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Introduction to Python programming for artificial intelligence
Your AI ideas need to translate into processing steps and code. Structure your programming practice to manipulate data and implement algorithms. Develop an approach that connects technical logic, experimentation and verification of results.
- Duration
- 2 days 14 hours
- Code
- IA033FR Code
Presentation
Python has become the leading language for data science and artificial intelligence. For professionals seeking to adopt these technologies, the technical barrier can seem insurmountable. This 2-day course demystifies programming and helps you become independent in using Python and its data-focused ecosystem.
The programme takes a pragmatic approach: learn to use essential libraries such as Pandas and NumPy to process data, and Matplotlib to visualise it. You will then build your first machine learning models with Scikit-learn, while understanding the theoretical fundamentals behind them.
Through progressive workshops, you will move from writing simple scripts to developing predictive models on real datasets. You will leave with the skills to prototype solutions, automate analytical tasks and communicate effectively with technical experts on your AI projects.
Objectives
By the end of this course, you will be able to:
- understand Python syntax and fundamental structures;
- manipulate and clean complex datasets with Pandas and NumPy;
- visualise data to extract relevant insights using Matplotlib and Seaborn;
- build and evaluate supervised machine learning models with Scikit-learn;
- apply these technical skills to practical artificial intelligence cases.
Program
Module 1: Learning Python and data manipulation basics
- Installing the development environment, including Jupyter and VS Code, and learning syntax basics.
- Writing functions and using modules to structure code.
- Manipulating arrays and vector operations with NumPy.
Hands-on exercises
- Write a simple Python script to automate a routine task.
Module 2: Visualising and analysing data
- Using Pandas DataFrames to filter, join and prepare data.
- Creating static and interactive charts with Matplotlib and Seaborn.
- Interpreting visualisations to support decisions.
Hands-on exercises
- Clean and analyse a raw CSV dataset with Pandas.
- Create advanced charts to explore a dataset visually.
Module 3: Introduction to machine learning
- Understanding supervised and unsupervised learning.
- Structuring an ML pipeline: collection, training and evaluation.
- Analysing performance metrics to validate a model's suitability.
Hands-on exercises
- Identify the problem type, such as classification, regression or clustering, in business cases.
Module 4: Modelling and further development
- Training regression and decision tree models with Scikit-learn.
- Evaluating performance and adjusting parameters.
- Introduction to deep learning through TensorFlow and PyTorch.
Hands-on exercises
- Build a complete predictive model using real data.
- Explore an AI notebook incorporating a simple model with instructor guidance.
Audience
This course is intended for those seeking technical independence, including:
- product owners and AI project managers seeking to understand code produced by their teams;
- data analysts, engineers and consultants seeking stronger Python skills;
- programming beginners seeking a practical introduction to AI;
- anyone involved in managing or delivering data projects.
Prerequisites
The following prerequisites apply:
- Basic knowledge: general computing knowledge and confidence with digital tools are sufficient.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- Fully accessible: learn programming basics without technical prerequisites through beginner-friendly teaching.
- Standard tools: use libraries such as Pandas and Scikit-learn that are global industry standards.
- Intensive practice: consolidate your knowledge through 6 practical workshops covering the entire data lifecycle.
- A foundation for AI: acquire the solid grounding needed to progress to more complex machine learning projects.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
Any brand names and logos mentioned in this course description, such as Python, Pandas and Scikit-learn, belong to their respective owners. Their mention for educational purposes does not constitute an endorsement or partnership.