Productionisation of ML Models

• 7.1 Model Deployment: From Training to Production

• 7.2 Cloud Computing for ML: AWS, Azure, GCP

• 7.3 Containerisation with Docker, S3 and Kubernetes

• 7.4 Model Serving with Flask and REST APIs

• 7.5 Scalability and Performance Considerations

• 7.6 Monitoring and Maintenance of ML Systems

• 7.7 Model Versioning and Continuous Integration/Deployment (CI/CD)

• 7.8 Security and Privacy in ML Systems

• 7.9 Case Studies of Successful Model Deployments

Module 8: Explainability and Interpretability in AI

• 8.1 Importance of Model Explainability

• 8.2 Techniques for Interpreting Black-Box Models

• 8.3 Feature Importance and Feature Selection

• 8.4 Local and Global Interpretability Methods

• 8.5 Model Explainability Frameworks: SHAP, LIME

• 8.6 Fairness and Bias in AI

• 8.7 Regulatory and Ethical Considerations for Model Explainability

• 8.8 Trade-offs between Model Performance and Explainability

• 8.9 Applications and Best Practices for Model Explainability

Module 9: AI Ethics, Privacy, and Governance

• 9.1 Understanding AI Ethics and Responsible AI

• 9.2 Ethical Considerations in Data Collection and Use

• 9.3 Bias and Fairness in AI Algorithms

• 9.4 Privacy and Security in AI Systems

• 9.5 Legal and Regulatory Landscape for AI

• 9.6 Responsible AI Governance and Frameworks

• 9.7 AI Ethics Committees and Review Boards

• 9.8 Transparency and Accountability in AI

• 9.9 Future Directions and Challenges in AI Ethics and Governance

Module 10: AI in Business Transformation and Future Trends

• 10.1 AI Adoption Strategies for Businesses

• 10.2 AI in Customer Relationship Management (CRM)

• 10.3 AI in Sales and Marketing

• 10.4 AI in Supply Chain and Operations

• 10.5 AI in Finance and Risk Management

• 10.6 AI in Healthcare and Medicine

• 10.7 AI in Manufacturing and Industry

• 10.8 AI in Human Resources and Talent Management

• 10.9 AI in Cybersecurity

• 10.10 Emerging Trends and Future of AI in Business