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 (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
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 be able to:
- 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
Who can Attend?
- Data scientists
- ML engineers
- Software developers
- Advanced Python users.
Why Choose this Course?
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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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, face-to-face session:
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 120 Collins Street, MELBOURNE, 180 Lonsdale Street Level 19, 180 Lonsdale Street, Australia
AUD 600.62 to AUD 773.21
