Course Code: mlcolab
Duration: 14 hours
Prerequisites:
  • An understanding of basic programming concepts
  • Experience with Python programming
  • Familiarity with basic statistical concepts

Audience

  • Data scientists
  • Software developers
Overview:

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.
Course Outline:

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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