- An understanding of basic programming concepts
- Experience with Python programming
- Familiarity with basic statistical concepts
Audience
- Data scientists
- Software developers
Google Colab is a cloud-based platform that provides a collaborative environment for machine learning development, offering free access to computing resources and an easy-to-use interface.
This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.
By the end of this training, participants will be able to:
- Set up and navigate Google Colab for machine learning projects.
- Understand and apply various machine learning algorithms.
- Use libraries like Scikit-learn to analyze and predict data.
- Implement supervised and unsupervised learning models.
- Optimize and evaluate machine learning models effectively.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Introduction to Machine Learning and Google Colab
- Overview of machine learning
- Setting up Google Colab
- Python refresher
Supervised Learning with Scikit-learn
- Regression models
- Classification models
- Model evaluation and optimization
Unsupervised Learning Techniques
- Clustering algorithms
- Dimensionality reduction
- Association rule learning
Advanced Machine Learning Concepts
- Neural networks and deep learning
- Support vector machines
- Ensemble methods
Special Topics in Machine Learning
- Feature engineering
- Hyperparameter tuning
- Model interpretability
Machine Learning Project Workflow
- Data preprocessing
- Model selection
- Model deployment
Capstone Project
- Defining the problem statement
- Data collection and cleaning
- Model training and evaluation
Summary and Next Steps
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