Python ML & AI Skills – 1 Day Intensive Workshop in Toronto

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Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session

The Machine Learning & AI in Python course equips you with the skills to understand, build, and evaluate predictive models using Python. You will explore key concepts in supervised and unsupervised learning, model evaluation techniques, and feature engineering, along with an introduction to neural networks and deep learning. Through practical, hands-on exercises, the course bridges the gap between theory and real-world machine learning applications, enabling you to apply Python-based ML techniques with confidence.

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

• Understand core machine learning concepts and end-to-end workflows
• Build supervised and unsupervised models using Python and scikit-learn
• Evaluate model performance using appropriate metrics
• Apply feature engineering techniques to improve model accuracy
• Gain foundational knowledge of neural networks and deep learning
• Use Python to solve real-world AI and machine learning problems

This course is designed for data scientists, machine learning engineers, developers, and advanced Python users.

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If you want to extend your Python skills into machine learning and AI, this course provides the ideal foundation. With a strong emphasis on applied learning and industry best practices, you will work with datasets and scenarios that reflect real-world challenges. Expert instructors simplify complex topics such as algorithms and neural networks through hands-on demonstrations. By the end of the course, you will gain confidence in using machine learning tools and be prepared to progress toward advanced AI workflows.

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Info:

• What is machine learning and AI?

• Role of Python in ML and AI

• Overview of ML workflow

• Activity

Info:

• Regression vs classification

• Building basic linear and logistic models

• Using scikit-learn for model implementation

• Activity

Info:

• Clustering basics

• K-means and hierarchical clustering

• Use cases for dimensionality reduction (PCA)

• Case Study

Info:

• Splitting datasets: train-test-validation

• Accuracy, precision, recall, F1-score, confusion matrix

• Cross-validation and tuning

• Activity

Info:

• Handling missing data and outliers

• Feature scaling and encoding

• Feature selection techniques

• Activity

Info:

• Understanding neurons and layers

• Basics of perceptrons and activation functions

• Overview of backpropagation

• Activity

Info:

• Understanding deep networks

• Brief intro to TensorFlow and Keras

• Practical examples in image and text processing

• Case Study

Info:

• Build a simple predictive model end-to-end

• Train, test, evaluate, and optimize

• Present insights and findings

• Activity

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