What is the refund policy?

You are eligible to request a refund up to 7 days before the event

Do I need Python experience to attend?

Yes, a solid understanding of Python programming is required.

Will this course cover deep learning in detail?

No, only introductory concepts will be covered, but it builds a foundation for further study.

Do we get hands-on experience?

Yes, the course includes practical code exercises and real datasets.

Does the course include data preprocessing techniques?

Yes, essential feature engineering and data cleaning steps are included.

Can I use this knowledge for real projects?

Yes, you will learn practical workflows and tools to apply directly in projects.

Which machine learning tools will we use?

Primarily scikit-learn, along with introductions to TensorFlow/Keras.

Is this course beginner-friendly in ML?

It’s ideal for those with Python knowledge but new to ML and AI workflows.

Is there a certification?

Yes, a Course Completion Certificate is provided.

Will we learn about model optimization?

Yes, tuning models and evaluating performance is part of the agenda.

Can this be customized for corporate teams?

Absolutely, we offer fully tailored content for team requirements.

Python for Machine Learning & AI – 1 Day Workshop in Toronto

By Catils_inc

Overview

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

💡 Group Discount Alert – Learn More, Save More Together!

🎟️ Check tickets now for exciting group discounts!

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.

📧 Contact us today to schedule a customized in-house, face-to-face session: info@catils.com

Category: Science & Tech, High Tech

Good to know

Highlights

8 hours
ages 18+
In person
Paid parking

Refund Policy

Refunds up to 7 days before event

Location

Regus ON, Toronto - Yonge & Shuter

229 Yonge Street Suite 400

Ph No +1 469 666 9332 Toronto, ON M5B 1N9 Canada

How do you want to get there?

Driving Public transport Biking Walking

Agenda

Module 1: Introduction to Machine Learning & AI

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

Module 2: Supervised Learning

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

Module 3: Unsupervised Learning • Clustering basics • K-means and hierarchical

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

Frequently asked questions

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Catils_inc

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