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Introduction to Machine Learning

Machine learning is a subfield of artificial intelligence (AI). Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range.

Self-Paced

Modality

Course

Learning Style

Beginner

Difficulty

10 Hours

Duration

Course Info

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Machine learning is a subfield of artificial intelligence (AI). Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range.

Course Information

About this Course:

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptron’s, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with Python, open-source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, Coca Cola, eBay, Snapchat, Uber and many more).

Course Objectives:

  • Understand the formulation of well-specified machine learning problems.
  • Learn how to perform supervised and reinforcement learning, with images and temporal sequences.
  • Overview: AI and Machine Learning
  • Linear Regression
  • Gradient Descent
  • Genetic Algorithms
  • Advanced Genetic Algorithms

Format of this Course:

This course includes lectures, exercises, and labs.

Recommended Prerequisites:

Computer programming (python); Calculus; Linear Algebra

 

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Subjects IT Ops & Management
Lab Access No
Modality Self-Paced Learning
Learning Type Course
Difficulty Beginner
Duration 10 Hours
Language English

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