About this Event
Group Discounts:
- Save 10% when registering 3 or more participants
- Save 15% when registering 10 or more participants
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.
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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 • Clustering basics • K-means and hierarchical
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
Event Venue & Nearby Stays
Regus - Montreal, QC, 1200 Avenue McGill College, Montreal, Canada
CAD 616.10 to CAD 797.09
