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Artificial Intelligence Foundations: Machine Learning

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Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If you’re looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.


01 - Introduction
  • 01 - Introduction to AI foundations Machine learning course
  • 02 - Reviewing the course scenarios

  • 02 - 1. Understanding Machine Learning
  • 01 - Exploring machine learning
  • 02 - Examining how machines learn

  • 03 - 2. Implementing a Machine Learning Solution
  • 01 - Breaking down the machine learning lifecycle
  • 02 - Framing machine learning problems
  • 03 - Identifying a pre-built model
  • 04 - Understanding tools used to train a model

  • 04 - 3. Preparing Data for Machine Learning
  • 01 - Obtaining data
  • 02 - Visualizing and understanding data
  • 03 - Understanding feature engineering
  • 04 - Demo Performing feature engineering

  • 05 - 4. Training a Machine Learning Model
  • 01 - Understanding learning algorithms and model training
  • 02 - Exploring learning algorithms for classification
  • 03 - Reviewing learning algorithms for regression
  • 04 - Examining additional learning algorithms
  • 05 - Training a custom machine learning model
  • 06 - Demo Training a custom machine learning model

  • 06 - 5. Evaluating Model Performance
  • 01 - Exploring common classification metrics
  • 02 - Understanding the confusion matrix
  • 03 - Exploring common regression metrics
  • 04 - Determining feature importance
  • 05 - Combating bias

  • 07 - 6. Operationalizing a Machine Learning Pipeline
  • 01 - Structuring a machine learning pipeline
  • 02 - Demo Designing and building a pipeline

  • 08 - Conclusion
  • 01 - Your machine learning journey
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    مدت زمان: 111 دقیقه
    تاریخ انتشار: 15 تیر 1402
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