Recommended prerequisite: Certified Python Professional (C|PP™)

Modules

Days

75+

Hours

150

Exam Questions

∞

Cert Validity

Module 1

Introduction to AI & Machine Learning

Understand what AI and ML are, why they matter in business, and the tools, frameworks, and ethical considerations that shape the field.

What is AI? ML Applications AI in Business Key Terminologies AI Tools & Frameworks Ethics in AI & ML Real-World Success Stories Challenges & Limitations Trends & Future Directions

You understand the AI/ML landscape and can set up your tools and environment

Module 2

Data Preprocessing & Exploratory Data Analysis

Clean, transform, and explore real datasets. Handle missing values, outliers, imbalanced data, and build visual representations.

Data Quality Collection & Storage Cleaning & Transformation Feature Extraction & Selection EDA Techniques Visualisation & Representation Missing Data & Outliers Imbalanced Data Best Practices

You can prepare and explore any dataset for machine learning

Module 3

Supervised Learning Algorithms

Build, train, and evaluate models that learn from labelled data — from regression to neural networks and deep learning.

Linear & Logistic Regression Decision Trees & Random Forests Support Vector Machines Naive Bayes k-Nearest Neighbours Neural Networks & Deep Learning Model Evaluation Metrics Hyperparameter Tuning

You can build, train, and evaluate supervised ML models

Module 4

Unsupervised Learning Algorithms

Discover hidden patterns in unlabelled data with clustering, dimensionality reduction, anomaly detection, and recommender systems.

K-means & Hierarchical Clustering DBSCAN PCA Dimensionality Reduction Association Rule Mining Anomaly Detection Recommender Systems Self-Organising Maps Evaluation Metrics

You can find structure and anomalies in unlabelled datasets

Module 5

Natural Language Processing (NLP)

Process and understand human language — from tokenisation and sentiment analysis to Transformer models, BERT, Llama, and GPT.

Text Preprocessing & Tokenisation Bag-of-Words & TF-IDF Sentiment Analysis Named Entity Recognition Text Classification Topic Modelling (LDA) Word Embeddings & RNNs Transformers, BERT, Llama & GPT

You can build NLP pipelines and understand modern language models

Module 6

Reinforcement Learning & Deep RL

Teach agents to make decisions through rewards — from Q-learning and policy gradients to deep RL algorithms like DDPG and A3C.

Markov Decision Processes Q-Learning & TD Learning Deep Q-Networks (DQN) Policy Gradient Methods Actor-Critic Methods DDPG & A3C Real-World RL Applications Challenges & Future Directions

You can design and implement reinforcement learning agents

Module 7

Deployment & Productionisation of ML Models

Take models from training to production — cloud platforms, containerisation, REST APIs, CI/CD, monitoring, and security.

Training to Production AWS, Azure & GCP Docker, S3 & Kubernetes Flask & REST APIs Scalability & Performance Monitoring & Maintenance Model Versioning & CI/CD Security & Privacy

You can deploy, scale, and maintain ML models in production

Module 8

Explainability & Interpretability in AI

Demystify black-box models — understand feature importance, fairness, bias, and use frameworks like SHAP and LIME.

Model Explainability Black-Box Interpretation Feature Importance & Selection Local & Global Methods SHAP & LIME Fairness & Bias in AI Regulatory Considerations Performance vs Explainability

You can explain and interpret AI model decisions responsibly

Module 9

AI Ethics, Privacy & Governance

Navigate the legal, regulatory, and ethical landscape of AI — responsible governance, transparency, accountability, and data privacy.

Responsible AI Ethical Data Collection Bias & Fairness in Algorithms Privacy & Security Legal & Regulatory Landscape AI Governance Frameworks Ethics Committees & Review Boards Transparency & Accountability

You can apply ethical and governance frameworks to any AI project

M10

Module 10

AI in Business Transformation & Future Trends

Apply AI across industries — CRM, marketing, supply chain, finance, healthcare, manufacturing, HR, cybersecurity, and emerging trends.

AI Adoption Strategies AI in CRM Sales & Marketing Supply Chain & Operations Finance & Risk Management Healthcare & Medicine Manufacturing & Industry HR & Talent Management Cybersecurity Emerging Trends & Future of AI

You can identify and implement AI transformation across any business domain

Proctored Examination & Certification

Certified Artificial Intelligence & Machine Learning Professional

You can design, build, and evaluate machine learning solutions end-to-end
You can deploy models to production and explain their decisions responsibly
You are ready for advanced NLP and large language model training

Assessment

Proctored Exam

Duration

4 Hours

Questions

150

Pass Mark

75%

Certificate

For Life

This course prepares you for Certified Applied Artificial Intelligence and Natural Language Processing Expert (C|AAINE™) and all advanced Obi.Academy programmes.