AI, Machine Learning, NLP and AI Governance Course

Name: AI Professional: Master Machine Learning, NLP, and AI Governance Training Course
Availability: InStock

Develop practical capabilities in machine learning, deep neural networks, natural language processing, and operational AI governance frameworks.

AI, Machine Learning, NLP and AI Governance Course

At a glance

Duration: 5 days
Format: Classroom
Cities: Tbilisi, Kuwait, Doha, Amman, Abu Dhabi, New York and more
Next session: 12 – 16 October 2026, Tbilisi
Average fee: 5,800 €

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AI Professional: Master Machine Learning, NLP, and AI Governance Training Course

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Overview

This five-day AI professional programme in machine learning, NLP and AI governance is a practical tour of how AI models are actually built, from a spreadsheet of raw data to a working text classifier. Participants work in guided Python notebooks using scikit-learn, PyTorch and Hugging Face libraries, and carry one realistic customer-feedback dataset through the whole week: cleaning it, predicting churn with classic machine learning, training a small neural network, fine-tuning a transformer to tag complaints by topic, and finally writing a model card that explains what the model can and cannot be trusted to do. No prior coding experience beyond basic scripting is assumed; every notebook is pre-built so participants can focus on understanding each step. This course is delivered by Agile Leaders Training Center.

Who Should Attend

Analysts who use Excel or SQL and want to understand how predictive models are trained
Developers adding a text or image feature to an existing application
Product owners who brief data teams and need to judge what is feasible
Researchers and engineers moving from statistics into neural networks and language models
Team leads preparing an internal AI pilot and its documentation

Departments and Industries

Relevant to any team sitting on text, tabular or image data it wants to use.

Customer service and contact centre analytics
Marketing and e-commerce personalisation teams
Clinical documentation and medical imaging groups
Quality inspection on manufacturing lines
Banking teams working with transaction and document data

Learning Objectives

By the end of this course, participants will be able to:

Use pandas to prepare a messy dataset: missing values, encoding, scaling and train/test splits.
Compare logistic regression, random forest and gradient boosting models trained in scikit-learn.
Evaluate a confusion matrix, precision, recall and ROC curve and choose a sensible threshold.
Build and train a small feed-forward and convolutional network in PyTorch.
Apply Hugging Face to fine-tune a pre-trained transformer for text classification.
Build a model card and a short data sheet describing intended use, limits and known biases.

Course Agenda

Day 1: From Raw Data to a Clean Table

What machine learning is, and when a simple rule works better
Notebook setup: Python, pandas and plotting in Jupyter
Exploring the course dataset: distributions, outliers and leakage traps
Handling missing values, categorical encoding and scaling
Train, validation and test splits done correctly, ending with a clean, model-ready table

Day 2: Classic Machine Learning

Linear and logistic regression explained with one feature, then many
Decision trees, random forests and gradient boosting, used to build a churn model that beats the baseline
K-means clustering to segment customers
Cross-validation and grid search for hyperparameters
Reading feature importance without over-interpreting it

Day 3: Neural Networks

Neurons, layers, loss functions and gradient descent in plain terms
Writing a training loop in PyTorch
Overfitting, dropout and early stopping seen on live curves
Convolutional networks for images: filters and pooling
Transfer learning with a pre-trained image model to classify product photos as defective or acceptable

Day 4: Working with Language

From words to numbers: tokenisation, bag-of-words and embeddings
How transformers and attention work, at a whiteboard level
Fine-tuning a pre-trained model to tag customer complaints by topic and urgency
Named entity extraction and sentiment scoring
Prompting a large language model versus fine-tuning a small one

Day 5: Responsible Use and Putting a Model to Work

Checking a model for unequal error rates across customer groups
Personal data in training sets: minimisation and anonymisation
Writing a model card and a data sheet
Wrapping a model in a simple API and a demo interface
Planning an internal pilot: success measure, users and fallback, with a closing capstone review

Practical Exercises

The following suggested activities carry the course dataset from raw table to documented model.

Suggested activity: build a churn prediction notebook and compare the model against a simple baseline.
Suggested activity: train a defect image classifier using transfer learning.
Suggested activity: fine-tune a pre-trained transformer as a complaint topic tagger.
Suggested activity: complete a model card for your own final model.

FAQs

How much programming do I need?

Comfort with basic scripting or spreadsheet formulas is enough. Notebooks are provided pre-written; participants edit and run cells rather than write code from scratch.

How many hours does the course run?

Four to five hours a day across five days, around 20 to 25 hours in total, most of it hands-on lab time.

Do I need a powerful laptop?

No. Labs run in a hosted notebook environment, including the neural network and transformer exercises.

Conclusion

Participants leave having built three working models themselves, a churn predictor, an image classifier and a complaint tagger, with the vocabulary to discuss machine learning, NLP and AI governance confidently and a documented model card they can use as a template for the next AI project back at work.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 41-60 of 76 events

Location	Dates	Duration	Mode	Price
Tokyo	Week 17, 2027 26 – 30 April 2027	5 Days	Onsite	€10,000
Accra	Week 17, 2027 2 – 6 May 2027	5 Days	Onsite	€4,100
Milan	Week 18, 2027 3 – 7 May 2027	5 Days	Onsite	€5,700
Manama	Week 18, 2027 9 – 13 May 2027	5 Days	Onsite	€4,700
Seoul	Week 19, 2027 10 – 14 May 2027	5 Days	Onsite	€10,000
Riyadh	Week 19, 2027 16 – 20 May 2027	5 Days	Onsite	€5,700
Abu Dhabi	Week 20, 2027 17 – 21 May 2027	5 Days	Onsite	€4,700
Vienna	Week 21, 2027 24 – 28 May 2027	5 Days	Onsite	€5,700
Abu Dhabi	Week 21, 2027 24 – 28 May 2027	5 Days	Onsite	€4,700
Cairo	Week 22, 2027 31 May – 4 June 2027	5 Days	Onsite	€4,100
Porto	Week 22, 2027 31 May – 4 June 2027	5 Days	Onsite	€5,700
Dubai	Week 23, 2027 7 – 11 June 2027	5 Days	Onsite	€4,500
Rome	Week 23, 2027 7 – 11 June 2027	5 Days	Onsite	€5,700
Dubai	Week 24, 2027 14 – 18 June 2027	5 Days	Onsite	€4,500
Phuket	Week 24, 2027 20 – 24 June 2027	5 Days	Onsite	€6,000
Toronto	Week 25, 2027 27 June – 1 July 2027	5 Days	Onsite	€12,000
Sharm El-Sheikh	Week 27, 2027 5 – 9 July 2027	5 Days	Onsite	€4,100
Prague	Week 27, 2027 5 – 9 July 2027	5 Days	Onsite	€6,000
Kuala Lumpur	Week 28, 2027 12 – 16 July 2027	5 Days	Onsite	€5,200
Baku	Week 28, 2027 12 – 16 July 2027	5 Days	Onsite	€5,000

Frequently asked questions

What does this course cover?

OverviewThis five-day AI professional programme in machine learning, NLP and AI governance is a practical tour of how AI models are actually built, from a spreadsheet of raw data to a working text classifier. Participants work in guided Python notebooks using scikit-learn, PyTorch and Hugging Face libraries, and carry one realistic customer-feedback datas…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

AI, Machine Learning, NLP and AI Governance Course