Python Machine Learning Crash Course for Beginners

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The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You’ll start with the basics of machine learning. You’ll learn how to create, train, and optimize models and use these models in real-world applications.

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57 on-demand videos & exercises
Level: All Levels
English
9hrs 27mins
Access on mobile, web and TV

What to know about this course

Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects. 

You’ll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You’ll learn to optimize this model and apply performance metrics to track its performance. You’ll also learn topics like regression, classification, and clustering to improve the performance of your model. You’ll learn the basics of neural networks and use scikit-learn to perform calculations in your project. 

By the end of this course, you’ll have created a face recognition application using everything you’ve learned in this course.  The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners

Who's this course for?

This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.

What you'll learn

  • Train different types of machine learning models for your project.
  • Prepare and clean data for your project.
  • Optimize your machine learning model to best suit your project needs.
  • Build your own machine learning model from scratch.
  • Apply performance metrics to track the performance of your model.
  • Use scikit-learn to perform calculations in your project.

Key Features

  • Build a face recognition application from scratch.
  • Prepare and train your data for your projects.
  • Learn real-world applications of your algorithms.

Course Curriculum

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science. AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.