Certified AI & Machine Learning Professional

What AI and ML are, why they matter to business, and the tools, frameworks and ethical issues shaping the field.

• What AI is, and ML applications
• AI in business and key terminology
• AI tools and frameworks
• Ethics in AI and ML
• Real-world success stories
• Challenges and limitations
• Trends and future directions

You will be able to: Understand the AI/ML landscape and set up tools and an environment.

Cleaning, transforming and exploring real datasets, including missing values, outliers and imbalanced data.

• Data quality
• Collection and storage
• Cleaning and transformation
• Feature extraction and selection
• EDA techniques
• Visualisation
• Missing data and outliers
• Imbalanced data
• Best practices

You will be able to: Prepare and explore any dataset for machine learning.

Building, training and evaluating models that learn from labelled data, from regression to neural networks.

• Linear and logistic regression
• Decision trees and random forests
• Support vector machines
• Naive Bayes
• k-nearest neighbours
• Neural networks and deep learning
• Model evaluation metrics
• Hyperparameter tuning

You will be able to: Build, train and evaluate supervised ML models.

Finding patterns in unlabelled data through clustering, dimensionality reduction, anomaly detection and recommender systems.

• k-means and hierarchical clustering
• DBSCAN
• PCA and dimensionality reduction
• Association rule mining
• Anomaly detection
• Recommender systems
• Self-organising maps
• Evaluation metrics

You will be able to: Find structure and anomalies in unlabelled datasets.

Processing and understanding human language, from tokenisation and sentiment analysis to transformers and generative models.

• Text preprocessing and tokenisation
• Bag-of-words and TF-IDF
• Sentiment analysis
• Named entity recognition
• Text classification
• Topic modelling (LDA)
• Word embeddings and RNNs
• Transformers, BERT, Llama and GPT

You will be able to: Build NLP pipelines and understand modern language models.

Training agents to make decisions through rewards, from Q-learning and policy gradients to deep RL algorithms.

• Markov decision processes
• Q-learning and TD learning
• Deep Q-networks (DQN)
• Policy gradient methods
• Actor-critic methods
• DDPG and A3C
• Real-world RL applications
• Challenges and future directions

You will be able to: Design and implement reinforcement learning agents.

Taking models from training to production, including cloud platforms, containers, REST APIs, CI/CD, monitoring and security.

• Training to production
• AWS, Azure and GCP
• Docker, S3 and Kubernetes
• Flask and REST APIs
• Scalability and performance
• Monitoring and maintenance
• Model versioning and CI/CD
• Security and privacy

You will be able to: Deploy, scale and maintain ML models in production.

Making black-box models understandable, including feature importance, fairness, bias and explanation frameworks.

• Model explainability
• Black-box interpretation
• Feature importance and selection
• Local and global methods
• SHAP and LIME
• Fairness and bias in AI
• Regulatory considerations
• Performance versus explainability

You will be able to: Explain and interpret AI model decisions responsibly.

The legal, regulatory and ethical landscape of AI, including governance, transparency, accountability and data privacy.

• Responsible AI
• Ethical data collection
• Bias and fairness in algorithms
• Privacy and security
• Legal and regulatory landscape
• AI governance frameworks
• Ethics committees and review boards
• Transparency and accountability

You will be able to: Apply ethical and governance frameworks to any AI project.

Applying AI across industries, and the trends that come next.

• AI adoption strategies
• AI in CRM
• Sales and marketing
• Supply chain and operations
• Finance and risk management
• Healthcare and medicine
• Manufacturing and industry
• HR and talent management
• Cybersecurity
• Emerging trends and the future of AI

You will be able to: Identify and implement AI transformation across any business domain.