Python ML & AI Bootcamp: 1 Day Practical Workshop in Wellington
Description
About This Course
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
If you would like weekend training sessions, kindly reach out to us at info@mangates.com for availability and scheduling.
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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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 session: corporate@mangates.com
Gallery
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 • Use cases for dimensionality reduction (PCA) • Case Study
Module 4: Model Training and Evaluation
• Splitting datasets: train-test-validation • Accuracy, precision, recall, F1-score, confusion matrix • Cross-validation and tuning • Activity
Module 5: Feature Engineering Essentials
• Handling missing data and outliers • Feature scaling and encoding • Feature selection techniques • Activity
Module 6: Introduction to Neural Networks
• Understanding neurons and layers • Basics of perceptrons and activation functions • Overview of backpropagation • Activity
Module 7: Deep Learning Concepts Overview
• Understanding deep networks • Brief intro to TensorFlow and Keras • Practical examples in image and text processing • Case Study
Module 8: Mini Project
• Build a simple predictive model end-to-end • Train, test, evaluate, and optimize • Present insights and findings • Activity
FAQs
- 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.
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