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Machine Learning In Python Environment

This free online course describes setting up a Python machine learning environment and how to declare Python variables.

Publisher: YouAccel Training
Machine Learning in the Python Environment is a free online course that introduces you to the fundamental methods at the core of modern machine learning. This Python machine learning tutorial covers how to install Python environments, declare Python variables, the theoretical foundations of supervised and unsupervised learning, and the important processes involved in building a classification model using scikit-learn.
Machine Learning In Python Environment
  • Duration

    3-4 Hours
  • Students

  • Accreditation






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Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. Setting up your Python environment for ML can be a tricky task. If you have never set up something like that before, you might spend hours fiddling with different commands trying to get it to work. This introduction to machine learning with Python will cover just about all you need to know about getting started including the fundamentals of modern machine learning, examples and uses of machine learning, and the machine learning process. You then move on to some practicalities of installing Jupyter, which is a free, open-source, interactive web tool. It is known as a computational notebook, which you can use to combine software code, computational output, and multimedia resources in a single document. This machine learning Python course gives you a step-by-step guide on how to install and use the Jupyter notebook.

Machine learning is key in developing intelligent systems and analyzing data in science and engineering. A Python variable is a reserved memory location to store values. In other words, a variable in a Python program can be used to give data to the computer for processing. This course explains how to declare the Python variables and how to work on them, before moving on to Python functions, conditionals, and loops. Tuples are the next topic covered and your learning includes how to create and get a list of them, and some examples as well. This section of the course wraps up by learning about modules, which refers to a file containing Python statements and definitions which is used to break down large programs into small manageable, and organized files. You will also be taught how to import modules into Python.

The last part of the course covers scikit-learn, which is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was founded in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. In this Python machine learning beginner tutorial, you will learn how to build a classification model using scikit-learn, along with its uses, and installation process. The Iris Dataset is widely used as a beginner's dataset for machine learning purposes. The dataset is included in R:BASE (or RBASE) and Python in scikit-learn, so that users can access it without having to find a source for it. This machine learning course finishes by delving into importing the Iris Dataset into a Python file using Scikit-learn, how to prepare, organize, and load the data, before teaching you how to create your own KNeighborsClassifier. You should enrol in this course if you are in website or app development or even hoping to explore a career as a data analyst and want to combine your passion for machine learning and Python programming.

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