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 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
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 ON, Mississauga - Robert Speck 2 (HQ), 2 Robert Speck Parkway, Suite 750, Mississauga, Canada
CAD 605.73 to CAD 776.51
