Python ML & AI Bootcamp: 1 Day Practical Workshop in Bristol

Dive into ML and AI using Python—master supervised and unsupervised learning, model evaluation, and neural network basics in one day.
About this Event

Bring your team and save

1. Groups of three or more receive a 10% discount

2. Organizations hosting an in-house session with 10+ participants enjoy a 15% discount.

Duration: 1 Full Day (9:00 AM – 5:00 PM)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, beverages, and light snacks included

Course Overview:

The Machine Learning & AI in Python course empowers you to understand, build, and evaluate predictive models using Python. You will learn the fundamentals of supervised and unsupervised learning, model evaluation metrics, feature engineering, and get a glimpse into neural networks and deep learning. With practical hands-on exercises, this course prepares you to transition from theory to real-world machine learning applications.

Learning Objectives:

By the end of this course, you will:

Understand core machine learning concepts and workflows
Build supervised and unsupervised models using scikit-learn
Evaluate model performance using appropriate metrics
Apply feature engineering techniques to improve predictions
Gain basic knowledge of neural networks and deep learning
Use Python for real-world AI and ML problem-solving

Target Audience:

Data scientists, ML engineers, developers, and advanced Python users.

Why is it the Right Fit for You?

If you’re looking to take your Python programming skills into the realm of machine learning, this course is ideal. With a strong focus on applied learning and best practices, you’ll build models and analyze datasets that mirror real-world challenges. Our experienced instructors make complex concepts like algorithms and neural networks accessible through hands-on examples. This course helps you build confidence in working with machine learning tools and prepares you for advanced AI workflows.

Looking to enhance your team's AI and machine learning capabilities?

We offer tailored in-house training sessions designed to fit your organization’s specific learning needs. Want your developers to build predictive models or enrich your team’s data expertise? Let us customize a course that aligns with your business goals and projects.

📧 Contact us today to schedule a customized in-house session: [email protected]

Agenda
Module 1: Introduction to Machine Learning & AI

Info: • What is machine learning and AI?
• Role of Python in ML and AI
• Overview of ML workflow
• Activity

Module 2: Supervised Learning

Info: • Regression vs classification
• Building basic linear and logistic models
• Using scikit-learn for model implementation
• Activity

Module 3: Unsupervised Learning

Info: • Clustering basics
• K-means and hierarchical clustering
• Use cases for dimensionality reduction (PCA)
• Case Study

Module 4: Model Training and Evaluation

Info: • Splitting datasets: train-test-validation
• Accuracy, precision, recall, F1-score, confusion matrix
• Cross-validation and tuning
• Activity

Module 5: Feature Engineering Essentials

Info: • Handling missing data and outliers
• Feature scaling and encoding
• Feature selection techniques
• Activity

Module 6: Introduction to Neural Networks

Info: • Understanding neurons and layers
• Basics of perceptrons and activation functions
• Overview of backpropagation
• Activity

Module 7: Deep Learning Concepts Overview

Info: • Understanding deep networks
• Brief intro to TensorFlow and Keras
• Practical examples in image and text processing
• Case Study

Module 8: Mini Project

Info: • Build a simple predictive model end-to-end
• Train, test, evaluate, and optimize
• Present insights and findings
• Activity