Machine Learning: Random Forest with Python from Scratch ©

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A step-by-step guide that walks you through the fundamentals of Python programming followed using Python libraries to create random forest from scratch. A comprehensive course designed for both beginners with some programming experience or even those who know nothing about ML and random forest!

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70 on-demand videos & exercises
Level: Beginner 
8hrs 20mins
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What to know about this course

Machine learning is designed to understand and build methods that 'learn' to leverage data to improve performance on a set of tasks. Machine learning algorithms are used in a plethora of applications in medicine, email filtering, speech recognition, and more, where it is challenging to develop conventional algorithms to perform tasks. The course begins with an introduction to machine learning concepts and explains the motivation for machine learning. The course teaches all major concepts about Python including variables, objects, strings, loops, decision-making statements, classes, and a small project to recap. You will learn to use the power of Python to train your machine and make predictions and implement the ML algorithm “Random Forest.” Use NumPy with Python for array handling, Pandas data frames for Excel files, and matplotlib for data visualization. You will learn to use Random Forest with sklearn, Matplotlib for Python plotting, and SciKit-Learn for Random Forest. Upon completion, you will Implement the structure of forest, impurity, information gain, partitions, leaf nodes, and decision nodes using Python and create a complete structure for Random Forest using Python to build one tree that lets you create an entire forest. You will write an accuracy calculator function and implement Random Forest on any dataset. All resources are available at:

Who's this course for?

This course is for you if you want to learn how to program in Python for machine learning or want to make a predictive analysis model. This course is for someone who is an absolute beginner and has truly little or even zero ideas of machine learning or wants to learn random forest from zero to hero.

What you'll learn

  • Use Random Forest with sklearn and Matplotlib for Python plotting.
  • Use SciKit-Learn for Random Forest using the titanic dataset.
  • Learn forest structure, impurity, partition, leaf/decision nodes.
  • Create a complete Random Forest structure from scratch using Python.
  • Build one tree that adds up to create a complete forest.
  • Write accuracy calculator functions and implement them on any dataset.

Key Features

  • Use the power of Python to train your machine to learn like a human and make predictions!.
  • Learn data preprocessing steps to prepare data for machine learning algorithms.
  • Master machine learning concepts and implement the essential ML algorithm, Random Forest.

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.

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